method for detecting at least one analyte in at least one sample of a body fluid, device and test sy

专利摘要:
METHOD FOR DETECTION, DEVICE AND TEST SYSTEM. The present invention relates to a method for detecting at least one analyte in at least one sample of a body fluid. At present, at least one test element (124) is used, at least one test element (124) which has at least one test field (162) with at least one test chemical ( 154) is used, in which the test chemical (154) is adapted to perform at least one optically detectable detection reaction in the presence of the analyte. The method comprises the acquisition of a sequence of images of the images of the test field (162), through the use of at least one image detector (178). Each image comprises a plurality of pixels. The method further comprises detecting at least one characteristic feature of the test field (162) in the images in the image sequence. The method further comprises correcting a change in the relative position between the image detector (178) and the test field (162) in the image sequence using the characteristic aspect, thereby obtaining a corrected image sequence.
公开号:BR112014028616B1
申请号:R112014028616-7
申请日:2013-06-17
公开日:2020-12-29
发明作者:Kai Dickopf
申请人:F. Hoffmann-La Roche Ag；
IPC主号:

专利说明:

FIELD OF THE INVENTION
[001] The present invention relates to a method, an apparatus and a test system for the detection of at least one analyte in a sample of a body fluid. The present invention further relates to a computer program with programming means for carrying out the method according to the present invention, as well as a computer system and a storage medium. The methods, devices, test systems, computer programs and computer systems, according to the present invention, can be used in medical diagnostics, for the detection, qualitatively or quantitatively, one or more analytes in one or more fluids bodily effects. Other fields of application of the present invention are possible. BACKGROUND OF THE INVENTION
[002] In the field of medical diagnostics, in many cases, one or more analytes need to be detected in the samples of a fluid in the body, such as blood, interstitial fluid, urine, saliva or other types of body fluids. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol or other types of analytes normally present in these body fluids. Depending on the concentration and / or the presence of the analyte, an appropriate treatment can be selected, if necessary.
[003] In general, devices and methods known to the person skilled in the art use test elements that comprise one or more test chemicals that, in the presence of the analyte to be detected, are capable of performing one or more detectable detection reactions , such as optically detectable detection reactions. Regarding these test chemicals, reference can be made, for example, to J. Hoenes et al.,: The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, S- 10 and S-26. Other types of chemical tests are possible and can be used to carry out the present invention.
[004] Normally, one or more optically detectable changes in the test chemical are monitored to obtain the concentration of at least one analyte to be detected from these changes. Examples of test fields, test chemicals and methods for monitoring one or more optically detectable changes in test fields are described in EP patent 0.821.234 A2. Therefore, as an example, the relative remission of the test field can be optically detected as a function of time, up to a defined end point for the chemical detection reaction. From the change in relative remission, the concentration of the analyte can be derived. Similar measurements for detecting the amount of light reflected from the test field as a function of time, up to a defined end point of the detection reaction, are described in EP patent 0.974.303 A1.
[005] For the detection of at least a change in the optical properties of the test field, several types of detectors are known in the state of the art. Therefore, several types of light sources to illuminate the test fields, as well as several types of detectors are known. In addition to individual detectors, such as photodiodes, several types of devices through the use of the array detector that has a plurality of photosensitive devices are known. Accordingly, a provision for measuring the concentration of an analyte contained in a sample of a body fluid is described in US patent 2011 / 0.201.909 A1. The arrangement, inter alia, comprises a light source and an array detector. Similarly, EP 1,359,409 A2 describes an apparatus for determining the concentration of an analyte in a physiological sample. The apparatus includes at least one light source and an array detector.
[006] In addition, when using the array detector, methods for detecting errors and artifacts in the images acquired through the array detector are known in the state of the art. Accordingly, US patent 2011 / 0.201.909 describes a correction algorithm that, inter alia, is capable of correcting imperfections present in the reaction site observed by the array detector. Similarly, EP 1,359,409 A2 describes the means for determining whether a sufficient amount of sample is present in each of a plurality of different detector areas in which only the light detected from those areas determined to have sufficient quantity sample, is used to determine the concentration of the analyte.
[007] To further enhance the evaluation of the images acquired through the array detector performing the imaging of a test field, statistical methods in the state of the art were used. Accordingly, EP patent 1,843,148 A1 describes a system for determining the concentration of an analyte in a liquid sample. At this time, the frequencies of occurrence of the gray values stored in the array detector's pixels are listed in a histogram, allowing the separation of the wet areas through the sample and the non-wet areas. The analyte concentration can be derived by evaluating these frequency distributions.
[008] EP patent 2,270,421 A1 describes a method of analyzing the liquid sample for the analysis of an analyte in a liquid sample using a part of the test in which the overflow block lines are formed to prevent the liquid sample can flow to the outside from a passing region of an extended layer. In a state where the liquid sample is not extended in the passage region, the test portion is measured, so as to pass through the passage region of the extended layer and the overflow block lines. Therefore, in a state where the difference in brightness is greater between the crossing region of the extended region and the overflow blocking lines, it is possible to properly recognize the border portions between the crossing region of the extended region and the lines of overflow. overflow block.
[009] US patent 6,471,355 B1 describes an image alignment system for rear projection in which a part of the normally altered pixel pattern contains a pixel reference mark, which appears in each of the pixel images side by side projected on a screen. A camera that has a field of view wide enough to encompass the pixel reference mark of each of the images on the screen captures the location of the pixel reference marks to allow a computer to determine the coordinates of each of the reference marks pixel and generates a deviation signal representative of the visual misalignment of the images side by side. A drive element controllable by the deviation signals from the computer reposition one of the images side by side in relation to the other to therefore align the images to produce a single visually perfect image. The camera and the computer can continuously monitor the two pixel reference marks to continuously generate a deviation signal so that the side-by-side images can be automatically placed into a single, visually perfect image.
[010] In addition, systems and methods are known to automatically detect a region of interest for evaluation after transferring a sample over the test fields. Therefore, in publication WO 2012/010454 A1 a device for detecting at least one analyte in a body fluid is described, the device comprises at least one test element that has at least an area of two-dimensional analysis. The device further comprises at least one optical spatial detector that has a plurality of pixels. The detector is configured to reproduce at least part of the test element in an image area. The detector is adapted to the test element in such a way that a determined minimum number of pixels is provided for each dimension within the analysis image area. In addition, a method for automatically detecting a region of interest to be evaluated to determine the concentration of the analyte is described.
[011] Despite the progress achieved by the known methods and devices mentioned above, some of the main challenges remain regarding the accuracy of analyte detection. Therefore, there is a constant effort to further reduce the sample volume to be applied to the test fields. To reduce the discomfort associated with sample generation, such as through the patient's finger or earlobe, sample volumes from modern devices have typically been reduced to volumes below 2 μl, in some cases even below 1 μL or even lower. Integrated test systems, including so-called microclassifiers, have been developed, such as that described in publication WO 2010/094426 A1, which comprises a plurality of lancets, each has a lancet end and at least one capillary to receive the fluid during the process of perforating or removing the microclassifier from the patient's skin. Small sample volumes are transferred to the test fields into a well where the microclassifier is retracted. The small sample volumes and the constant need to reduce the size of the test fields, however, increase the demands on the spatial resolution of the array detector and in relation to its ability to eliminate artifacts and impurities from images to be evaluated.
[012] Another technical challenge is the precise normalization of measurement data acquired through optical detectors or the array detector. In many cases, as in the example mentioned above, the devices and methods described by patent EP 0.821.234 A2, a relative remission of the test fields is detected, which requires the determination of at least one value called white or dry, that is, a reflectance value of the test chemical before the detection reaction starts. Specifically in the case where the precise location of the sample application to the test field is unknown, determining a blank value, however, is quite difficult, since the blank value itself may be dependent on the precise location on the test field. Therefore, in most cases, white values at locations other than the location of the sample application in the test field will need to be used, which leads to high uncertainty of the blank value or, a large number of images from the test field , before and after the sample application is stored and evaluated, leading to a high need for data storage and calculation resources. The latter, however, is specifically disadvantageous for manual testing devices that normally offer very limited hardware resources.
[013] In addition, specifically in relation to the determination of the blank value, mechanical tolerances and optical image acquisition tolerances need to be considered, specifically in sophisticated systems that have transfer mechanisms to transfer the sample to the test fields. Therefore, in systems using microclassifiers, as in publication WO 2010/094426 A1, the transfer of the sample from the microclassifiers to the test fields occurs by pressing the capillaries of the microclassifiers to the test fields. This method of sample transfer or other types of sample transfer can lead to a highly structured sample application, which requires a high optical resolution of image acquisition and image evaluation. This type of sample transfer, however, usually implies dynamic processes that involve moving parts, which can lead to a displacement of the test fields or their parts. Therefore, pressing microclassifiers to the test fields can lead to distortion and / or displacement of the test fields. In addition, the test fields are often accommodated in a box of test elements in a rather loose shape, such as by simply inserting the ring test chemical into the box without actually assembling the fields test in a shockproof way. Consequently, during the use and handling of the test devices, specifically during the measurements, the test fields can move around, therefore creating inaccuracies in relation to the acquisition of the blank values and in relation to the determination of the actual area of the test fields. tests to be evaluated for the determination of the analyte. BRIEF DESCRIPTION OF THE INVENTION
[014] It is therefore an object of the present invention to provide the methods and devices that, at least in part, overcome the deficiencies of the known methods and devices mentioned above. Specifically, the methods and devices for detecting at least one analyte in at least one sample of a body fluid must be described, which are capable of evaluating even very small sample volumes at high accuracy , through largely avoiding the artifacts and inaccuracies generated through mechanical disturbances and through the application of the sample in a structured way. DETAILED DESCRIPTION OF THE INVENTION
[015] This problem is solved by a method and a device for the detection of at least one analyte in at least one sample of a body fluid that has the characteristics of the additional independent claims. The problem is still solved by a computer program, a computer system, a storage medium and a test system that has the characteristics of the other independent claims.
[016] As used herein, the terms "owns", "understands" and "contains", as well as their grammatical variations are used in a non-exclusive way. Therefore, the term “A has B”, as well as the term “A comprises B” or “A contains B” both can refer to the fact that, in addition to B, A contains one or more additional components and / or constituents , and in the case where, besides B, there are no other components, constituents or elements present in A.
[017] In a first aspect of the present invention, a method for detecting at least one analyte in at least one sample of a body fluid is described. As described above, at least one analyte may preferably comprise one or more substances that are normally contained in a human body or an animal body, such as one or more metabolites. Preferably, at least one analyte can be selected from the group consisting of glucose, cholesterol, triglycerides and lactate. Other types of arbitrary combinations of analytes and / or analytes are possible. Preferably, the method is adapted for the detection of the analyte with a high specificity. At least one body fluid, in general, can comprise an arbitrary type of body fluid, such as blood, interstitial fluid, saliva, urine or any other type of body fluid or combinations of the named body fluids. In the following, without restriction of the other embodiments, the present invention will be specifically explained in the context of a method for detecting glucose in blood and / or interstitial fluid.
[018] The method uses at least one test element, the test element comprises at least one test field. At least one test field has at least one test chemical. The test chemical is adapted to perform at least one optically detectable detection reaction in the presence of the analyte, preferably a color change reaction. In the context of the present invention, the term "test field" refers to a continuous or discontinuous amount of the test chemical that is preferably maintained through at least one vehicle, such as through at least a vehicle film. Accordingly, the test chemical may form or may be included in one or more films or layers of the test field, and / or the test field may comprise a layer configuration having one or more layers, in which, at least At least one of the layers comprises the test chemical. Therefore, the test field may comprise a layer configuration arranged on a vehicle, where the body fluid sample can be applied to the layer configuration from at least one side of the application, such as from from one end of the test field and / or from an application surface of the test field. The test field can be part of a test element that comprises at least one test field and at least one vehicle to which the test field is applied.
[019] As used herein, the term “test chemical”, in general, refers to a substance or mixture of substances, which is adapted to perform at least one optically detectable detection reaction in the presence of the analyte. Therefore, the detection reaction, preferably, may result in a color change of the test chemical or at least a part of it. In relation to the test chemical, several possibilities for detecting the test chemical are known in the state of the art. In this regard, reference can be made to the prior art documents mentioned above. Specifically, reference can be made to J. Hoenes et al .: The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics Volume 10, Supplement 1, 2008, S-10 and S-26. However, other types of test chemicals are possible. Preferably, the test chemical comprises at least one enzyme which, preferably, directly or indirectly reacts with the analyte, preferably with a high specificity, in which, in addition, one or more optical indicator substances are present in the test chemical, which perform at least one optically detectable change in properties when at least one enzyme reacts with the analyte. Therefore, at least one indicator can comprise one or more dyes that perform a color change reaction indicative of the enzymatic reaction of at least one enzyme and the analyte. Therefore, at least one enzyme can comprise glucose oxidase and / or glucose dehydrogenase. However, other types of enzymes and / or other types of test chemical or active components of the test chemical may be used.
[020] The method also comprises the acquisition of a sequence of images from the images of the test field. This image acquisition may comprise the acquisition of images from the complete test field and / or from a specific part of the test field. Therefore, at least one viewing window can be defined, for example, through a mask and / or a test element box, which provides the limits of a visible part of the test field which will then be simply referred to as a test field viewport. This type of viewing window, for example, is known from the publication WO 2010/094426 A1 mentioned above.
[021] As used in the present, the term “sequence of images” refers to a plurality of images acquired at later points in time. Preferably, the acquisition of images occurs at equidistant points in time, such as through the use of a constant rhythm of images. Therefore, frame rates of 20 frames per second, 25 frames per second, 37 frames per second, or other types of frame rates can be used. In addition, as used in the present, the term “image” refers to a one-dimensional or two-dimensional array of information values, where each position of the array indicates a specific pixel of the image detector and where the value of the information stored in that position of the matrix indicates optical information acquired per pixel from the image detector, such as a gray value. As described in more detail, an image can comprise the information values of all pixels in the image detector. Alternatively, only a partial image can be used, as only a specific section of the images. Next, the term image can refer to both options, that is, the option to use the full image or the option to use partial images only, such as just a pre-defined section of the images.
[022] Consequently, the term “image detector” (hereinafter also simply referred to as the detector) refers to an arbitrary detection device that has a plurality of optically sensitive sensor elements arranged in a one-dimensional array (line detector) or a two-dimensional matrix (array detector). The detector's image sensors below will also be referred to as the detector pixels. The pixels are preferably arranged in a common plane, which can also be referred to as the detector plane. The pixel array may comprise a straight line of pixels and / or a rectangular set of pixels. However, other types of pixel arrangements are possible, such as circular arrangements and / or hexagonal arrangements. The pixels themselves are optically sensitive sensor elements, such as optically sensitive semiconductor elements, such as CCD or CMOS sensor elements, preferably CMOS sensor elements.
[023] The method, according to the present invention, still comprises the detection of at least one characteristic aspect of the test field in the images of the image sequence. Detection can be performed at least once, which comprises the option to detect repeatedly or the attempt to detect the characteristic aspect. Therefore, the detection of at least one characteristic aspect can also comprise an iterative algorithm, such as an algorithm that has two or more iterations, such as four iterations, preferably the iterations that have the refined parameters.
[024] As used herein, the term "characteristic aspect" refers to an arbitrary aspect or irregularity in the test field that is detectable in the images in the image sequence, preferably in all of these images. Therefore, the characteristic aspect can comprise a characteristic spatial distribution of values in the images, indicating the random structures and / or regular structures. The characteristic aspect preferably indicates a property of the test field itself, such as a property of the test chemical and / or another component of the test field. Therefore, the characteristic aspect can be formed through a visible random structure of the test field, such as through a granularity and / or roughness of the test field. These types of random structures are usually unavoidable when testing fields are manufactured and, within the scope of the present invention, can be used without intentionally introducing these characteristic aspects into the fields of testing. Alternatively or additionally, characteristic features can be intentionally introduced into the test field, such as through the introduction of one or more positioning marks and / or fiducial marks.
[025] The term "detection", as used herein, may refer to an arbitrary algorithm known in the art for one or more patterns, as known in the field of pattern recognition in images. The detection can specifically comprise identifying the characteristic aspect and / or the coordinates of the characteristic aspect in the images in the image sequence. Therefore, the result of the detection of the characteristic aspect can specifically comprise one or more coordinates, such as the coordinates of one or more matrices, indicating the position of the characteristic aspect in the images of the image sequence. In case the detection fails and if the characteristic aspect is not detected in the images in the image sequence, the detection process can return an error or defect value. However, other realizations of a detection algorithm can be used, as the person skilled in the field of standard recognition will immediately recognize.
[026] The detection of at least one of the characteristic aspects can form an explicit or implicit stage of the present method. Therefore, the characteristic aspect can be explicitly indicated in an output of an intermediate step of the process according to the present invention. Alternatively or additionally, the detection of the characteristic aspect may simply comprise selecting at least a specific part of one or more images in the image sequence, indicating the information contained in this part, as the characteristic aspect, in which the other images of the image sequence of images are scanned or searched for this information or similar types of information.
[027] The method also comprises correcting a change in the relative position between the image detector and the test field in the image sequence by using the characteristic aspect, thereby obtaining a corrected image sequence. As used herein, the term "change in relative position" between the image detector and the test field in the image sequence refers to an arbitrary change in at least one of an absolute position, an angular orientation and a shape geometry of the test field as photographed through the image detector. This change in relative position may be due to a change in the position of the image detector and / or a change in the position of the test field.
[028] In addition, as used in the present, the term “correction” refers to an arbitrary algorithm adapted to compensate for the change in the relative position in the image sequence. Therefore, the algorithm can be adapted to transform the information matrix of each image in the image sequence, such as by translating the matrix in at least one direction in space and / or by rotating the information matrix. with respect to at least an angle, and / or by stretching or compressing the matrix by a specified amount. The correction can be individually adapted for each image in the image sequence, according to the characteristic aspect detected in the specific image. Specifically, one of the images in the image sequence can be defined as a reference image, in which the other images in the image sequence are corrected so that the characteristic aspect of all corrected images in the corrected image sequence can be found in the same image. matrix position.
[029] The corrected image sequence below is also referred to as the corrected sequence. By obtaining the corrected sequence according to the present invention, the corrected sequence can be used for the detection of at least one analyte, such as, for the observation - optionally time-dependent - of the alteration of at least an optically detectable property of the test field due to the detection reaction of the test chemical with the analyte to be detected. By correcting the image sequence, a high degree of robustness and reliability can be achieved, unlike conventional techniques, and most of the deficiencies of the known methods and devices mentioned above are overcome.
[030] The basic method, as described above, can be developed in several advantageous ways. Therefore, as described above, each image in the image sequence may contain a one-dimensional or two-dimensional or n-dimensional matrix, in general, of information values, preferably a gray value information, preferably a value of information of 4 bits, 8 bits, 12 bits or 16 bits.
[031] As mentioned above, the correction of the change in the relative position between the image detector and the test field can comprise an arbitrary correction algorithm. Preferably, the correction comprises at least one correction selected from the group consisting of: a correction of a translation of an image of the test field in the image detector in at least one spatial direction; a correction of a rotation of an image of the test field in the image detector on at least one rotational axis; a distortion correction of an image of the test field in the image detector, preferably a distortion due to a deformation of the test field, such as a correction, using at least one extension and / or at least one of compression The corrections mentioned above can be easily implemented through a mathematical correction algorithm transforming the matrix of information values. Suitable matrix transformations are known to the person skilled in the art.
[032] As mentioned above, images in the image sequence are preferably acquired in a constant time sequence, with time intervals equidistant between the acquisition of subsequent images in the sequence. Therefore, time intervals from 1/100 s to 5 s, preferably time intervals from 1/64 s to 2 s, can be used.
[033] The image detector, preferably, can comprise at least one detector selected from the group consisting of a line detector that has a line of photosensitive sensor elements and a two-dimensional detector that has a two-dimensional matrix of photosensitive sensor elements. Photosensitive sensor elements are also referred to as pixels, as described above. Preferably, a two-dimensional array detector can be used, most preferably a rectangular array detector. The matrix preferably comprises at least 3, preferably at least 5 or even at least 10 pixels in each dimension, such as at least 50 pixels in each dimension. As an example, a two-dimensional array detector comprising 20 to 1,000 pixels in each dimension can be used.
[034] Other preferred achievements refer to the correction of the change in relative position. As indicated above, the correction may preferably comprise the use of at least one image in the image sequence as a reference image. The reference image is kept unchanged during correction. At least one a, preferably more than one, and preferably all other remaining images in the image sequence, can then be corrected using at least a computational pixel position correction, for example, through the use of a mathematical transformation of the matrices of these images, such as one or more transformations as listed above. Calculational correction can be selected in such a way that a correlation between the reference image and the remaining corrected images in the image sequence is maximized. In other words, the calculational correction can be selected in such a way that, as indicated above, the characteristic aspect of the test field can be found in the same location and has the same orientation in each and every image in the corrected image sequence. at least to a predefined predetermined degree of tolerance. As used in the present, the term "correlation" refers to an arbitrary measure to indicate the identity or similarity of images and / or aspects contained in these images. Therefore, as an example, one or more correlation coefficients can be used to quantify the similarity and / or identity of the images, such as the empirical correlation coefficients and / or Pearson correlations.
[035] As mentioned above, the calculational correction may comprise an offset of the pixels of the remaining images in the image sequence in at least one spatial direction. This displacement of pixels can be accomplished through a translation transformation of the matrix of the information values that represent the images. The offset can be selected in such a way that the correlation between the reference image and the remaining corrected images between is maximized. The offset can be selected individually for each image of the remaining images in the image sequence.
[036] In an additional or alternative way, for a displacement of the pixels of the remaining images, a rotation can be used. Therefore, the calculational correction can comprise at least one rotation of the remaining images in the image sequence over at least one rotational axis of at least one rotation angle. The rotational axis and / or the rotation angle can be selected in such a way that the correlation between the reference image and the remaining corrected images can be maximized. Again, the rotational axis and / or the rotation angle can be selected individually for each image of the remaining images in the image sequence. In addition, the calculational correction may include a plausibility check. Therefore, in the event that a calculational correction is required, which ends up exceeding a predetermined limit value, the correction may return an error and / or may be interrupted. Similarly, if more than one calculational correction is plausible, such as by detecting more than one standard match, more than a high or plausible correlation, an error can be returned and / or the calculational correction can be aborted.
[037] Other preferred embodiments of the present invention relate to the characteristic aspect mentioned above. One or more characteristic features can be used to perform the correction. The characteristic aspect may comprise at least one characteristic aspect selected from the group consisting of: a test field roughness detectable in the images in the image sequence; a granularity of the test chemical in the test field detectable in the images in the image sequence; the failures of the detectable test field in the images in the image sequence; at least one, preferably at least two fiducial marks comprised in the test field and detectable in the images in the image sequence. As used herein, the term "failure" can refer to an arbitrary imperfection in the test chemical and / or test field, such as dirt, fibers, cracks or any other type of irregularity. Other types of characteristic features can be used.
[038] The method can still comprise at least one step of deriving the actual analyte concentration from the image sequence or sequence of corrected images. Preferably, an analyte concentration is detected by detecting at least one optical property of the test chemical and / or by detecting at least one change in at least one optical property of the chemical in question. test due to the optically detectable detection reaction of the test chemical and the analyte. Therefore, at least one optical property can comprise at least one optical property selected from the group consisting of a color, an absolute reference and a relative reference. As used herein, the term "color" refers to a specific absorption of light in at least a predetermined spectral range, which can reside in the visible and / or ultraviolet and / or infrared spectral region. The term "remission" refers to an indirect reflection of light, such as scattered light. Therefore, to determine remission, at least one light source can be used to illuminate the test field from at least one side of detection, and light reflected and / or scattered from the field of The test can be detected using the detector mentioned above, preferably at an angle different from the illumination angle of the test field. The term "relative remission" refers to a standardized remission, where, preferably, a specific remission is used as a norm value. Therefore, when the detection changes from at least one optical property after applying the sample of the body fluid to the test field, a so-called white remission value before applying the sample can be used to normalize the later values of remission, to obtain the relative remission. The white value is also referred to as an empty, dry value. At least one of these values can be used. In case a normalization (also referred to as a standardization) is performed, such as for the creation of relative reference values, normalization can occur on the basis of a complete image, on the basis of a partial image, on the basis of pixel- a-pixel. Therefore, on a pixel-by-pixel basis, an information value for each pixel in an image can be divided by the information value of a corresponding pixel in a blank image.
[039] At least one optical property of the test chemical and / or at least a change of at least one optical property can be derived from an information value, more than an information value or all information values contained in the arrays of one, more than one or all images in the image sequence or the corrected image sequence. The examples will be provided below in more detail.
[040] At least one fluid sample from the body can be applied to the test field during the acquisition of the image sequence. Consequently, the image sequence can be subdivided into two or more image sequences, depending on the time point of acquisition of the respective image. Therefore, the sequence of images may comprise a sequence of blank images, wherein the sequence of blank images may comprise a plurality of acquired images in white prior to the application of the body fluid sample to the test field. Preferably, the blank image sequence may include all images acquired prior to application of the sample. The blank image sequence can also be referred to as the empty, dry image sequence.
[041] The sequence of blank images, preferably, can be used to obtain at least one information about the test field, before applying the sample. For this purpose, preferably, the images corrected in white are used, that is, the images in white after performing the above mentioned, at least, a correction of the change in the relative position between the image detector and the test field in the sequence of blank images. Preferably, at least one average blank image is derived from the blank images in the blank image sequence after correcting the change in the relative position of the blank images in the blank image sequence. As used in the present, the term "average image" or, specifically, the term average blank image, refers to a result of a process of calculating the arbitrary average of several images, in which a matrix of average values is generated. Therefore, the calculation of the average can be carried out on a pixel-by-pixel basis, by means of the corresponding matrix fields. Therefore, in the case of a two-dimensional matrix, an average of the corresponding pixels of the matrices can be performed, therefore, generating an average value for each field of the matrices. The average calculation, in general, can comprise any type of known average method, such as a weighted average, a geometric mean or an arithmetic mean. The average blank image, therefore, can be a matrix that has the same number of fields in each dimension as the corrected images in the corrected blank image sequence, where each of the fields in the average blank image matrix contains an average value of information, as a result of a process of averaging over the corresponding fields of the corrected blank images.
[042] The medium blank image, preferably, can be derived in a continuous process during the acquisition of the images in the image sequence. In this continuous process, preferably, a preliminary average blank image can be derived from the acquired acquired blank images so far. New acquired blank images can be used to review the preliminary average blank image. Therefore, with each newly acquired blank image, the preliminary average blank image can be updated, thereby generating a new preliminary average blank image. The final version of the preliminary average blank image, that is, the preliminary average blank image derived after the incorporation of the last corrected blank image of the corrected blank image sequence, can then be used as the final average blank image . In general, the information of the corresponding pixels of the white corrected images of the blank image sequence can be used to obtain information of a corresponding pixel of an average blank image. Therefore, in general, the information of the corresponding pixels of the corrected blank images can be combined through at least a linear combination and / or at least an averaging operation for the derivation of the corresponding pixel of the blank image. average. Therefore, all pixels (i, j) n of all corrected images n of the corrected blank image sequence can be combined in at least one linear combination and / or at least one averaging operation, for the derivation of the corresponding pixel (i, j) av of the average blank image, for all i, j of the matrices.
[043] The analyte can be detected using the blank image sequence, preferably using the corrected blank image sequence and, preferably, using the medium blank image.
[044] In addition or alternatively, the method may comprise at least one additional step to determine at least one touchdown image, preferably at least one corrected contact image. As used in the present, the term “contact image” refers to an image of the sequence of images acquired precisely at the time of applying the sample, which is also referred to as the moment of contact, or to the image of the sequence of images acquired after moment of application of the sample that is necessary at a time that is the closest to the moment of application of the sample, compared to all other images in the image sequence. Correspondingly, as used in the present, the term “corrected contact image” refers to a corrected image of the sequence of corrected images acquired precisely at the time of application of the sample, which is also referred to as the moment of contact, or to the image corrected the sequence of corrected images acquired after the moment of application of the sample that is acquired at a time that is closest to the moment of application of the sample, compared to all other corrected images of the sequence of corrected images.
[045] Preferably, the contact image is an image that visualizes the test field or, at least, a part of it after the application of the sample, before any detection reaction occurs, through the lower tolerance hole that is provided by a detection limit of the image detector. Therefore, the contact image is an image of the test field or at least a part of it with the sample of the body fluid that moistened the test chemical is at least a part of it, where, however, preferably, no test detection chemical reaction has occurred. Therefore, the contact image can provide information regarding optically detectable changes to the test field or at least part of it in the images acquired before applying the sample, such as changes due to wetting of the chemical test with the body fluid sample and / or changes to the test field due to mechanical deformation of the test field due to the application of the sample, such as mechanical information due to contact with the test field with a capillary element and / or drilling, such as the lancet, to transfer the sample from the fluid body to the test field.
[046] Therefore, in an additional or alternative way, to the use of one or more of the blank image strings, the blank corrected image and the average blank image, the contact image or the corrected contact image can be used for the detection of the analyte.
[047] As an example, the analyte can be detected using at least one image, preferably at least one corrected image, acquired after applying the sample and / or any information derived from it, such as a temporal sequence of the average values obtained in relation to these images or corrected images or parts thereof. In addition, since the sample application can introduce the artifacts in the corrected image or image sequence, the detection of the analyte can take into account at least information derived from the contact image mentioned above, or corrected contact image . Therefore, as an example, changes in the corrected image sequence or image sequence due to the sample application can be fully or partially corrected, such as changes induced by wetting the test field and / or changes induced by mechanical deformation of the test field. In addition or alternatively, the detection of the analyte may take into account at least information derived from the blank image sequence, such as at least information derived from the average blank image. Therefore, as an example, the influences of the batch-to-batch variations of the test field and / or the influences of a test field lighting can be fully or partially corrected.
[048] Therefore, the analyte can be detected by comparing the images in the sequence of the corrected images with one or more contact images and the sequence of blank images, preferably with the average blank image and / or the image contact. As used in the present, the term "comparison" refers to an arbitrary process suitable for deriving information in relation to a deviation, which are the differences in the values of information contained in the images. Therefore, the term "comparison" can specifically refer to the formation of a value of the difference between two information values and / or to the formation of a quotient of two information values.
[049] The comparison, preferably, can be performed on a pixel-by-pixel basis, by comparing each pixel of the corrected images in the corrected image sequence with the corresponding pixels of the contact image or corrected contact image and / or with the corresponding pixels of the images in the blank image sequence, preferably the corrected blank image sequence and, preferably, with the pixels of the average blank image. Therefore, a pixel-by-pixel difference and / or a pixel-by-pixel ratio can be derived, generating a matrix of proportions by dividing the corresponding pixels of the corrected images and the corresponding pixels of the contact image and / or blank images, preferably the average blank image, and / or subtracting the corresponding pixels from the images in the corrected image sequence and the corresponding pixels from the contact image or the corrected contact image and / or the corresponding pixels from the images in white, preferably the average blank image.
[050] As mentioned above, the comparison between the images in the corrected image sequence and the contact image and / or the blank image sequence, preferably with the average blank image, can be performed on a pixel basis -a-pixel, therefore, deriving an array of comparison values, such as an array containing the differences and / or quotients.
[051] As an example, a comparison matrix can be derived and used for detecting the analyte, preferably for determining the concentration of the analyte. The information value of each pixel of the comparison matrix, preferably, can be a difference of corresponding values of information of the pixels of the corrected image or image and the pixels of the corrected contact image or contact image, the difference being divided by corresponding pixel information value of at least one blank image or corrected blank image, preferably the average blank image. Examples of this type of comparison matrix will be provided in more detail below. As an example, in the case of the information pixel value (i, j) of the nth image or corrected image of the image sequence are indicated by In (i, j), the information value of the average blank image is indicated by B (i, j) and the pixel information value (i, j) of the contact image is indicated by T (i, j), the corresponding pixel of the comparison matrix Cn can be derived according to the following Formula:

[052] To detect from the analyte, specifically to derive a concentration of the analyte in at least one sample of at least one fluid in the body, at least one comparison matrix can still be evaluated. Therefore, an average value of the information contained in this comparison matrix can be evaluated or, alternatively, only a part of this comparison matrix can be evaluated, such as the information values within a region of interest in the comparison matrix, as will be explained in more detail below.
[053] Preferably, the information contained in each pixel of the images in the corrected image sequence after applying the body fluid sample to the test field can be divided by the information contained in the corresponding pixel of at least one image in white, preferably the average blank image, therefore, creating normalized information for each pixel. Consequently, a sequence of corrected relative images can be created, each corrected relative image has pixels that contain the normalized information of the respective pixel. In this sense, at least, an average normalized value can be created over at least part of the sequence of corrected relative images, preferably in relation to a region of interest of the corrected relative images. Preferably, the normalized value may be an average value in relation to the portion of the sequence of the corrected relative images, preferably in relation to the region of interest of the corrected relative images. The average normalized value can preferably be used to derive a concentration of the analyte in the body fluid. The average normalized value, preferably, can be monitored as a function of the time after application of the body fluid sample to the test field. Examples of this method will be provided in more detail below. However, it should be noted that other types of derivation from the concentration of at least one analyte in the body fluid sample can be performed.
[054] Other realizations of the present invention relate to the detection of the limits of the test field. Preferably, the limits of the test field and / or the limits of a visible window of the test field can be detected in the sequence of corrected images. Detection can occur in each corrected image in the corrected image sequence, in a group of corrected images, or in a corrected image. As an example, detection of the test field boundaries and / or limits of a visible test field window can occur in the sequence of corrected blank images and / or in the average blank image. As used herein, the term “limits” refers to one, two, three or four limits of the test field, which determine a lateral extension of the test field and beyond which an image evaluation will not be performed. Therefore, as indicated above, the test field can be bounded by one or more limits beyond which no test chemical is applied to a vehicle of a test element. In an additional or alternative way, as mentioned above, the optically detectable reaction can be observed through the detector through a viewing window defined by one or more windows of a mask or box of a test element, which totally or partially covers the test field. Specifically, reference can be made to the publication WO 2010/094426 A1 mentioned above, which describes a box that has windows through which a reaction of the test chemical can be observed. Accordingly, the method according to the present invention may comprise detecting one or more limits of the test field and / or limits of the visible window of the test field in one, more than one, or even all images. the corrected image sequence and / or corrected blank image sequence and / or the average blank image.
[055] For the detection of the limits of the test field and / or the limits of the visible window of the test field, several methods can be used that, in general, are known to the technician of the subject. Therefore, one or more threshold methods can be used, comparing the pixel information of the corrected images with one or more thresholds. In general, since, after the correction process mentioned above, all images in the corrected image sequence must be oriented and / or correctly positioned, it may be sufficient to determine the limits of the test field and / or the limits of a window visible from the test field in one of the corrected images in the corrected image sequence, since the position of these limits can be transferred to the other corrected images in the sequence. Therefore, as an example, the limits of the test field and / or the limits of the visible window of the test field can be defined as a function of position coordinates of the corrected image sequence, where the function is preferably applicable to all corrected images in the corrected image sequence. Therefore, the sequence of corrected images can even be oriented and / or corrected, in such a way that the limits of the test field and / or the limits of the viewing window are oriented parallel to the axes of a matrix coordinate system. sequence of the corrected image sequence.
[056] Other preferred embodiments refer to a detection of the application of the body fluid sample to the test field. Therefore, in a preferred embodiment of the method, according to the present invention, the moment of application of the body fluid sample to the test field is detected in the image sequence, preferably in the corrected image sequence. As used in the present, the term "application moment" refers to a point in time when the transfer of the sample of the body fluid sample to the test field occurs. As used in the present, the term "time" can refer to an arbitrary or variable parameter indicating a progress of the method. This parameter can be a time parameter, an internal clock of a method execution device, or it can even be a number or an indicator of a specific image in the image sequence, in which the sample transfer is detected. Since, preferably, the images are acquired at predetermined points of time, the identifier of the specific image indicating the transfer of the sample indicates a specific moment in the process, therefore, indicating the moment of application of the sample of the body fluid to the test field.
[057] In the following, the moment of application of the sample of the body fluid to the test field is also referred to as the moment of application of the sample, the moment of contact, the moment of application or the moment of transfer. This moment can actually indicate a specific point in time or even a period of time, since the transfer of samples usually takes place over a period of time. In case the transfer time is actually a transfer period, the start of the transfer period can be indicated as the specific transfer time. Alternatively or additionally, the acquisition time of the first image in the image sequence, in which a transfer of the sample is detected, can be indicated as the time of the transfer.
[058] Several methods can be used to detect the moment of application. Therefore, the moment of application can be detected by observing one or more changes in the information contained in the sequence of images, preferably the sequence of corrected images. Therefore, changes in the average information contained in the images in the image sequence can be observed. In this sense, the sequence of uncorrected images and / or the sequence of corrected images can be used. Preferably, the timing of application of the body fluid sample to the test field can be detected by observing one or more changes in the corrected images in the corrected image sequence.
[059] For the detection of the moment of application, the neighboring images of the sequence of images, preferably the sequence of corrected images, can be compared after correction. The comparison, preferably, can be made through the use of average neighboring images. Therefore, for each corrected image or image, an average value can be derived, such as by averaging the values of the information contained in the corrected images or images and / or by averaging more than one predetermined group or determinable group of information values contained in these images. In this way, an average value of the difference for each pair of neighboring images can be derived, indicating the difference between neighboring images in the corrected neighboring images. The moment of application of the sample to the test field can be detected by comparing the mean value of the difference with at least one threshold. Other types of detection of the moment of application may be possible.
[060] As mentioned above, in addition to detecting the moment of application of the sample, a contact image can be identified in the sequence of images or, correspondingly, a corrected contact image can be identified in the sequence of corrected images. As used at present, the contact image is the image of the sequence of images after the application of the acquired sample as close to the moment of application of the sample. Therefore, the contact image can be acquired precisely at the moment of application of the sample or close to the moment of application of the sample. Therefore, as soon as the moment of application of the sample is identified, for example, through the use of one or more of the methods described above, the contact image is the image acquired at the time of application of the sample or, in the case of none no image acquired at this time, the next image in the image sequence, acquired at an acquisition time as close to the time of application of the sample.
[061] The contact image or corrected contact image, preferably, can be used to detect a region of interest, as will be described in greater detail below.
[062] Other realizations of the present invention refer to the fact that, after applying the sample to the test field, normally only a part of the test field actually performs a detectable reaction depending on the concentration of the analyte and therefore, only part of the test field is suitable for assessing the determination of the analyte concentration. Therefore, in a preferred embodiment of the present invention, after applying the sample of the body fluid into the test field, at least one region of interest is determined in the image sequence. This region of interest can be determined in an image in the image sequence, in a plurality of images in the image sequence or in all images in the image sequence. Preferably, the region of interest is determined in a corrected image, a plurality of corrected images, or in all corrected images in the corrected image sequence. Therefore, by executing the correction of the image sequence mentioned above, the region of interest can simply be defined by defining the coordinates in the corrected image sequence, since the orientation and / or positioning of the corrected images within the sequence corrected images remains constant.
[063] As used herein, the term "region of interest" refers to a group or set of pixels, defined by a group or set of pixel coordinates, in the image matrices of the image sequence, preferably in the images corrected from the sequence of corrected images, in which the set or group of pixels are considered to contain the information values to be considered for further analysis, with the purpose of detecting, qualitatively or quantitatively, at least one analyte in the sample of the body fluid. In an extreme case, the region of interest may comprise as little as a single pixel. However, normally, the region of interest comprises a plurality of pixels.
[064] Therefore, the region of interest can define a set or group of pixels in the corrected images or images, in which the values of the information contained in those pixels are considered for further analysis, while other information values of the matrices that are not contained in the set or group of pixels can be neglected, or can be considered to a lesser degree or a lower weight. Therefore, the region of interest can define a set or group of pixels that contains the information values contained in one or more of the images, which are considered significant to determine the concentration of the analyte, while the other pixels are considered insignificant or less significant.
[065] Preferably, the region of interest is determined using a digital mask, which assigns a value of 1 (to be considered for further analysis) or 0 (not to be considered for further analysis) for all matrix pixels corrected images or images in the image sequence or corrected image sequence. Therefore, preferably, a matrix, a plurality of matrices or all matrices of the image sequence or, preferably, of the corrected image sequence can be modified by the mask in such a way that all information values positioned outside the region of interest are replaced by 0, where all information values inside the region of interest are kept unchanged. Other examples will be provided below.
[066] To determine the region of interest, preferably following corrected images, several methods can be used. Preferably, at least one corrected image can be acquired before or during the application of the body fluid sample into the test field and at least one corrected image can be acquired after the application of the body fluid sample to the test field. As an example of a corrected image or image acquired during sample application, reference can be made to the corrected contact image or contact image as defined above. Therefore, the image acquired during the application of the sample can be the contact image as defined above.
[067] The image acquired after application of the sample, preferably the corrected image acquired after application of the sample, preferably can be an image acquired at a predetermined point of time after the moment of application of the sample. Preferably, the point in time can be selected in such a way that the detection reaction has already led to a significant change in the information values or, at least, some of the values of the information contained in the image. As an example, the image obtained after application of the sample can be acquired in a predetermined time interval after the moment of application of the sample, such as a period of time from 0.5 seconds to 4 s, preferably a period of time of 0.7 s of 2 if, more preferably, a time period of 1 s.
[068] As a preferred example, the image acquired before or during the application of the sample can be the contact image mentioned above. The image acquired after applying the sample can be an image acquired one second after the moment of applying the sample.
[069] To determine the region of interest, at least one corrected image acquired before or during sample application can be compared to at least one corrected image acquired after sample application, on a pixel-by-pixel basis. pixel, that is, by comparing each pixel of the corrected image acquired before or during application of the sample with the corresponding pixel of the corrected image acquired after application of the sample. In this way, a difference value can be generated for each pixel, such as by subtracting the pixel information value from the corrected image acquired before or during sample application from the corresponding pixel information value of the corrected image acquired after sample application. In this way, a difference value can be generated for each pixel, where the difference value indicates a difference in the information contained in the corresponding pixels of the corrected images acquired before or during the application of the sample and after application of the sample to the field. test. Therefore, a difference matrix can be generated, in which the differences of these images can be provided for each pixel.
[070] Based on these difference values, again, a threshold method can be used to determine the region of interest. Therefore, pixels can be classified as pixels belonging to the region of interest, or as pixels not belonging to the region of interest based on the difference values. Therefore, the difference values for each pixel can be compared to one or more thresholds, in which the differences that exceed (by themselves or in their absolute values) the threshold can be classified as the pixels belonging to the region of interest, in that other pixels may be pixels classified as not belonging to the region of interest. In this way, by determining the positions of the pixels that belong to the region of interest, a set or group of positions can be derived, by itself, which can be defined as the region of interest within the matrices.
[071] As mentioned above, at least one corrected image is acquired before or during application of the sample, and at least one corrected image is acquired after application of the sample. Preferably, the time to acquire the corrected image acquired before or during the application of the sample and the time to acquire the corrected image acquired after application of the sample are very close to the time of application of the body fluid sample to the test field. . Therefore, preferably, the acquisition time of the corrected image acquired before or during the application of the sample is selected to be greater than 1 s before the moment of application of the sample, and the acquisition time of the corrected image acquired after application of the sample. sample is selected no more than 1 s after the moment of applying the sample. Through this selection of the acquisition times, it can be ensured that the difference values defined above are mainly due to the wetting of the test field and / or the test chemical through the body fluid sample.
[072] The region of interest is determined in such a way that the region of interest can entirely reside within the test field or, preferably, within the limits of the visible window of the test field, as defined above.
[073] As mentioned above, when determining a matrix difference comprising the difference values for each pixel derived from comparing the corrected images acquired before or during sample application and after sample application, at least one threshold method can be used for pixel classification, to derive the region of interest. Several types of threshold methods are known in the art and can be used for the purpose of determining the region of interest. Therefore, one or more thresholds can be defined through the empirical measurements of typical samples, therefore, empirically deriving at least one threshold for the classification of pixels into pixels belonging to the region of interest and pixels not belonging to the region of interest. Therefore, at least one threshold can be defined for each dimension, such as a threshold for an x direction and a threshold for a y direction. Preferably, at least one threshold method can comprise an Otsu method. Therefore, pixels that contain the difference values can be classified into two sets or groups, a group that contains pixels that belong to the region of interest and a group that does not belong to the region of interest. Groups can be selected by selecting at least one threshold value that delimits the groups, where the pixels that have the difference values above the threshold are assigned to a first group, and the pixels that have the difference values below the threshold are assigned to the second group. At this point, the threshold can be selected in such a way that the variation between the difference values within the same class is minimized, while the variation between the difference values that belong to different classes is maximized. However, in addition to or alternative to the Otsu method, other types of threshold methods can be used.
[074] As mentioned above, the region of interest can be provided as an image mask. This image mask can be generated by indicating the pixels that belong to the region of interest. Therefore, the image mask can be adapted to replace all the information values of the images, preferably the corrected images positioned outside the region of interest by 0, while the information values of the pixels located within the region of interest can be kept unchanged. This image mask can be applied to an image, preferably a corrected image, for a plurality of images, preferably a plurality of corrected images, or for all images, preferably for all corrected images. Preferably, the image mask can be a binary image mask, multiplying all the information values of the pixels within the region of interest by 1, while the information values of the pixels located outside the region of interest are multiplied by 0.
[075] The region of interest can be used to detect at least one analyte in the body's fluid sample in several ways. Therefore, preferably, only the pixels belonging to the region of interest in the corrected image sequence can be used for the detection of the analyte in the body fluid sample.
[076] In addition, as mentioned above, the analyte concentration can be derived from the corrected image sequence or image sequence, such as through the use of at least one evaluation algorithm. The evaluation algorithm can be predetermined or can be determinable. In addition, the selection of the evaluation algorithm from a plurality of possible evaluation algorithms can be performed, based on the difference matrix mentioned above, which contains the differences in the information values of the images acquired before or during the application of the sample. and the images acquired after the application sample. Therefore, based on these differences, such as based on a difference on average from the complete images or a part of these images, such as across the region of interest mentioned above, a suitable evaluation algorithm can be selected.
[077] Therefore, as an example, the difference matrix mentioned above, which contains the differences in the information values of an image acquired 1 second after the moment of application of the sample and the contact image can be used. Based on the difference values contained in this difference matrix, based on an average difference value of all difference values contained in the difference matrix or in relation to the region of interest within the difference matrix, a decision can be made in an adequate evaluation of the algorithm to be used to determine the concentration of the analyte from the image sequence, preferably the corrected image sequence. Therefore, in the event that the average difference value satisfies at least one predetermined condition, an evaluation algorithm assigned to this predetermined condition can be selected. As an example, the predetermined condition may comprise a comparison with one or more thresholds.
[078] In another aspect of the present invention, a computer program is described. The computer program comprises the programming means for performing the steps of the method of the method, according to one or more of the embodiments of the method, according to the present invention, such as the embodiments described above or the embodiments described below. Therefore, the programming means for carrying out the step of acquiring the image sequence of the images from the test field can be provided, using the image detector, the programming means for detecting at least one characteristic aspect. of the test field in the images of the sequence and the means of programming the image to correct the change in the relative position in the image sequence, thereby generating the corrected image sequence. In addition, programming means can be provided to perform optional additional steps of the method as described above or as described below. Each programming medium, in general, can comprise one or more computational decryption commands adapted for a computer or computer network to perform the steps of the process, when the computer program is executed on the computer or computer network. Preferably, the programming means can be stored on a storage medium, such as a volatile or non-volatile computer storage medium, which can be deciphered by a computer or computer network, such as on at least one medium computational deciphering data. As used herein, the term "computer" can refer to an arbitrary data processing device. Therefore, the term "computer" can refer to a stationary data processing device and / or a portable device, such as a portable computer, a manual computer, a compact portable device or any other type of portable device.
[079] As used in the present, the term "portable", in general, can refer to a device that can be carried by a human person, such as in a pocket. Therefore, preferably, the portable device, in general, can have a weight that preferably does not exceed 1 kg, and, most preferably, does not exceed 500 g. In addition, the portable device preferably has a volume that does not exceed 1,000 cm3 and, most preferably, does not exceed 500 cm3.
[080] In addition, a computer system, such as a system comprising one or more computers and / or a computer network, is described, the computer system has at least one processor to load the computer program in order to according to the present invention, and for executing the computer program. The computer system itself can be wholly or partially part of a device for detecting at least one analyte in at least one sample of a body fluid, as described in greater detail below. Other implementations of the computer system may be possible.
[081] In another aspect, a storage medium is described, in which a data structure is stored in the storage medium, in which the data structure is adapted to execute the method, according to one or more of the achievements of the method, according to the present invention, after being loaded onto a computer or computer network.
[082] In another aspect of the present invention, a device for detecting at least one analyte in at least one sample of a body fluid is described. The device comprises at least one receptacle of the test element for receiving at least one test element that has at least one test field with at least one test chemical. For the potential realizations of the test element, reference can be made to the description of the method in one or more of the realizations described above, or described in greater detail below.
[083] At least one test element, in general, can comprise one or more test elements, wherein the different types of test elements are usable within the scope of the present invention. Therefore, the device can comprise exactly one test element. Alternatively, the device may comprise a plurality of test elements. Therefore, as an example, the device can comprise a compartment, the compartment contains precisely a test element or, alternatively, the device can comprise a compartment, the compartment contains a plurality of test elements.
[084] Therefore, test strips, test strips, test cassettes or test compartments that comprise a plurality of test elements can be used. Specifically, reference can be made to the prior art documents listed above. Other types of test elements can be useful. Regarding the test element receptacle, any type of receptacle adapted to receive at least one test element is usable, in which the receptacle can be adapted to hold and / or position at least one test element on the part the device, such as in at least one measurement position and / or in at least one sample application position. Therefore, at least one measurement position and / or at least one position for applying the sample of the test element or a part thereof can be provided. The test element, as indicated above, can comprise at least one test field. The test element can optionally still comprise at least one puncture element, such as at least one puncture element which, preferably, can be mounted mobile in relation to the test field, to perform a puncture movement, a sampling movement or a puncture movement, thus generating an incision in a skin surface. Preferably, the test field remains in a fixed position during the drilling, sampling or puncture movement, in which a sample of a body fluid is transferred to the test field, such as by capillary action and / or pressing the drilling element or part of it to the test field after the drilling, sampling or puncture movement.
[085] The device further comprises at least one image detector, also referred to as a detector, for acquiring a sequence of images from the test field images. Regarding the potential realizations of the image detector, reference can be made to the detectors mentioned above. The device may further comprise one or more data storage devices adapted to store the images in the image sequence, such as one or more volatile or non-volatile data storage devices.
[086] Regarding the potential realizations of the image detector, reference can be made to the description of the method above. Specifically, the image detector may comprise at least one line detector and / or at least one two-dimensional image detector, preferably a CCD line sensor, a CMOS line sensor, a two-dimensional CCD array sensor , a two-dimensional CMOS array sensor or an arbitrary combination of so-called sensor systems and / or other sensors.
[087] The detector or image detector, in addition to the plurality of pixels, can still comprise one or more additional elements. Therefore, preferably, the detector may comprise one or more light sources adapted to generate light in the spectral range of ultraviolet and / or visible and / or infrared. At least one optional light source can be adapted to illuminate at least part of at least one test chemical and / or at least part of at least one test field. Therefore, preferably, the detector comprises at least one light source generating the light to illuminate at least a part of the test field, where the plurality of pixels is adapted for detecting the propagating light of the field of test. Therefore, preferably at least one light source and the plurality of pixels are arranged on the same side of the test field. Several possibilities regarding the nature of light propagation from the test field to the detector's plurality of pixels are viable. Without restricting these possibilities, the light generated by at least one light source can be referred to as the excitation light and the propagation light from the test field for the plurality of pixels can be referred to as the detection light or the light response. In general, as an example, the detection light can be or can comprise an excitation light that is fully or partially reflected or diffracted by the test field. In addition or alternatively, the detection light can be or can comprise the light emitted through the test chemical or its parts, such as fluorescence (for example, as in EP 1,780,288 B1) and / or phosphorescence light. Therefore, in general, the excitation light and the detection light can have the same wavelength or the same spectral properties or they can have different different wavelengths or spectral properties. Preferably, the angle of incidence of the excitation light for the test field differs from the angle of emission of the detection light. Therefore, preferably, no excitation light reflected directly from the test field is detected by the plurality of pixels. However, other possibilities are viable.
[088] The following is a preferred embodiment of a detector. The detector can be used as the detector in the methods and devices according to the present invention. However, the detector can also be performed in isolation, without the additional details of the present invention or with details relating to the detector only, without other elements of the present invention. The detector comprises at least one light source, as described above. The detector further comprises the plurality of optically sensitive sensor elements, as described in greater detail above. The detector further comprises at least one wavelength conversion material adapted to convert the wavelength of the light that passes the wavelength conversion material to a different wavelength, preferably a longer wavelength. long. Therefore, at least one wavelength conversion material can be comprised of one or more layers. Preferably, the wavelength conversion material can be comprised of at least one coating. The coating can totally or partially coat the plurality of pixels of the detector. The pixels of the detector can be comprised in the image sensor of the detector, wherein the image sensor is fully or partially coated by at least one coating comprising at least one wavelength conversion material.
[089] Preferably, at least one light source is adapted to generate ultraviolet light. However, other types of light sources can be used in an additional or alternative way. At least one wavelength conversion material, preferably, can be adapted to convert light in the spectral range of ultraviolet rays or light in the blue spectral range into visible light in the green or red or even infrared spectral range. Therefore, at least one wavelength conversion material can be adapted to convert light into a spectral range of maximum pixel sensitivity.
[090] Various types of wavelength conversion materials are known in the art and are partially commercially available. Accordingly, the wavelength conversion material may comprise one or more fluorescent materials and / or one or more phosphorescent materials. Organic and / or inorganic wavelength conversion materials can be used, such as one or more fluorescent dyes. Therefore, as an example, quantum dot materials, europium complex materials or fluorescent dyes known from display technology or lighting technology can be used as wavelength conversion materials.
[091] The detector may comprise an image sensor that comprises a plurality of pixels, such as a CMOS or CCD image sensor chip. The image sensor may have one or more coatings that comprise the wavelength conversion material, such as at least one fluorescent material. As an example, the wavelength conversion material can be adapted to convert photons that have a wavelength of 360 nm into photons of 600 nm. Other types of conversion are possible. A large number of dyes are available that have sufficient quantum efficiency and durability. Even though the conversion may imply some loss of photon flow, mainly due to geometric reasons and sensitivity, the conversion of the wavelength in total can increase the detection efficiency due to a better sensitivity of the image sensor chip in the superior wavelength. As a result, standard CMOS image sensor chips typically exhibit maximum efficiency in the 600 to 900 nm range. The sensitivity in the spectral range of ultraviolet or blue rays, on the other side, can be reduced to 10% or even less than 1%. Therefore, by using the wavelength conversion material, a high increase in detection efficiency can be achieved.
[092] In addition, the specific wavelength conversion material can be adapted to the specific detection circumstances, specifically in relation to the conversion properties. Therefore, normally, fluorescence dyes can convert light in the 360 nm range to light that has a wavelength of 600 nm or greater.
[093] In addition to at least one wavelength conversion material, the detector may comprise one or more filter materials adapted to fully or partially filter one or more wavelengths of light passing through the filter material. Therefore, a layer configuration can be used, such as a coating on the image sensor chip, which comprises one or more layers that have at least one wavelength conversion material and one or more layers that have, at least one filter material. In addition or alternatively, at least one wavelength conversion material and at least one filter material can be comprised in one and the same layer. Therefore, by using the filter material, unwanted light can be eliminated and / or suppressed. Through the use of this or other techniques, an efficient image sensor, such as an efficient CCD or CMOS image sensor chip, can be realized, for use in the ultraviolet spectral range, such as for use at a wavelength 360 nm, which can be specifically adapted for the present invention. However, other types of detectors can be used, additionally or alternatively.
[094] The wavelength conversion material can still be used in simple one-dimensional photodiodes, such as silicon photodiodes, such as BPW34 photodiodes, as, for example, available from Vishay Semiconductor GmbH, D-74025 Heilbronn, Germany. A general advantage lies in the relatively low costs, since silicon photodiodes are usually available in a price range of about 2 orders of magnitude below photodiodes that have a maximum sensitivity in the ultraviolet or blue spectral range, such as Gap photodiodes .
[095] The wavelength conversion material can still be encapsulated, to avoid a contact of the oxygen content for the analytical measurements. Encapsulation can take place through the use of a suitable matrix material, wherein the wavelength conversion material is totally or partially contained in the matrix material, such as those dispersed in the matrix material. As an example, one or more resins can be used as a matrix material, such as epoxy resins. The matrix material itself can form the coating. Accordingly, one or more fluorophores as the wavelength conversion material can be comprised of one or more residents forming a coating on the image sensor chip. In addition or alternatively, one or more glasses can be used as a matrix material, glasses containing one or more dopants, as wavelength conversion materials. As an example, one or more rare earths can be used as dopants that have the wavelength conversion properties. These types of glasses that contain one or more dopants are known, as an example, from fiber technology. The glasses can be coated directly on the image sensor chip and / or they can be manufactured separately and, independently, and later, they can be mechanically mounted to the image sensor chip, such as through the use of one or more glass plates that have the appropriate dopants. In this or other ways, an excellent signal-to-noise ratio can be achieved.
[096] The detector mentioned above, in one or more of the embodiments described specifically, can be combined with a test chemical containing NAD. Therefore, an excitation wavelength can be selected close to the maximum absorption of NADH, that is, closer to about 350 nm. For a test chemical with better carb-NAD stability, as well as a mutant enzyme can be employed (for example, as described, for example, in publication WO 2009/103540 A1 or in publication WO 2010/094632 A1). Due to the displacement at the peak of NADH-carb absorption, a light source or light emitting device, such as an LED, which emits at a wavelength of about 365 nm may be preferred. Alternatively, analysis systems that contain a detection layer with absorption indicators in the visible or infrared range, such as phosphomolybdate 2.18 (such as, for example, those described in EP 821.234 B1) or tetrazolium salts (for example, as described in US patents 6,656,697 B1 or US 7,867,728 B2).
[097] The present invention, as described herein, can also be applied or can be performed through the analytical detection layers, using oxidative coupling with glucose oxidase / peroxidase and the MBTH-ANS or MBTH-DMAB indicators (as described , for example, in US patents 4,935,346 A or EP 1,167,540 B1)
[098] Through the use of the wavelength conversion material, such as a material containing the europium fluorescent, the light sent from the test chemical, such as in a metering photometry mode, can be shifted to the closest to the maximum sensitivity of the detector's pixels, such as the pixels of a silicon image sensor chip.
[099] The device can still comprise at least one control unit, in which at least one control unit is adapted to execute the method, according to the present invention, that is, the method, according to one or more of the achievements described above, or described in greater detail below. As mentioned above, at least one control unit may preferably comprise one or more processors, where one or more processors can form a computer system and / or a computer and / or a computer network.
[0100] Specifically, the control unit can comprise one or more processors which, through the implementation of the appropriate software and / or program code, is adapted to execute the method, in accordance with the present invention. Therefore, for further potential details of the device, reference can be made to the method described above and / or described in greater detail below.
[0101] The device, preferably, can be a manual device and / or portable device. The implementation of the method, according to the present invention, is especially advantageous in manual devices, since the hardware resources necessary to execute the method, according to the present invention, can be kept at a very low level. Therefore, low-level data storage systems, as well as low-level control units, specifically very simple processors, can be used, without the need for sophisticated image analysis tools. Specifically, the correction of the change in the relative position between the image detector and the test field in the image sequence, preferably through the use of one or more correlation means, can be implemented through the use of low level processors.
[0102] As mentioned above, the control unit can preferably comprise at least one processor. At least one processor may preferably comprise one or more microcontrollers. Additionally or alternatively, at least one processor may comprise at least one application-specific integrated circuit (ASIC).
[0103] In another aspect of the present invention, a test system for detecting at least one analyte in at least one sample of a body fluid is described. The test system can preferably be a manual and / or portable test system. Regarding the portable expression, reference can be made to the definition provided above. The test system comprises at least one device, according to the present invention, according to one or more of the embodiments mentioned above and / or according to one or more of the embodiments described in more detail below. In addition, test systems comprise at least one test element that has at least one test field with at least one test chemical. In relation to at least one test element and at least one test field, reference can be made to the description mentioned above. The test chemical is adapted to perform at least one optically detectable detection reaction in the presence of the analyte.
[0104] The test system may preferably still comprise at least one drilling element. As used herein, the term "perforation element" refers to an arbitrary element adapted to create one or more openings in a portion of a user's skin. Therefore, at least one piercing element may comprise one or more lancets or lancet elements. The test system can still be adapted to pierce at least a portion of a user's skin using the perforation element. Accordingly, the test system may comprise one or more actuators adapted to engage at least one piercing element and to drive the piercing element in a forward motion to pierce at least a portion of the wearer's skin. In addition, at least one actuator can be adapted to retract at least one piercing element from the user's skin portion. Optionally, during this retraction movement, the body fluid sample can be collected through the drilling element and, optionally, can be transferred to at least one test element. Therefore, the test system can be adapted to perforate at least a portion of the user's skin, through the use of the piercing element, thereby creating the body fluid sample, in which the test system can still be adapted to transfer the fluid sample from the body to the test field of the test element. The sample transfer can be carried out in several ways. Therefore, at least one transfer element can be performed, and / or at least one perforation element, can be brought in close proximity or in contact with at least one test field to transfer the fluid sample from the body.
[0105] The drilling element preferably can comprise at least one microclassifier. At least one microclassifier can comprise at least one end of the lancet and at least one capillary to form the body fluid sample. Therefore, the microclassifier can comprise one or more lancets having one or more lancet ends, each lancet has at least one capillary, preferably at least one capillary channel or at least two capillary channels. As used herein, the term capillary can comprise any type of element adapted to compensate and / or transport a liquid by capillary action. The capillary may comprise a closed channel, such as a hollow needle channel, and / or an open channel, such as a capillary groove or a capillary slit. The closed channel can be peripherally bounded through a tubular capillary wall, while the open channel can provide an open surface along a longitudinal axis of the channel.
[0106] The transfer of the sample can be performed in several ways, as described above. Accordingly, the test system can be adapted to press the drilling element, preferably the microclassifier, into the test field, thereby transferring, at least partially, the body fluid sample to the test field. . Therefore, when using a microclassifier, the microclassifier can be pressed onto the test field. At that time, preferably at least one optional capillary channel can be brought into contact with at least one test field. Therefore, the fluid contained in the capillary channel body is transferred, at least partially, into the test field. In addition or alternatively, other types of sample transfer may be possible.
[0107] The test field can preferably be located inside a cavity in a test element box. A single test field can be understood or located in a box, or several test fields can be understood or located in a box of the test element. A test element, therefore, can be defined as an element that has at least one test field, preferably adapted for a single test, that is, precisely for the detection of an analyte in a sample of a fluid of the body. A test element or several test elements can be comprised in the test system, such as through the use of a compartment comprising a test element, or a compartment comprising more than one test element. In addition, several test elements can share a box or part of the box, such as by implementing a plurality of cavities, each receiving at least one test field, inside a common box. In addition, as described above, at least one piercing element can be located in each cavity. Preferably, the transfer of fluid from the body to the test field can take place inside the cavity.
[0108] In another preferred embodiment, the test system can be adapted to transfer the fluid sample from the body to the test field from the side of the application. The image detector, preferably, can be adapted to acquire the image sequence of the images of the test field from a lateral detection part being located opposite the lateral part of the application. Therefore, the test element can preferably comprise one or more vehicles, in which the test field is applied to the vehicle. The body fluid sample can be applied to the test field from the side of the application. The vehicle may be transparent and / or may comprise one or more openings, in which the acquisition of the sequence of images can occur through the vehicle, such as through the use of a transparent vehicle and / or through one or more optional openings in the inside of the vehicle.
[0109] In addition, the test element may comprise one or more boxes. A viewing window, whereby the detection side is observable can be defined through the window provided by the test element box. Specifically, reference may be made to the disk-shaped test element compartment described by publication WO 2010/094426 A1, as described above. Other realizations of the test element can be performed.
[0110] The test chemical, as described above, preferably directly or indirectly, can be applied to a vehicle of the test chemical of the test element. In addition, as described above, the test system may comprise a plurality of test elements comprised in a compartment. The compartment preferably can comprise a compartment box, in which the test system still comprises a test chemical vehicle, in which the test chemical vehicle is mechanically connected to the box, preferably via a plug-in connection and / or through a plug-in power connection. As mentioned above, this type of connection for connecting the test chemical vehicle to the box is quite favorable in relation to manufacture. However, as mentioned in the discussion of the prior art above, some relative movements of the test chemical vehicle and the box are possible when handling the test system, specifically the relative movements of the test field and the window provided by the test box. test element. The correction of the change in relative position mentioned above, however, can be adapted to correct these movements, thereby improving the precision and accuracy of the analyte detection.
[0111] The compartment, in general, can have an arbitrary format. Therefore, a compartment can be provided which comprises precisely a test element, such as a rectangular compartment. Alternatively, the compartment can comprise a plurality of test elements. Therefore, as mentioned above, the compartment can have an annular shape. In this case, the test elements are preferably oriented radially inside the annular shaped compartment. Therefore, the annular-shaped compartment may comprise a plurality of radially oriented chambers within a compartment box, in which, within each chamber, at least one microclassifier can be located and in which, within each chamber, at least , a field test can be located adapted for sample application.
[0112] The devices and methods according to the present invention provide a large number of advantages over devices and methods known in the art. Therefore, specifically, the method according to the present invention provides the possibility of carrying out a dynamic algorithm, which, in the first place, corrects for a change in the relative position. Therefore, the position of the test field and / or the rotation of the test field can be determined and / or corrected. By correcting for changes in position and / or to know the position of the test field and / or the test field rotation, all subsequent image processing steps can be performed on the same basis. Ideally, the correction is carried out in such a way that the test field boundaries and / or the limits of a visible test field window work horizontally and / or vertically in an image recognition process coordinate system. Therefore, ideally, the lines of research can be used in image processing, which are comparable across all images in the corrected image sequence. In addition, knowing the limits of the test field and / or the limits of a visible window of the test field, the limit areas of the test elements, such as areas outside the test field and / or the portions of the test element test outside the test field which, due to mechanical tolerances, are in the field of view of the image detector, can be eliminated for further image processing.
[0113] The method, according to the present invention, provides the means to solve the technical problem of correcting the losses and / or deformations in a sequence of images of a test field during complex operations, which may include the application sample and detection reactions. Conversely, the patent specification EP 2,270,421 A1 deals with the fact that, during the monitoring of part of the test on a support, mechanical displacements can occur due to mechanical tolerances. Consequently, EP 2,270,421 A1 proposes a recognition of a reference mark placed outside the test field (see, for example, paragraph [0066]) and proposes an initial correction of the loss, before the actual measurement starts (see , for example, paragraph [0036]). EP 2,270,421 A1, however, does not recognize the technical problem of image displacement or image formation during operation and during the detection reaction and does not provide any technical solution to the problem.
[0114] Furthermore, the method, according to the present invention, easily allows the determination of the moment of application of the sample of the body fluid to the test field. This sample application time can be determined based on the information values stored in the pixels of the corrected images. As described above, a contact image or corrected contact image can be identified in the image sequence or corrected image sequence, the contact image being the image in the acquired image sequence as close to the time of application of the sample. The contact image can be used to take into account changes in the image sequence due to the application of the sample and / or moistening of the test field, which are not generated through the detection reaction itself and, therefore, do not contain the information in relation to the concentration of the analyte. The contact image can be used to accurately determine a region of interest and / or to accurately determine the concentration of the analyte. In addition, one or more threshold values, such as one or more threshold values, can be used to detect a change in the value information on average in relation to the corrected image matrices, indicating a wetting of the test field by the fluid sample. of the body.
[0115] In addition, as described above, the method according to the present invention allows easy determination of a blank image, preferably a medium blank image. This determination of the average blank image, preferably, occurs in parallel with the determination of the moment of sample transfer. The determination of the average blank image can be performed at a very high precision by using the corrected images from the corrected image sequence before applying the body fluid sample, such as before placing the microclassifier in contact with the test field. . As mentioned above, to determine the average blank image, a continuous process can be used, which can also be referred to as a moving or sliding process. Therefore, in a moving process, a preliminary average blank image can be derived from the acquired blank images corrected so far, in which, with each new acquired blank image, the preliminary average blank image can be revised. . The averaging can take place on a pixel-by-pixel basis. In other words, the information values, such as the gray values stored in a specific coordinate of an image, are combined with the corresponding information values, such as the corresponding gray values of other images in the same coordinate. For this combination, basically any type of averaging process can be used, such as a process for determining an arithmetic mean value, a geometric mean value, or other types of averaging processes.
[0116] Specifically, the correction of the images mentioned above allows compensation for the vibrations and / or shocks of the images which, in conventional measurements, can lead to agitation or glare during the measurements. Therefore, in conventional methods, due to these vibrations and shocks, a combination of a plurality of images usually implies a high degree of uncertainty. Conversely, in the method, according to the present invention, it is possible to combine the corrected images of the corrected image sequence on a pixel-by-pixel basis, since the correction allows a correct combination of the corresponding pixels. Therefore, the average blank image can be determined with a high degree of accuracy.
[0117] Furthermore, the method, according to the present invention, allows a significant reduction of image data to be stored in a data store. Therefore, by combining the blank images from the sequence of images corrected to a single average blank image, a storage of this average blank image is fully sufficient for further and later determination of the analyte concentration. In addition, the continuous or medium moving process that can be used to determine the average blank image is also highly resource efficient. As a result, all the corrected blank images acquired so far can be combined for the preliminary average blank image and therefore only the preliminary average blank image can be stored, while the self-corrected blank images can be stored. be erased.
[0118] In addition, the correction of the change in relative position, mentioned above, can be carried out in a very simple and resource efficient way, which can easily be implied, even in manual or portable devices. Therefore, correction can occur on the basis of a standard recognition using the characteristic aspect of at least one image of the sequence. The characteristic aspect preferably can be or can comprise an image section of the images in the image sequence having a defined position and size, such as an image section of a reference image of the image sequence. As a reference image, for example, the first image in the image sequence can be selected. For the purpose of the correction mentioned above, the degree of identity or correspondence of this image sequence with the image sequences in another image can be quantified. This quantification can be easily incorporated using the appropriate algorithms. Therefore, the degree of identity or correspondence can be quantified by using cross-correlations or cross-correlation coefficients, preferably the standard cross-correlation coefficients. In addition, an offset in the images to be compared can be used, such as an Euclidean distance. In this way and / or in other ways, such as through the variation of the Euclidean distance, the images to be compared virtually can be displaced and / or rotated among themselves through the degrees of variation, in which, for each variation, the degree of identity and / or the correspondence can be determined, such as the standard correspondence of the image section, with the corresponding image section in the image to be compared. The displacement and / or rotation that leads to a high degree of identity and / or superior correspondence can be used for image correction, as well as for matrix transformation.
[0119] In addition, optionally, the degree of identity or correspondence can be compared with one or more thresholds or limit values. Therefore, in the case of the degree of identity and / or correspondence it should be considered less than a predetermined limit value, a different type of characteristic aspect, such as a different image section of the reference image, can be used, and the process pattern matching can be repeated. Therefore, a different image section that has a different position and / or a different size can be used for a retry.
[0120] The method for recognizing the characteristic aspect for the purpose of correction implies a number of advantages in comparison with other methods known in the prior art. Therefore, as an example, variations in clarity between images and / or errors or minor flaws in images, such as flaws due to dirt and impurities from the detector and / or an optical system and / or the test chemical, they do not normally lead to a failure of standard recognition. Not until the flaws or errors lead to significant interruptions or disturbances of the image, which should lead to the detection of an error anyway, the method will fail due to insufficient degrees of identity. Therefore, the method can still be used to determine errors in image acquisition, thereby leading to the selection and disposal of defective images, or even to an abort of the measurement, optionally in conjunction with an appropriate notice provided to a device user.
[0121] In addition, using the method according to the present invention, the region of interest can be efficiently determined, even for the complex geometries of the test field and / or for the complex geometries of the transfer of the sample to the test field, such as a transfer of the samples through one or more capillaries. Consequently, even complex geometries of transferring the sample to the test field can be processed, leading to regions of interest that have a very irregular shape. As an example, microclassifiers that have one, two or more capillary channels can be used, in which the transfer of samples from these capillary channels to the test field leads to an irregular shape of the area in which the body fluid sample is applied. Therefore, the region of interest can be determined by detecting significant changes in the corrected image sequence. For this purpose, one or more corrected images of the corrected image sequence after the moment of application of the test field sample, such as images acquired in a predetermined waiting time (also referred to as a predetermined time interval) after the moment of sample transfer or sample application (such as a 1 s wait time or similar wait times) can preferably be assessed on a pixel-by-pixel basis, and significant changes can be detected. Significant changes, as mentioned above, can be determined by comparing this corrected image acquired after applying the sample to an image acquired before or after applying the sample, such as the contact image. In case the image quality of the corrected image acquired after the moment of application of the sample, such as a signal-to-noise ratio, is insufficient, one or more other corrected images acquired after the moment of application of the sample can be used for the detection of significant changes, such as by averaging a plurality of corrected images acquired after sample application. Therefore, one or more of the corrected images acquired after applying the sample can be used to generate an average corrected image after applying the sample, and the difference values or one or more comparison matrices can be determined. Therefore, the average image after applying the sample can be compared with the average image acquired before or during the application of the sample, thus creating one or more matrices of difference of means, such as by determining the differences of these matrices, on a pixel-by-pixel basis. Therefore, the difference matrix can comprise, in each field, the difference values of the corresponding information values of the matrix of the average corrected image acquired after applying the sample and the image acquired before or during the application of the sample.
[0122] Using these or other types of comparison, significant changes can be easily assessed, such as using histograms and / or one or more threshold methods. In addition, optionally, a filtering of the histograms and / or a calculation of the mean of the histograms can be performed. On the basis of at least one histogram, a threshold value can be determined, which can be used to assess significant changes, as well as to assess the data contained in the difference matrix.
[0123] As mentioned above, the region of interest, in general, may contain a set of coordinates in the corrected images, that is, a group of pixels in each corrected image that can be used to qualitatively and / or quantitatively determine the analyte in the fluid body sample, such as to determine the concentration of at least one analyte. By using the method, according to the present invention, pre-sent and by using the possibility of averaging a plurality of images to detect significant changes in the images, the determination of the region of interest can be kept efficient It's simple. Therefore, since all corrected images are comparable in relation to their positioning and / or rotation, the limits of the region of interest can be determined, which can be applicable to a plurality of corrected images or even to all corrected images .
[0124] Therefore, in a next step to determine the region of the averages of interest, horizontal and / or vertical in the corrected images can be determined in the difference matrix, such as in an x and / or a y direction of the difference matrix. that contains the difference values. Therefore, a maximum of average gray values in the difference matrix can be calculated, for one or more directions in space, such as a maximum for an x direction (horizontal direction of the corrected images) and / or a maximum of a y direction ( vertical direction of corrected images). Based on this, at least a maximum of at least one threshold value can be determined, as can at least one threshold value for each direction in space. By using this at least a threshold value, significant changes can be determined, as can significant changes in horizontal and / or vertical mean values. Therefore, the limits of the region of interest in the difference matrix can be determined, that is, the coordinates of the limits that indicate the position of the limits in this matrix. In addition and optionally, safety distances can be applied and / or the known geometry of the sample transfer, such as the geometry of the microclassifier, can be used to correct this region of interest. In general, by using the difference matrix indicating changes before and after applying the sample on a pixel-by-pixel basis, the limits of the region of interest can be determined, such as four limits of a rectangular region of interest. , for the approximate delimitation of the region of interest.
[0125] This determination of the region of interest can be further refined by further processing the information values of the pixels within the approximate determined region of interest. Therefore, in general, the approximate determination of the region of interest can comprise several steps, such as at least an approximate determination of the region of interest and at least a refined determination of the region of interest. Therefore, as soon as a rough estimate of the region of interest is known, such as by determining the boundaries of the region of interest, such as the boundaries that define one or more rectangular regions of interest, this approximate estimate of the region of interest may still be assessed using statistical methods. In this way, an additional disposal of one or more regions within these approximate limits can be performed. As an example, the approximate region of interest can still be assessed using one or more histograms and / or using one or more filtering steps, such as the filtering steps for the histogram, to search for significant pixels , that is, the pixels assigned to the refined or revised region of interest. Therefore, on the basis of a histogram and / or a filtered histogram of the pixel information values of the difference matrix within the approximate region of interest, one or more threshold values can be determined. The information values of the pixels on one side of the threshold values can be assigned to the region of interest, while the pixels on the other side of the threshold can be determined to be outside the region of interest. In addition or alternatively, to find at least one threshold, the Otsu method mentioned above can be used, which can be based on standardized histograms.
[0126] In this way, or otherwise, through the evaluation of significant changes in the corrected images before / during sample application and after sample application, the region of interest can be easily determined as a set or group of coordinates or positions within corrected images. Therefore, the region of interest can be easily represented using a binary matrix that indicates whether an image element of the corrected images belongs to the region of interest or not. This binary mask or binary matrix on a pixel-by-pixel basis can precisely define the region of interest, even for the most complex geometries of microclassifiers, unlike traditional methods that normally use the definitions of regions of interest in instead of simple geometries, such as circular and / or rectangular geometries.
[0127] Based on this precisely defined region of interest, preferably containing a binary mask and / or an accurate definition of the pixel coordinates of pixels that belong to the region of interest, an accurate assessment of the corrected images can be performed, in order to qualitatively and / or quantitatively detect at least one analyte, such as for determining the concentration of the analyte to a high degree of accuracy. Therefore, by evaluating the information values of the pixels within the region of interest in the corrected images, the reaction kinetics of the detection reaction can be evaluated, preferably, on a pixel-by-pixel basis and / or on a basis on average to determine the analyte concentration. For this purpose of determining the concentration of the analyte, one or more evaluation algorithms can be used, which can be predetermined and / or determinable. As mentioned above, it is still possible to select an evaluation algorithm based on changes in the information values after a detection reaction has started, such as by comparing the information values of one or more images acquired before or during the application of the sample. and one or more images acquired in a predetermined period of time in a waiting time after applying the sample, such as 1 second after applying the sample.
[0128] For the purposes of determining the concentration of the analyte, in general, the information values in these corrected images acquired after the moment of application of the sample can be corrected and / or normalized, such as through normalization on a pixel- a-pixel, using the average blank image. Therefore, from each corrected image of the sequence of corrected images acquired after the moment of application of the sample, the corresponding matrices that contain, as an example, the relative remission values for each pixel can be determined, such as by dividing of the information value of each pixel by the value of the corresponding information contained in the corresponding pixel of the average blank image and / or subtracting the compensated ones on a pixel-by-pixel basis. Based on these modified and / or corrected images, a calculation of the average of the pixels of the corrected images can therefore occur, obtaining a very accurate average value of the information values of the pixels within the region of interest.
[0129] Summarizing the results mentioned above and the optional embodiments of the present invention, the following embodiments of the present invention are preferred: Realization 1: A method for the detection of at least one analyte in at least one sample of a fluid of the body, preferably for the detection of blood glucose and / or interstitial fluid, in which at least one test element with at least one test field is used, at least one test field has, at least one test chemical, wherein the test chemical is adapted to perform at least one optically detectable detection reaction in the presence of the analyte, preferably a color change reaction, where the method comprises the acquisition of a sequence of images of the images of the test field, through the use of at least one image detector, in which each image comprises a plurality of pixels, in which the method still comprises the detection of at least an asp characteristic ecto of the test field in the images of the image sequence, in which the method still comprises the correction of a change in the relative position between the image detector and the test field in the image sequence, through the use of the characteristic aspect, for therefore obtaining a sequence of corrected images. Realization 2: The method, according to the previous realization, in which each image in the image sequence contains an array of information values, preferably an array of gray values. Realization 3: The method, according to one of the previous realizations, in which the correction of the change in the relative position between the image detector and the test field comprises at least one correction selected from the group consisting of: a correction of a translation of an image of the test field on the image detector in at least one spatial direction; a correction of a rotation of an image of the test field in the image detector on at least one rotational axis; a correction of a distortion of an image of the test field in the image detector, preferably a distortion due to a deformation of the test field. Realization 4: The method, according to one of the previous realizations, in which the images in the image sequence are acquired in a constant sequence of time and / or at a constant rate of images. Realization 5: The method, according to one of the previous realizations, in which the image detector comprises at least one detector selected from the group consisting of a line detector and a two-dimensional detector. Realization 6: The method, according to one of the previous realizations, in which the correction of the change in the relative position comprises the use of at least one image of the image sequence of a reference image, in which the reference image is kept unchanged, in which the remaining images of the image sequence are corrected using at least one calculational correction of the pixel position, in which the calculational correction is selected in such a way that a correlation between the reference image and the remaining images in the corrected image sequence are maximized. Realization 7: The method, according to the previous realization, in which the calculational correction comprises a displacement of the pixels of the remaining images of the image sequence in at least one spatial direction, in which the displacement is selected in such a way that the Correlation between the reference image and the remaining corrected images is maximized. Realization 8: The method, according to the previous realization, in which the displacement is individually selected for each image of the remaining images in the image sequence. Realization 9: The method, according to one of the three previous realizations, in which the calculational correction comprises, at least, a rotation of the remaining images of the image sequence on at least one rotational axis of at least an angle of rotation, in which the rotational axis and / or the rotation angle are selected in such a way that the correlation between the reference image and the remaining corrected images is maximized. Realization 10: The method, according to the previous realization, in which the rotational axis and / or the rotation angle are individually selected for each image of the remaining images in the image sequence. Realization 11: The method, according to one of the previous realizations, in which the characteristic aspect comprises at least one aspect selected from the group consisting of: a roughness of the test field detectable in the images of the image sequence; a granularity of the test chemical in the test field detectable in the images in the image sequence; the failures of the detectable test field in the images in the image sequence; at least one, preferably at least two fiducial marks comprised in the test field and detectable in the images in the image sequence. Realization 12: The method, according to one of the previous realizations, in which an analyte concentration is detected by detecting at least one optical property of the test chemical and / or by detecting at least one alteration of at least one optical property of the test chemical due to the optically detectable detection reaction. Realization 13: The method, according to the previous realization, in which at least one optical property comprises at least one of a color, an absolute remission, a relative remission and a fluorescence. Realization 14: The method, according to one of the previous realizations, in which the body fluid sample is applied to the test field during the acquisition of the image sequence. Realization 15: The method, according to the previous realization, in which the sequence of images comprises a sequence of blank images, in which the sequence of blank images comprises a plurality of images acquired in white before the application of the fluid sample from the body to the test field. Realization 16: The method, according to the previous realization, in which at least one average blank image is derived from the blank images of the blank image sequence after the correction of the change in the relative position of the images blank image sequence. Realization 17: The method, according to the previous realization, in which the average blank image is derived from a continuous process during the acquisition of the images in the image sequence, in which a preliminary average blank image is derived from the images acquired blank images corrected so far, in which new acquired blank images are used to review the preliminary average blank image. Realization 18: The method, according to one of the two previous realizations, in which the pixel information of the white corrected images of the corresponding blank image sequence is used to derive information from a corresponding pixel of the average white image. Realization 19: The method, according to the previous realization, in which the information of the corresponding pixels of the images corrected in white are combined through, at least, a linear combination and / or through, at least, an operation of calculating the mean for the derivation of the corresponding pixel of the average blank image. Realization 20: The method, according to one of the five previous realizations, in which the analyte is detected by comparing the images in the corrected image sequence with the blank image sequence, preferably with the average blank image. Realization 21: The method, according to the previous realization, in which the comparison is carried out on a pixel-by-pixel basis. Realization 22: The method, according to the previous realization, in which the information contained in each pixel of the images of the corrected image sequence after the application of the body fluid sample to the test field is divided by the information contained in the corresponding pixel of at least one blank image, preferably the average blank image, therefore creating normalized information for each pixel, where, preferably, a sequence of corrected relative images is created, each corrected relative image has the pixels that contain the normalized information of the respective pixel. Realization 23: The method, according to the previous realization, in which at least an average normalized value is created over at least part of the sequence of corrected relative images, preferably over a region of interest of the corrected relative images. Realization 24: The method, according to the previous realization, in which the normalized value is an average value over the part of the sequence of the corrected relative images, preferably over the region of interest of the corrected relative images. Realization 25: The method, according to one of the two previous realizations, in which the average normalized value is used to derive a concentration of the analyte in the body fluid. Realization 26: The method, according to one of the three previous realizations, in which the average normalized value is monitored as a function of time after the application of the body fluid sample to the test field, therefore, preferably, generating a kinetic curve. Realization 27: The method, according to one of the previous realizations, in which the limits of the test field and / or the limits of a visible window of the test field are detected in the sequence of corrected images, preferably in the sequence of images in corrected white and / or medium blank image. Realization 28: The method, according to the previous realization, in which the limits are detected through the use of a threshold method and / or a standard recognition method. Realization 29: The method, according to one of the previous realizations, in which a moment of application of the body fluid sample to the test field is detected in the image sequence. Realization 30: The method, according to the previous realization, in which the moment of application of the body fluid sample to the test field is detected by observing changes in the information contained in the image sequence. Realization 31: The method, according to the previous realization, in which changes in the average information contained in the images of the image sequence are observed. Realization 32: The method, according to one of the three previous realizations, in which the moment of application of the body fluid sample to the test field is detected by observing the changes in the corrected images of the corrected image sequence. Realization 33: The method, according to one of the four previous realizations, in which the average neighboring images of the image sequence are compared after the correction of obtaining an average value of the difference for each pair of neighboring images, in which the moment of Sample application over the test area is detected by comparing the mean value of the difference with at least one threshold. Realization 34: The method, according to one of the previous realizations, in which the body fluid sample is applied to the test field during the acquisition of the image sequence, in which at least one contact image is preferably at least one corrected contact image is detected in the image sequence, preferably the corrected image sequence, where the contact image is an image of the image sequence acquired at a point in time closest to the moment of application of the body fluid sample to the test field. Realization 35: The method, according to the previous realization, in which the analyte is detected by comparing the images in the corrected image sequence with the contact image. Realization 36: The method, according to the previous realization, in which the comparison is carried out on a pixel-by-pixel basis. Realization 37: The method, according to one of the previous realizations, in which, after applying the sample of the body fluid into the test field, at least one region of interest is determined in the image sequence. Realization 38: The method, according to the previous realization, in which at least one corrected image acquired before or during the application of the body fluid sample to the test field is compared with at least one acquired corrected image after applying the body fluid sample to the test field, on a pixel-by-pixel basis. Realization 39: The method, according to the previous realization, in which a difference value is generated for each pixel, in which the difference value indicates a difference in the information contained in the corresponding pixels of the corrected images acquired before or during the application of the body fluid sample into the test field and after applying the body fluid sample into the test field, where the pixels are classified as pixels belonging to the regions of interest or as pixels that do not belong to the region of interest based on the difference values. Realization 40: The method, according to the previous realization, in which at least one threshold method is used to classify the pixels, preferably an Otsu method. Realization 41: The method, according to one of the three previous realizations, in which an image mask is generated indicating the pixels that belong to the region of interest. Realization 42: The method, according to the previous realization, in which the image mask is a binary mask. Realization 43: The method, according to one of the six previous realizations, in which only the pixels that belong to the region of interest in the sequence of corrected images are used for the detection of the analyte in the body fluid sample. Realization 44: A computer program that comprises the programming means to perform the steps of the method, according to the method of one of the previous realizations, when the computer program is executed on a computer or a computer network. Realization 45: The computer program according to the previous realization, in which the programming means are stored in a storage medium deciphered by a computer or computer network. Realization 46: A computer system that has at least one processor to load the computer program, according to one of the two previous achievements and for executing the computer program. Achievements 47: A storage medium, in which a data structure is stored in the storage medium, in which the data structure is adapted to execute the method, according to one of the previous achievements concerning a method, after it has been loaded to a computer or computer network. Embodiment 48: A device for detecting at least one analyte in at least one sample of a body fluid, the device comprising at least one receptacle of the test element to receive at least one test element that has at least one test field with at least one test chemical, in which the device still comprises at least one image detector for the acquisition of a sequence of images of the field images test, in which the device still comprises at least one control unit, in which the control unit is adapted to perform the method, according to one of the previous achievements. Realization 49: The device, according to the previous realization, in which the device is a portable and / or manual device. Realization 50: The device, according to one of the two previous embodiments, in which the image detector comprises at least one sensor of a line detector and a two-dimensional image detector, preferably a sensor of a line sensor CCD, a CMOS line sensor, a two-dimensional CCD array sensor and a two-dimensional CMOS array sensor. Realization 51: The device, according to one of the previous realizations concerning a device, in which the control unit comprises at least one processor. Realization 52: A test system for detecting at least one analyte in at least one sample of a fluid in the body, the test system comprising at least one device, according to one of the previous embodiments For a device, the test system still comprises at least one test element that has at least one test field with at least one test chemical, in which the test chemical is adapted to at least one optically detectable detection reaction in the presence of the analyte. Realization 53: The test system, according to the previous realization, in which the test system is a portable test system and / or a manual test system. Realization 54: The test system, according to one of the two previous embodiments, in which the test system, preferably the test element, still comprises at least one perforation element, in which the test system is adapted to pierce at least a portion of a user's skin through the use of the piercing element, thereby creating the body fluid sample, in which the test system is still adapted to transfer the body fluid sample to the test field of the test element. Realization 55: The test system, according to the previous realization, in which the piercing element comprises at least one microclassifier, the microclassifier comprises at least one end of the lancet and at least one capillary to compensate for the sample of body fluid, preferably at least one capillary channel or at least two capillary channels. Realization 56: The test system, according to one of the two previous realizations, in which the test system is adapted to press the drilling element into the test field, therefore transferring the fluid sample from the body to the field of test. Realization 57: The test system, according to one of the three previous realizations, in which the test field is located inside a cavity of a test element box, in which the transfer of body fluid to the test field occurs inside the cavity. Realization 58: The test system, according to one of the previous realizations regarding a test system, in which the test system is adapted to transfer the fluid sample from the body to the test field from a lateral part of the application, in which the image detector is adapted to acquire the image sequence of images from the test field from a lateral detection part located opposite the lateral part of the application. Realization 59: The test system, according to the previous realization, in which a viewing window, whereby the detection side is observable, is defined through a window provided by a box of the test element. Accomplishment 60: The test system, according to one of the two previous embodiments, in which the test chemical is applied to a vehicle of the test chemical. Realization 61: The test system, according to one of the previous realizations, relating to a test system, in which the test system comprises a plurality of test elements comprised in a compartment. Embodiment 62: The test system, according to the previous embodiment, in which the compartment comprises a compartment box, in which the test system still comprises a test chemical vehicle, in which the test chemical vehicle it is mechanically connected to the housing, preferably via a plug-in connection and / or via a plug-in power connection. Realization 63: The test system, according to one of the two previous realizations, in which the compartment has an annular shape, in which the test elements are oriented in a radial form on the side of the compartment. BRIEF DESCRIPTION OF THE FIGURES
[0130] Other details and optional aspects of the present invention can be derived from the later description of the preferred embodiments, preferably in conjunction with the dependent claims. In these realizations, in each case, the optional aspects can be performed in isolation or in an arbitrary combination of several aspects. The present invention is not restricted to embodiments. The achievements are shown schematically in the figures. Identical reference numbers in the Figures refer to identical, similar or functionally identical elements.
[0131] In the Figures: Figure 1 shows a concept of a test system for the detection of an analyte in a sample of a body fluid; Figure 2 shows a detector to be used in the test system, according to Figure 1; Figures 3A to 3C show different views of a microclassifier that can be used in the test system, according to Figure 1; Figure 4 shows a compartment to be used in the test system, according to Figure 1; Figures 5A to 5C show a schematic view of a sample transfer to a test field and an image acquisition; Figure 6 shows a series of images acquired with blood samples that contain different concentrations of glucose; Figures 7 and 8 show different options for detecting a region of interest; Figure 9 shows a block diagram of an example of a correction for a change in relative position following an image; Figures 10A and 10B show an example of a comparison image for the purpose of position correction; Figure 11 shows an example of a detection of a test field and / or a viewing window; Figure 12 shows an algorithm for determining an average blank image; Figures 13A and 13B show an example of detecting significant changes in an image sequence using histograms; Figure 14 shows an example of detecting the moment of transferring the sample to a test field; Figures 15 and 16 show an example of defining a region of interest on a pixel-by-pixel basis; Figure 17 shows an observation of a detection reaction for the detection of blood glucose, by observing the average relative remission over time for various glucose concentrations; and Figure 18 shows a schematic block diagram of a potential embodiment of a method, according to the present invention. DETAILED DESCRIPTION OF ACHIEVEMENTS
[0132] In Figure 1, a potential test system (110) for detecting at least one analyte in at least one sample of a body fluid is described in two different states, in which the system of test (110) on the left side of Figure 1 is shown in a closed state, and on the right side in an open state. The test system (110) comprises a device (112) for detecting at least one analyte in at least one sample of a body fluid and, as an example, a compartment (114) received in a receptacle (116) of the device (112).
[0133] The device (112) can comprise one or more control units, in which, in Figure 1, in general, they are indicated by reference (118). Therefore, as mentioned above, at least one control unit (118) can comprise at least one processor (120), such as at least one microcontroller. In addition, the device (112) can comprise one or more user interfaces (122), such as at least one monitor and / or at least one operational element that allows a user to operate the test system (110 ) and / or the device (112).
[0134] In the present embodiment, the housing (114) comprises a plurality of test elements (124), received in the housing (114) in a radial manner, therefore providing an annular shape of the housing (114) and / or a disc format of the compartment (114). However, it should be noted that other types of compartments (114) and / or devices (112) are possible using only one test element (124), instead of a plurality of test elements (124).
[0135] The device (112) provides at least one application position (126). The device (112) is adapted to rotate the compartment (114) inside the receptacle (116) and to perform a test with the test element (124) located in the application position (126).
[0136] The exemplary embodiments of the compartment (114) and / or the test elements (124) are described in several views and details in Figures 3A to 3C and Figure 4. The general configuration of these compartments (114) is known, for For example, in publication WO 2010/094426 of A1, therefore, reference can be made to this publication. However, other configurations are possible.
[0137] Therefore, the compartment (114) can comprise a compartment box (128), which can also be part of the boxes (130) of the test elements (124). In this specific embodiment, the box (130) comprises a lower housing (132), also referred to as the bottom part, which is normally made of an opaque and preferably black plastic material. In addition, the box (130) comprises an upper housing (134), also referred to as the covering part, which is normally made of a transparent plastic material. In addition, the box (130) may comprise a sealing film (136), which is normally produced from a metallic foil, such as an aluminum foil, which can be glued to the upper housing (134) by an adhesive (138) .
[0138] Furthermore, in this specific embodiment, each test element (124) can comprise one or more skin penetrating or perforating elements (140) which, as an example, can be formed as microclassifiers (142), each microclassifier it contains a lancet (144) with a lancet end (146) and at least one capillary element, such as at least one capillary channel (148). Further potential details regarding the microclassifiers (142) will be highlighted below.
[0139] In addition, compartment (114) may comprise a test chemical ring (150) comprising a test chemical vehicle (152) and a test chemical (154) applied to the chemical vehicle test (152) on a side facing the lower housing (132). The test chemical ring (150) can be glued to the lower housing (132) using at least one adhesive (156), such as, for example, an adhesive tape, and / or perhaps attached to the compartment box (128) through other means.
[0140] Within the compartment box (128), a plurality of cavities (158) are formed, through suitable recessions in the lower housing (132) and / or the upper housing (134). These cavities (158), in general, can be oriented radially, as shown in Figure 4. In each cavity (158), a microclassifier (142) is received, with the end of the lancet (146) turned to the side external of the annular shaped compartment (114) and with the capillary channels (148) facing downwards in Figure 4, towards the ring of the test chemicals (150).
[0141] In each cavity (158), yet, a window (160) is formed in the lower housing (132). The test chemical (154) accessible through these windows (160) therefore forms a test field (162) or part of a test field (162) for each test element (124). Therefore, through the window (160), the body fluid sample can be applied to the test fields (162). Each test element (124), therefore, in the present embodiment, comprises at least one test field (162) and, optionally, a cavity (158), a drilling element (140), as well as a box ( 130) which, in this embodiment, can be an integral part of the compartment box (128).
[0142] Further details on the generation of samples and / or the transfer of samples will be explained with respect to Figures 3A to 3C and Figures 5A to 5C. Therefore, Figure 3A shows a top view of the microclassifier (142), as described above. Figure 3B shows a cross-sectional view of the lancet (144) of the microclassifier (142), showing at least one, in the present embodiment, two capillary channels (148) which, as an example, may have a U shape Figure 3C shows a perspective view of the microclassifier (142) of Figure 3A, which still shows an optional coupling opening (164) at the rear end of the microclassifier (142), which allows coupling of the microclassifier (142) through a device actuator (112). This step is schematically shown in Figures 5A and 5B, which show a cross-sectional view of a cavity (158) of a test element (124).
[0143] As can be seen in Figure 5A, an actuator (166) couples a rear end of the microclassifier (142) and the coupling opening (164) directs the microclassifier (142) through a drilling opening (168) in the box (130), when the test element (124) is located in the application position (126) of the device (112), therefore creating an opening in a portion of a user's skin and generating and collecting a fluid sample of the body in the capillary channels (148). Then, as shown in Figure 5B, the actuator (166) retracts the microclassifier (142) into the cavity (158), in which the capillary channels (148), through the proper curvature of the microclassifier (142), are pressed against the test field (162). Therefore, at least part of the fluid sample contained in the body capillary channels (148) of the microclassifier (142) is transferred to the test field (162) of the respective test element (124). Therefore, the sample or part of the sample can react with the test chemical (154) contained in the test field (162) in a detection reaction, which leads to an optically detectable change. This change in at least one optically detectable property of the test chemical (154), due to the detection reaction can be observed through the window (160) which, in this way, defines a viewing window (170). Therefore, the lateral part of the test field (162) facing the cavity (158) can form a lateral application part (172), while the side facing the window (160) can form a lateral detection part (174) of the test field (162) and / or the test element (124). Optically detectable changes can be detected through a detector through the window (160), which is not shown in Figures 5A and 5B.
[0144] In Figure 5C, the sample transfer process and the detection reaction detection through a detector (176) are schematically represented. The detector (176) comprises an image detector (178) which has, as an exemplary embodiment, a two-dimensional array of rectangular photosensitive elements (180) which are hereinafter also referred to as pixels of the image detector (178). In addition, the detector (176) may comprise one or more light sources (182), such as one or more light-emitting diodes, to illuminate the detection side (174) of the test field (162), for example, through the test chemical vehicle (152) of the test chemical ring (150).
[0145] As an example, light sources (182) may comprise one or more light-emitting diodes (LEDs), such as two light-emitting diodes, which emit an ultraviolet or blue spectral range, such as in the spectral range of 350 to 400 nm, preferably in a spectral range of 350 to 380 nm or from 360 to 365 nm. Alternatively or in addition, other commercially available LEDs, such as Green-LEDs (from 570 to about 30 nm); Red-LEDs (from 650 to about 50 nm) or IR-LEDs (from 700 to 1000 nm) can be used. Alternatively or in addition to the LEDs, one or more other types of light sources can be used. Therefore, as an example, lamps can be applied. In an additional or alternative way, normally, depending on the requirements for the light signal, laser diodes can be used, although this type of light source usually implies an increase in costs.
[0146] The detector (176) can still comprise one or more optical elements (184), such as one or more optical imaging, to image the test field (162) and / or at least a portion of it for the image detector (178), therefore, creating an image (186) of the test field (162) and / or a part of it in the image detector (178). The image (186) can comprise an array of information values, such as gray values, forming a matrix in one or two dimensions. In Figure 5C, a two-dimensional matrix is shown, with an x dimension and a y dimension.
[0147] For the purpose of transferring the samples, as described above in relation to Figures 5A and 5B, the microclassifier (142) is activated through at least one actuator (166). When retracting the microclassifier (142) into the cavity (158) (not shown in Figure 5C), as described above, the sample contained in at least one capillary channel (148) of the microclassifier (142) is transferred to the field test (162) on the side of the application (172). This wetting of the test field (162) through the body fluid sample, as well as the optically detectable changes in the test chemical (154) due to a detection reaction that is not homogeneous, since normally only one portion (188) of the test field (162) will be moistened by the sample. By using the control unit (118), a sequence of images (186) can be acquired, to be evaluated as described in more detail below.
[0148] In Figure 2, a perspective view of a potential realization of the detector (176) is shown. As can be seen in this Figure, the detector (176), in addition to the image detector (178) (such as a CCD and / or CMOS detector) and at least the optical element (184), such as at least a lens can comprise at least one light source (182). In this embodiment, two light sources (182) are connected to the image detector (178), therefore forming a blog of the detector comprising the image detector (178), the light sources (182) and the optical element ( 184). As schematically represented in Figure 5C, preferably, a test field illumination (162) and an image of the test field (162) through the image detector (178), preferably occurs in an unreflected and / or not directed, such as through the use of different lighting and detection angles. Therefore, scattered and / or diffusely reflected light from the test field (162) can be recorded through the image detector (178).
[0149] As an example, CCD / CMOS image detectors (178) can be used, such as image sensors, available from Eureca Messtechnik GmbH, Germany. Therefore, image detectors from different manufacturers can be employed, such as CCD / CMOS image detectors manufactured by Fairchild imaging, Panavision, NEC, Sony, Toshiba, CMOS Sensor Inc., Kodak, Texas Instruments, TAOS or others. As an example, CCD / CMOS line sensors and / or area sensors of one or more of the CCD111A, CCD424 models manufactured by Fairchild imaging, of one or more of the LIS-500 or MDIC-2.0 models manufactured by Panavision, from model μPD3753CY-A manufactured by NEC, one or more of the models ICX207AK-E or ILX551B manufactured by Sony, one or more types TCD1201DG or TCD132TG manufactured by Toshiba, one or more of the models M106-A9 or C106 manufactured by CMOS Sensor Inc., one or more of the models KAC9618 or KAC-01301 manufactured by Kodak, model TC237B manufactured by Texas Instruments or model TSL201R manufactured by TAOS may be used. In addition or alternatively, camera plates containing one or more image sensor chips on printed circuit boards can be used as image detectors (178).
[0150] As discussed in greater detail above, the detector (176) can still comprise at least one wavelength conversion material, which is not shown in the Figures. Therefore, the image detector (178) can be coated with one or more coatings that comprise at least one wavelength conversion material, such as at least one fluorescent material. Therefore, specialized UV coatings that have the wavelength conversion properties are commercially available from Eureca Messtechnik GmbH, Germany. However, other types of wavelength conversion materials can be employed, such as inorganic or organic fluorescent materials.
[0151] After wetting the test field (162) by the body fluid sample, that is, after applying the body fluid sample to the test field (162), the reaction detection mentioned above will occur, leading to optically detectable changes in the test field (162) and / or the test chemical (154) contained therein. The examples of different images of the test field (162) as acquired through an image detector (178) are shown in Figure 6. Therefore, the different images indicate different types of body fluid samples, in this case blood, which contains different concentrations of the analyte to be detected, in this case, glucose. The analyte concentrations are provided in the images, indicated in milligrams per deciliter (mg / dL). As can be seen, from the gray values of the images (186) or changes in these gray values, an analyte concentration can be derived directly or indirectly. Therefore, color changes and / or changes in gray values in images (186) can be recorded and observed up to a specific end point at which the detection reaction has been completed. For this purpose, changes or rates of change of images (186) can be observed and compared with one or more thresholds, where, in the event of a change over a predetermined period of time, it is below a certain threshold, an end point of the detection reaction can be detected and the image at that end point can be evaluated to determine the concentration of the analyte. Examples of processes for deriving the analyte concentration from the images (186) and / or for determining the end point of the detection reaction are provided in EP 0.821.234 A2 mentioned above, as well as in EP 0.974,303 TO 1.
[0152] Therefore, through the evaluation of the images (186), the concentration of the analyte can be determined, through directly or indirectly evaluating the information provided in a sequence time of the images (186), which, at present, is referred to as a sequence of images (186). Preferably, the image detector (178) may comprise a grid of photosensitive elements (180) having a size of 20 μm to 50 μm, preferably 30 μm, in each direction. However, other dimensions are possible. In addition, several photosensitive elements (180) of the image detector (178) can be combined to form the combined photosensitive elements (180), in which the information provided through these combined photosensitive elements (180) is combined and considered as information from a superpixel image detector (178). In the present specification, this option should be included, regardless of whether the raw combined photosensitive elements (180) of the image detector (178) are used or whether several photosensitive elements (180) are combined, therefore, creating an image detector that comprises an array of superpixels.
[0153] Normally, which is also possible, within the scope of the present invention, only a part of the images (186) is evaluated to determine the concentration of the analyte. Therefore, a region of interest needs to be defined, which defines the pixels of the image (186), which are considered for the determination of the analyte. In Figures 7 and 8, several options to determine the region of interest (indicated by the reference (190)) are represented. Therefore, as shown in Figure 7, the fixed areas of the images (186), such as the predetermined rectangular areas, can be used as regions of interest (190). This is due to the fact that, normally, the application of the sample through the sample transfer described in Figure 5C occurs at about a predetermined position, leading to regions of sample transfer corresponding to one or more capillary channels (148), as can be be observed in the images (186) represented in Figure 7. At that moment, the images (186) in this Figure are generated through the use of samples with different concentrations of analyte.
[0154] The option shown in Figure 7, using the predetermined regions of interest (190), however, require very tight position tolerances, specifically tight tolerances in relation to the transfer sample and / or tolerances in relation to the microclassifier geometry (142), the detector (176) and the excess of all test elements (124).
[0155] Because of this, as will be described in greater detail below, a second option for determining the region of interest (190) is an analysis of the image sequence of the images (186) at an early stage of wetting the test field ( 162) with the body fluid sample and / or at an early stage of the detection reaction process. In this option, changes in the information contained in the pixels of the images (186) can be evaluated, which are caused by moistening the test field (162), after the transfer of the fluid sample. Specifically in the case, a signal-to-noise ratio of the images (186) is sufficient, just moistening the areas that can be evaluated after the end point is reached, which can lead to a significant reduction in the volume of data storage and the evaluation times.
[0156] As a third option, which can be combined with the second option listed above, changes in the values of the information stored in the image pixels (186) of the image sequence can be evaluated to determine the region of interest. Therefore, for detecting changes in the images (186), at least two of the images (186) can be compared, and the region of interest (190) can be determined on the basis of these detected changes. Therefore, image pixels (186) can be selected based on their history, such as by assigning those pixels with the highest rate of change in a given time interval to the region of interest (190). In Figure 8, two images (186) of the image sequence are described at different times, when the image on the right is acquired at a later point in time, compared to the image on the left. The different images (186) obtained from variations in capillary geometry can be used in the transfer step and reagent film compositions and, by selecting an appropriate method to determine the region of interest (190), the artifacts, the lack of color homogeneity, trapped air bubbles and changes depending on the time of the signal can be compensated.
[0157] As mentioned above, the method according to the present invention comprises at least one correction step to correct a change in the relative position between the image detector (178) and the test field (162) in the sequence of images. As mentioned above, the term "change in relative position" can refer to any type of movement of the test field (162), as observed through the detector (176) and, specifically, through the image detector (178). This type of movement can be due to internal and / or external reasons in the test system (110). Therefore, the corresponding movements and changes in position may be due to manipulation of the test system (110), for example, mechanical vibrations during the handling of the device (112) by a user, since, preferably, the device (112) can be a portable device. In addition or alternatively, the movements may be due to the action of the test system (110) itself, that is, the internal reasons. Therefore, the application of the body fluid sample to the test field (162), as shown in Figure 5C, can lead to a movement and / or distortion of the test field (162) itself, since, preferably, the microclassifier (142) can come into direct contact with the test field (162) or can even be pressed into the test field (162), thereby exerting mechanical forces. Therefore, as used in the present invention, any type of movement of the test field (162) or parts thereof, and / or any type of distortion of the test field (162) or parts thereof, as can be seen in the image detected by of the image detector (178), can be included in the term “change of relative position”.
[0158] According to the present invention, this change in the relative position between the image detector (178) and the test field (162) in the image sequence comprising the acquired images (186) at different times is at least partially corrected. An example of a correction process will be explained with reference to Figures 9 and 10A, 10B in the following.
[0159] Therefore, Figure 9 shows a schematic block diagram of a method, according to the present invention, leading to a sequence of corrected images. In a first step, step (192), a new image (186) is acquired. This new image (186), which belongs to a sequence of images of the uncorrected images, is corrected in at least one correction step (194). Therefore, at least one characteristic aspect of the test field (162) is detected in the image (step (196)) and the correction step (194) is performed based on the characteristic aspect. The final image correction (186) is indicated by the process step (198) in Figure 9.
[0160] As an example for a correction (198) based on detection (196) of at least one characteristic aspect, reference can be made to Figures 10A and 10B. Therefore, the image (186) to be corrected, that is, the image acquired as in the acquisition step (192), can be compared with one or more reference images. Therefore, as an example, the first image in a sequence of images can be used as a reference image, and all images in the image sequence acquired subsequently can be corrected to be positionally in conformity with this reference image. Basically, however, any other image in the image sequence can be used as a reference image, even combinations of several images.
[0161] Therefore, in Figure 10A, a portion (200) of the image (186) to be corrected can be selected as a characteristic aspect (202), including information values, as stored in this portion (200). At that point, from the point of view of the present invention, the portion (200) can be a portion of the reference image, and the corresponding portions in the image to be corrected can be searched, and / or the portion (200) can be searched. a portion of the image to be corrected, and the corresponding portions in the reference image can be searched. Both options are possible and will be included in the method according to the present invention. In the following, the option to define the characteristic aspect (202) in the reference image will be explained as an example, without restricting the scope of the invention.
[0162] Each image (186), including the reference image, can be described as a matrix comprising a certain number of information values I, in each position, or pixel of the image (186), such as the following:

[0163] At present, Ii, j indicates the information values of pixel i, j of image I, such as gray values. N and M are integers that indicate the image width (186) (N) and the image height (M). A specific position of this matrix, indicated by the coordinates i, j with 1 <i <M and 1 <j <N, indicates a pixel or a specific position in the image (186).
[0164] As indicated in Figure 10A, a characteristic aspect (202), being a part of a reference image, is selected, and a search for that characteristic aspect (202) in the image (186) to be corrected is performed. For this purpose, the portion (202) of the reference image is again shifted along the matrix I of the image (186) to be corrected. The portion (200) itself can be represented by a matrix with dimensions smaller than the matrix I. The portion (200) is displaced by r in an x direction and by s in a y direction, along a search region ( 204), which is inferior to the image (186) to be searched for itself. Starting with r = 0 and s = 0, the maximum values to be assumed by res during the displacement process are: rmax = M - hR, with hR being the height of the portion (200), and smax = N - wR, with wR being the width of the portion (200). in Figure 10A, wI indicates the image width (186), and hI indicates the image height (186).
[0165] For each possible displacement value (r, s), a degree of conformity and / or a degree of identity or similarity is determined for the portion (200) and the corresponding portion of the image (186) to be searched for. This is represented schematically in Figure 10B. Therefore, with R indicating the characteristic aspect (202) or the portion (200) to be searched for in the image (186), a search for the displacement coordinates (r, s) is performed so that the corresponding portion (200), ( 202) of image I corresponds to the R portion. As an example, for each pair of values (r, s), the following sum of the square differences can be determined:

[0166] Through the displacement of the characteristic aspect (202) (that is, through the displacement of R) over the entire image (186) to be searched, a dE can be determined for each displacement (r, s). Finally, by comparing all dE (r, s), determined in this way, a minimum of all dE can be determined, that is, a specific displacement (r, s) can be determined so that dE assumes a minimum value. This shift indicates a better estimate of a search result of the search for the characteristic aspect (202) in the image (186). To avoid the artifacts, this displacement candidate can be compared with one or more limit values, that is, by comparing the minimum value of E, min with at least one limit value. Only if the dE, min is lower or almost as large as the limit value, can a positive match be detected.
[0167] It should be noted, however, that the sum of the square differences mentioned above, is just an algorithm out of a large number of possible algorithms suitable for the search for standard matches to find the characteristic aspects in the image (186). This search algorithm for pattern matches, for example, is described in W. Burger et al .: Image Processing, Springer Verlag, London, 2008, pages 429-436. However, in an additional or alternative way, other possible types of algorithms of the standard search correspondences of the characteristic aspects in the images (186) can be used to determine a change between images.
[0168] As soon as the search for the characteristic aspect (202) in the image (186) is successful, the search will return an offset (r *, s *), indicating the amount of change in the relative position between the image (186) and the reference image. This displacement (r *, s *) can be used in the method step (198) in Figure 9 to perform image correction (198), therefore, creating a corrected image (step (206) in Figure 9) and, in this corrected image to a corrected sequence, which contains the corrected image sequence. For this purpose, the following correction of the image matrix I (186) can be performed:
For r * = 0 and s * = 0: I * = I.
[0169] As an example, r * es * can be limited to plausible values, such as values not exceeding 50. Instead of adding the offset (r *, s *), as indicated above, a subtraction is also possible .
[0170] For further details on the potential algorithm for the correction step (194) and / or for other optional realizations, reference can be made to the publication mentioned above W. Burger et al .: Digital Image Processing, Springer Verlag, London, 2008, pages 429-436. Specifically, the model matching algorithm described in this text passage can be applied to the correction algorithm or correction step (194). However, it should be noted that other types of correlation and / or matching algorithms can be used, such as cross-correlation algorithms and / or standard recognition algorithms. In addition, it should be noted that the algorithm described as an exemplary embodiment above, in relation to the examples presented in Figures 10A and 10B, only refers to changes in position, which can be described through a shift in an x and / or a direction displacement in a y direction. However, a large number of other correction algorithms can be used. Therefore, with a similar algorithm, as described above, changes in rotation can be detected, for example, through the use of a rotation parameter, instead of the translation parameters (r, s) and the search for standard matches. In addition, through the use of similar algorithms, a distortion of the images (186) can be detected and corrected for step (198) in Figure 9.
[0171] The entire correction step (194) in Figure 9 can be performed repeatedly, just like once for each newly acquired image (step (192)). In Figure 9, this is indicated by repetition (208). Repeat (208) can be performed for each newly acquired image, as shown in Figure 9, as an online correction process. However, other time sequences for correction can be applied, such as applying the correction step (194) to the entire sequence of images and / or to a plurality of images (198), that is, through correction simultaneously a plurality of images (186).
[0172] In addition, as indicated by the reference number (210) in Figure 9, the corrected image sequence or corrected sequence can then be used for further evaluation. Therefore, all additional steps for the evaluation (210) of the images (186) of the image sequence for the purpose of detecting at least one analyte in the body fluid sample can be based on the corrected images and / or the sequence of corrected images. Therefore, as mentioned in detail above, the accuracy of all other steps can be greatly improved.
[0173] The images (186) that are submitted to the correction algorithm, such as the correction algorithm of Figure 9, do not necessarily need to contain all the image information as processed through the image detector (178). Therefore, as can be seen in the exemplary images (186) in Figures 6 to 8, part of this image information processed through the image detector (178) may be outside the actual visible window or viewing window (170), as represented in Figure 5B. Therefore, before or after the evaluation of the images (186), the limits of the test field (162) and / or the limits of the visible window of the test field (162) can be detected in the raw images to be corrected in the correction step (194) or corrected images in the corrected image sequence. This step is preferably performed in the sequence of corrected images, since, in this case, the test field (162) and / or the limits of a visible window of the test field (162) can be provided in a system absolute coordinates of the corrected images, that is, it can be valid for all corrected images of the corrected image sequence. Therefore, as shown in Figure 11, the viewing window (170) and / or visible window (the two terms are used interchangeably at present) can be detected by evaluating the information values in the image matrices. Therefore, for example, by using a gray scale edge detection of images processed through the image detector (178), before or after correction, the limits (212) can be detected, as well as the limits in the direction x and / or the limits in the y direction. When using edge detection to detect the limits (212), the selection edge detection algorithm can be a tolerant algorithm against debris or similar image disturbances. For further analysis, images (186) can be reduced to the area within these limits (212), to reduce the amount of data. In addition, in an additional or alternative way, a position and / or rotation of the viewing window (170) can be detected in the images (186) and / or in the corrected images. Therefore, the term “image”, as described above, does not necessarily need to refer to the entire amount of information provided through the image detector (178). Data reduction can occur in one or more steps of the method, according to the present invention, such as by reducing the images (186) of the reduced images or corrected reduced images, which only contain the information values within the field test (162) and / or within the viewing window (170) or visible window of the test field (162). Both options are referred to when using the term “image” (186).
[0174] In addition, in conventional methods for qualitatively and / or quantitatively detecting an analyte concentration, the determination of a blank value and / or an empty value (the two terms will be used interchangeably in the present) usually plays an important role . Therefore, since the optical properties of the different patches of the test fields (162) or test chemicals (154) can be different, even in a dry state, the blank value can be used to normalize the detected optical changes that are actually due to the detection reaction. Normally, in known methods, such as in publication WO 2012/010454 A1, one or more blank values are acquired before applying the body fluid sample to the test field (162) and, after applying the sample, the Subsequent measurement values are normalized by using this blank value, as well as dividing all subsequent measurement values processed through the detector (176) by at least one blank value.
[0175] The present invention, specifically the correction step (194), offers the possibility of generating, in a very high precision, a medium blank image, instead of a single blank value, the average blank image contains the average information from a plurality of blank images.
[0176] In Figure 12, an implementation of an algorithm to generate an average blank image is described in the schematic block diagram. The medium blank image can also be referred to as a medium dry empty portrait. The algorithm, as represented in Figure 9, can be implemented for the method, according to the present invention.
[0177] In a first step, a new image (186) is acquired using the image detector (178), as indicated by step (192) of the method in Figure 12. As explained in relation to Figure 11 above , this newly acquired image can be reduced to a real image within the limits (212) of the viewing window (170). In addition, one or more correction steps (194) can be performed, for example, using the algorithm, as explained in relation to Figure 9 above. The optional detection of the viewing window (170) can be performed using the uncorrected raw images, and / or using the corrected images.
[0178] Subsequently, in the newly acquired image or in the newly acquired corrected image, at least one step (214) of application detection of the sample is performed. This sample application detection provides an answer to the question whether, between the acquisition of the previous image and the current, newly acquired image, the body fluid sample was applied to the test field (162). This sample application detection step (214) can be performed by detecting changes in the information values I (i, j) of the corrected image or image compared to the previous image. As an example, changes in the averages of the information values contained in the corrected images or images can be calculated and used, for example, using the following Formula:
on what
indicates an average value of the differences of the neighboring images in In-1 and In, where In (i, j) indicates the pixel information value (i, j) of the average newly acquired image or the corrected newly acquired image, and where In-1 (i, j) indicates the corresponding pixel information value (i, j) of the previously acquired image or the corrected image previously acquired.
[0179] The difference in the average value | ΔIFI can still be optionally normalized to the average information contained in the In image, to obtain a relative differentiated average value:

[0180] Next,
it is also referred to as.
In Figure 14, IΔI ^ I is represented as a function of the image number n. Therefore, the entire images can be evaluated, or only a part of the images (186). Therefore, only the part of the images within the limits (212) of the viewing window (170) can be evaluated. The graph shows a significant peak (216). The number of the image n, or what is equivalent, as an indicator of a time variable, the number or the identifier of the image in which the peak (216) is detected indicates the moment (218) of the application of the sample. Therefore, by generating suitable values, indicating changes in the information contained in the images (186), preferably the corrected images, the moment (218) of the application of the sample can be easily detected. In addition, optionally, an image (186) of the image sequence that was or is acquired as close to the time (218) as the sample application can be determined, this image to be referred to as a contact image.
[0181] Returning the average blank image in Figure 12 to the detection algorithm for each newly acquired image or corrected newly acquired image, an appropriate test can be performed indicating whether an application of the sample has occurred or not. This detection (214) of the sample application, for example, can use the algorithm as described above, or, additionally or alternatively, any other type of algorithm for detecting significant changes due to the application of the sample.
[0182] In case the sample application has not been detected (arm N in Figure 12, indicated by the reference number (220)), the newly acquired image, preferably the newly acquired image corrected after performing the step correction (194), can be added to a preliminary average blank image (step (222) in Figure 12), on a pixel-by-pixel basis. For this purpose, the following Formula can be used:
where Bpr, n indicates the average blank image nth (pixel i, j), and In indicates the newly acquired image nth before applying the sample (pixel i, j). As an initial value for Bpr, 1, the first blank image I1 can be used. Therefore, an average blank image Bpr, n can be generated using a moving algorithm, by updating the preliminary average blank image Bpr, n. Finally, as soon as the sample application has been detected (arm Y in Figure 12, indicated by the reference number (224)), the most recent average blank image can be used as the final blank image, therefore defining the average blank image B (step (226)) in Figure 12), using the following Formula:

[0183] This medium blank image B can be used as a reference for all subsequent changes to the images that are due to the sample application.
[0184] Therefore, the average blank image B can be used to determine the concentration of the analyte by normalizing the corrected images or images, preferably after applying the sample, to the average blank image B, on a base pixel-by-pixel, such as by transforming the images (ie an image, a plurality of images, or even all images) into one or both of the following transformed matrices:


[0185] In addition or alternatively, as mentioned above, at least one T-contact image or corrected contact image can be used to determine the concentration of the analyte. Therefore, as an example, one or more of the following transformed matrices can be used to determine the concentration of the analyte:

[0186] The last Formula corresponds to the comparison matrix Cn, as defined above, which can also be used for the detection of significant changes for the purpose of detecting a region of interest in the image sequence and / or in the corrected image sequence .
[0187] Other types of standardization processes are possible. Next, when reference is made to the evaluation of the image sequence or the sequence of corrected images for the purpose of determining the concentration of the analyte, the possibility of using the corrected images or images, or the possibility of using normalized, transformed images or images corrected, such as using one or more of the previous Formulas, should be possible.
[0188] In addition, as mentioned above, the determination of a region of interest plays an important role in many processes for detecting analytes in a body fluid. The method, according to the present invention, specifically by creating the sequence of corrected images, for example, using the algorithm described in Figure 9, allows a very precise determination of the region of interest (190), specifically and, in preferably on a pixel-by-pixel basis in the corrected images.
[0189] First, as shown in Figures 13A and 13B, a detection of significant changes can occur, to define the region of interest (190) and / or a region of preliminary interest. For this purpose, changes in the values of information contained in the images or, preferably, the corrected images, are evaluated. As an example, the following difference matrix indicating changes in the information values of the images can be used:
where dI indicates a matrix indicating the change in the information values and where Im indicates a corrected image or image or combined or transformed image acquired after the moment (218) of the application of the sample and where In, indicates an image, a corrected image, or a transformed or combined corrected image acquired before or during the moment (218) of the application of the sample. As an example, In may be the contact T image mentioned above. However, other achievements are feasible, such as those in which In is an image acquired before the moment of applying the sample. Preferably, the Im and In images are acquired as close as possible to the moment (218) of the application of the sample. Therefore, In may be the image acquired immediately before the time of application of the sample, and Im may be the image acquired immediately after application of the sample. In an additional or alternative way, images acquired at predetermined time distances before and after application of the sample can be compared, for example, by using the image acquired one second before application of the sample as the In image and the acquired image one second after applying the sample as the image Im. Alternatively, In may be the contact image, and Im may be an image acquired at a point in time of 0.5 s to 4 s after the moment of application of the sample, such as 1 s after the time of application of the sample. . In addition, several images can be combined, for example, by using a preliminary average blank image, instead of the In image and / or by using the average blank image B instead of the Im image.
[0190] In Figure 13A, an example of the information values contained in the matrix dI is drawn on a three-dimensional graph. At that time, x and y indicate the pixel coordinates, and z indicates the information value of the corresponding pixels (i, j) of the matrix dI, such as a gray value. In the exemplary embodiment of Figure 13A, significant changes can be detected. In the event that no significant changes are detected in the dI matrix, several images can be combined, such as more than one image obtained after applying the sample, to detect significant changes.
[0191] As can be seen in Figure 13A, significant changes are usually found in the entire area of the test field (162), partially in the form of peaks, due to the lack of chemical homogeneity. The graph in Figure 13A still shows different regions. At that time, a background region (228) can be detected, a region of the non-wet test field (230) and a real region of significant changes (232) that, later on, can be a candidate for the region of interest (190).
[0192] To define the region of interest (190) or a rough estimate of the region of interest (190), a threshold method can be used, for example, through the use of an algorithm, as represented in Figure 13B. In this algorithm, the image of the changes, as indicated by the matrix dI above, is indicated by the reference number (234). The image of the changes (234) was acquired with a blood sample that has a glucose concentration of 556 mg / dL. In this image of changes (234), the average values of the lines (graph (236) in Figure 13B) and the average values of the columns (reference number (238)) can be formed by calculating the average of the matrix information values dI in relation to each row and each column, respectively. These average values can be compared with one or more thresholds, indicated by reference numbers (242) and (244) in Figure 13B. By calculating the average in relation to the rows and / or columns, the peaks in the matrix dI can be removed. In addition, a filtration of the average values can be applied. Through the use of the threshold method, as represented in Figure 13B and / or through the use of other types of threshold methods, the flatness in the matrix dI, indicating a region of significant changes, can be detected, and the coordinates of the limits of the region of interest (190) and / or a rough estimate of the region of interest can be generated. Therefore, the outermost columns where the graph (236) crosses the threshold (242) can be used as the column coordinates for the region of interest, and the outermost coordinates where the graph (238) crosses the threshold ( 244) can be used as the line of coordinates for the region of interest, thus generating a rectangular region of interest (190). In addition to simply crossing the threshold, other criteria can be used. Therefore, an additional criterion may be that a predetermined number of subsequent values of the graphs (236), (238) also exceeds the threshold values (242) or (244), respectively.
[0193] In addition to or alternative to the approximate estimate of the region of interest, using the threshold method of calculating the average represented in Figures 13A and 13B, other pixel-oriented methods can be used, as will be explained in greater detail in relation to to Figures 15 and 16.
[0194] Therefore, the method described in Figure 15 can start with the region of interest, preliminary, rectangular (190), as determined by using the method in Figures 13A and 13B. The information values contained in the matrix dI outside the preliminary region of interest can be eliminated or replaced by 0. In an additional or alternative way, very small values of information in the matrix dI can be cut and / or replaced by 0. In addition, others smoothing types can be applied, such as removing peaks within the mean values. In this way, an image of changes (246) can be generated, as described on the left side in Figure 15, in a graph similar to that provided in Figure 13A.
[0195] In addition, a histogram method can be used to evaluate the image of changes (246), as indicated by the histogram (248) in Figure 15. In this histogram (248), the relative frequency for each gray value or value of the information contained in the dI matrix (vertical axis) is graphically represented for each information value or gray value (horizontal axis).
[0196] In addition, to evaluate the histogram (248), an additional threshold method can be used. As mentioned above, this threshold method may imply an automatic selection of one or several thresholds (250). For this purpose, threshold methods as known in the art can be used. Therefore, preferably, the so-called Otsu method can be used. In this method, the threshold (250) is selected, separating the histogram (248) into two classes: class (252) of information values below the threshold (250) and class (254) of information values above the threshold (250) , in the dI change matrix or a corrected change dI matrix, before or after filtering or applying additional data reduction steps. The threshold (250) can be automatically selected in such a way that the variance of the values of each of the classes (252) is minimized, while the variation between the values of different classes is maximized.
[0197] In a next step, all pixels that belong to the class (252) can be eliminated from the region of interest (190). Therefore, a region of interest (190) in the form of a binary mask (256) can be generated, as shown in the right part of Figure 15. Therefore, the region of interest (190) can be defined by a binary matrix. ROI with ROI (i, j) = 1 if the pixel (i, j) is within the region of interest, and ROI (i, j) = 0 if the pixel (i, j) is outside the region of interest (190). When graphically representing this binary mask (256), the black-and-white portrait occurs, as shown in Figure 15.
[0198] In Figure 16, a more complex region of interest (190) indicated by a binary mask (256) is represented which, normally, can result when using the microclassifiers (142) that have two capillary channels (148), as represented in Figure 3A. In this case, the region of interest (190) and / or the binary mask (256), clearly shows two separate horizontal white bands, due to the parallel capillary channels (148). In addition, the binary mask (256) can eliminate bubbles and / or debris, as indicated by the black regions (258) in Figure 16. Therefore, this way of detecting the region of interest (190) on a pixel basis -a-pixel, through the evaluation of images (186), preferably, after the correction step (194), determines the region of interest (190) with a very high precision and confidence. Disturbances within the region of interest (190), such as disturbances caused by bubbles or debris, can be reliably removed through the threshold process. The method can still be refined, such as through the application of additional plausibility controls for the automatically detected region of interest (190), such as through the implementation of plausibility controls in relation to the dimensions, number of relevant pixels or other types of controls. plausibility of the region of interest (190).
[0199] The region of interest (190) defined on a pixel-by-pixel basis, using the binary mask (256), can be used to evaluate the images (186), preferably after the correction step (194), as well as through the evaluation of the corrected images acquired after the moment (218) of the application of the sample. Therefore, the corrected images (186) after performing the correction step (194) can be transformed as follows:

[0200] Therefore, in any image, image sequence, group of images, corrected image or average image, all pixels outside the region of interest can be deleted, while pixels within the region of interest (190) can be maintained unchanged. Therefore, image masking can occur.
[0201] For further evaluation and determination of the analyte concentration, the pixels of the images within the region of interest, such as the pixels of the IROI matrix, can be evaluated. For this purpose, one or more of the images, corrected images or, for example, the relative images, such as one or more of the images mentioned above I ', I''orI''' can be masked, using only the pixels of these images within the region of interest. Therefore, the image I '''mentioned above can be used and masked for further evaluation, for example, using the following Formula:

[0202] Therefore, a matrix that indicates a change in the remission or remission of the relative percentage can be created. From this matrix I '' 'ROI, the average values can be created in relation to all pixels within the ROI, in which, basically, any type of average process can be used, such as the average values in relation to all the pixels within ROI, average value, weighted averages and other averaging processes.
[0203] In Figure 17, these average values I for several measurements are represented (vertical axis), as a function of time t. At this time, the average values I are given in arbitrary units, such as the remission of the relative percentage, and the time is presented in seconds. The curves represent different concentrations of glucose in the blood, where the curve (260) indicates 20 mg / dL, the curve (262) indicates 70 mg / dL, the curve (264) indicates 150 mg / dL, the curve (266) indicates 250 mg / dL, and the curve (268) indicates 550 mg / dL. For each concentration, several graphs are listed, indicating the low dispersion of these curves from (260) to (268). In addition, in this graph, the moment (218) of application of the sample is marked by an arrow.
[0204] The curves from (260) to (268), as represented in Figure 17, can still be evaluated, for example, through the use of known methods for the evaluation of the reaction kinetics. Therefore, to assess the concentration of the analyte, the I value can be determined at a predetermined time after application of the sample. In an additional or alternative way, as, for example, known from EP 0.821.234 A2 or EP 0.974.303 A1, an end point of the reaction can be determined by observing changes in the curves from (260) to ( 268). Therefore, the change in curves from (260) to (268) can be observed over time and, if the change over a predetermined time interval is below a predetermined threshold, an end point of the detection reaction can be determined. The I value at this end point can be used for the calculation of the analyte concentration, such as through the use of a defined algorithm transforming the end point value to a corresponding analyte concentration, as known in the prior art.
[0205] In Figure 18, an overview of a potential realization of the method, according to the present invention, with several optional steps of the method is described as a block diagram. In an optional first step of the method after the start (270), as described with reference to Figure 11 above, the test field (162) and / or the limits (212) of the viewing window (170) can be detected, preferably automatically (step (272)). In addition, in the optional step of method (274), as described above with reference to Figure 14, the moment (218) of applying the sample, also referred to as the moment of contact, is detected. In addition, preferably, in parallel with the method step (274), the blank image and / or the average blank image can be detected, such as using the process described above with reference to Figure 12 (step ( 276)).
[0206] In addition, as mentioned above in relation to Figures 13A, 13B, 15 and 16, significant changes due to sample application can be detected (step 278), significant changes can be processed (step (280)), and the region of interest (190) can be determined (step (282)).
[0207] Subsequently, in a series of additional optional steps of the method, the reaction kinetics can be measured (step (284)), the measurement results can be evaluated (step (286) of the measurement analysis) and, further, optionally, a statistical analysis of the measurement results can be performed (measurement statistics, step (288)), before the method is finished (step (290)).
[0208] When observing the method described in Figure 18, it appears that no method of the separate step (194) (correction step) is portrayed in this realization. This is due to the fact that the correction step (194) can be part of one, more than one, or even of all the steps of the method, according to Figure 18. Therefore, the detection of the field of The test in step (272) can be performed in conjunction with the correction step (194), that is, through the evaluation of one or more corrected images. In addition, the steps for detecting the moment of sample application (step (274)) and the detection of the blank image or medium blank image (step (276)) can be performed in conjunction with the correction step (194) , that is, through the use of corrected images. In addition, preferably, the significant changes in step (278) can be detected in conjunction with the correction step (194), that is, by using one or more corrected images to detect significant changes. Similarly, as mentioned above, the processing of significant changes (step (280)) and the determination of the region of interest (190) (step (282)) can be performed using the corrected images. In addition, as mentioned above in relation to the curves represented in Figure 17, the measurement of the reaction kinetics (284) can be performed through the use of the corrected images, as well as the analysis of the measurement results (step (286)) and the measurement statistics (step (288)). Therefore, the correction algorithm mentioned above can be beneficial in one, more than one, or even in all stages of the method of exemplary implementation of the method for determining the concentration of at least one analyte in a sample of a body fluid, as shown in Figure 18. LIST OF REFERENCE NUMBERS - (110) test system - (112) device '- (114) compartment - (116) receptacle - (118) control unit - (120) processor - (122) user interface - (124) test element - (126) application position - (128) compartment box - (130) box - (132) lower housing - (134) upper housing - (136) sealing film - (138) adhesive - (140) perforation element - (142) microclassifier - (144) lancet - (146) lancet end - (148) capillary channel - (150) test chemical ring - ( 152) test chemical vehicle - (154) test chemical - (156) adhesive - (158) cavity - (160) window - (162) test field - (164) coupling opening - (166) actuator - (168) drilling opening - (170) viewing window - (172) application side - (174) detection side - (176) detector - (178) image detector - (180) photosensitive element - (182) light source - (184) optical element - (186) image - (188) moist portion - (190) region of interest (ROI) - ( 192) new image acquisition - (194) correction step - (196) detected characteristic aspect - (198) correction - (200) portion - (202) characteristic aspect - (204) search region - (206) corrected image created - (208) repetition - (210) subsequent evaluation - (212) limits - (214) detection of the sample application - (216) peak - (218) moment of the sample application - (220) no sample application detected - ( 220) add new image to the preliminary average blank image - (224) application of the detected sample - (226) defined average blank image - (228) background region - (230) non-wet test field - (2 32) region of significant changes - (234) image of changes - (236) average line values - (238) average column values - (242) threshold - (244) threshold - (246) image changes - (248) histogram - (250) threshold - (252) class of information values below the threshold - (254) class of information values above the threshold - (256) binary mask - (258) bubbles or debris - (260) 20 mg / dL - (262) 70 mg / dL - (264) 150 mg / dL - (266) 250 mg / dL - (268) 550 mg / dL - (270) start - (272) test field detected - (274) moment of sample application detected - (276) detected blank image - (278) significant changes detected - (280) significant change processes - (282) determined ROI - (284) measured reaction kinetics - (286) measurement analysis - (288) measurement statistics - (290) ending

权利要求:
Claims (19)
[0001]
1. METHOD FOR DETECTING AT LEAST ONE ANALYT AT LEAST ONE SAMPLE OF A BODY FLUID, in which at least one test element (124) with at least one test field (162) is used, the at least one test field (162) having at least one test chemical (154), wherein the test chemical (154) is adapted to perform at least one optically detectable detection reaction in the presence of the analyte; characterized by the method comprising the acquisition of a sequence of images of the images of the test field (162), through the use of at least one image detector (178), in which each image comprises a plurality of pixels, in which the method still comprises the detection of at least one characteristic aspect of the test field (162) in the images of the image sequence, in which the method still comprises the correction of a change in the relative position between the image detector (178) and the image field. test (162) on the image sequence using the characteristic aspect, whereby obtaining a sequence of corrected images, in which the sequence of corrected images is adapted to observe the time-dependent change of at least one optically detectable property of the test field (162) due to the detection reaction of the test chemical (154) with the analyte to be detected.
[0002]
2. METHOD, according to claim 1, characterized by the detection of the characteristic aspect, comprising the selection of at least a specific part of one or more images of the image sequence, denoting the information contained in this part as the characteristic aspect, in which the other images in the image sequence are scanned or searched for this information or similar types of information.
[0003]
METHOD, according to any one of claims 1 to 2, characterized by the correction being individually adapted for each image in the image sequence, according to the characteristic aspect detected in the specific image.
[0004]
METHOD according to any one of claims 1 to 3, characterized by correcting the change in relative position comprising using at least one image of the image sequence as a reference image, in which the reference image is kept unchanged , in which the remaining images in the image sequence are corrected using at least one calculational correction of the pixel position, where the calculational correction is selected in such a way that a correlation between the reference image and the remaining corrected images of the image sequence is maximized.
[0005]
5. METHOD, according to claim 4, characterized by the calculational correction comprising at least one of the following: - a displacement of the pixels of the remaining images of the image sequence in at least one spatial direction, in which the displacement is selected in such a way whereas the correlation between the reference image and the remaining corrected images is maximized; - at least one rotation of the remaining images of the image sequence on at least one rotational axis by means of at least one rotation angle, in which the rotational axis and / or the rotation angle are selected in such a way that the correlation between the reference image and the remaining corrected images are maximized.
[0006]
METHOD, according to any one of claims 1 to 5, characterized in that the characteristic aspect comprises at least one aspect selected from the group consisting of: a test field roughness (162) detectable in the images of the image sequence; a granularity of the test chemical (154) of the test field (162) detectable in the images in the image sequence; test field faults (162) detectable in the images in the image sequence; at least one, preferably at least two, fiducial marks comprised in the test field (162) and detectable in the images of the image sequence.
[0007]
7. METHOD according to any one of claims 1 to 6, characterized in that the body fluid sample is applied to the test field (162) during the acquisition of the image sequence, in which at least one contact image is detected in the image sequence, where the contact image is an image of the image sequence acquired at a point in time closest to the moment of application of the body fluid sample to the test field (162).
[0008]
METHOD, according to any one of claims 1 to 7, characterized in that the body fluid sample is applied to the test field (162) during the acquisition of the image sequence, wherein the image sequence comprises an image sequence blank, where the blank image sequence comprises a plurality of blank images acquired prior to application of the body fluid sample to the test field (162), wherein at least one average blank image is derived from of the blank images in the blank image sequence after correcting the change in the relative position of the blank images in the blank image sequence.
[0009]
9. METHOD according to claim 8, characterized in that the average blank image is derived from a continuous process during the acquisition of the images in the image sequence, in which a preliminary average blank image is derived from the corrected blank images acquired so far, in which new acquired blank images are used to review the preliminary average blank image.
[0010]
10. METHOD, according to any one of claims 8 to 9, characterized in that the analyte is detected by comparing the images in the corrected image sequence with a contact image and / or the blank image sequence, in which the comparison runs on a pixel-by-pixel basis.
[0011]
11. METHOD according to claim 10, characterized in that at least one average normalized value is created over at least part of the sequence of corrected relative images, in which the average normalized value is monitored as a function of time after sample application of body fluid to the test field (162).
[0012]
METHOD, according to any one of claims 1 to 11, characterized in that limits (212) of the test field (162) and / or limits (212) of a visible window of the test field (162) are detected in the sequence corrected images.
[0013]
13. METHOD, according to any one of claims 1 to 12, characterized in that a moment of application of the body fluid sample to the test field (162) is detected in the image sequence.
[0014]
14. METHOD according to any one of claims 1 to 13, characterized in that after application of the body fluid sample to the test field (162), at least one region of interest is determined in the image sequence.
[0015]
15. METHOD according to claim 14, characterized in that at least one corrected image acquired before or during the application of the body fluid sample to the test field (162) is compared with at least one corrected image acquired after application of the body fluid sample to the test field (162) on a pixel-by-pixel basis, whereby generating a difference value for each pixel, where the difference value denotes a difference of information contained in the pixels corresponding to the corrected images acquired before or during and after applying the body fluid sample to the test field (162), where the pixels are classified as pixels belonging to the region of interest, or as pixels not belonging to the region of interest based on the difference values.
[0016]
16. METHOD, according to any one of claims 14 to 15, characterized in that an image mask is generated denoting the pixels that belong to the region of interest.
[0017]
17. DEVICE (112) for the detection of at least one analyte in at least one sample of a body fluid, characterized in that the device (112) comprises at least one test element receptacle (116) to receive at least one test element test (124) which has at least one test field (162) with at least one test chemical (154), where the device (112) still comprises at least one image detector (178) for the acquisition of a sequence of images of the test field images (162), in which the device (112) still comprises at least one control unit (118), in which the control unit (118) is adapted to execute the method, as defined in any of claims 1 to 16.
[0018]
18. TEST SYSTEM (110) for detecting at least one analyte in at least one sample of a body fluid, characterized in that the test system (110) comprises at least one device (112), as defined in claim 17, the test system (110) further comprising at least one test element (124) which has at least one test field (162) with at least one test chemical (154), wherein the test chemical (154 ) is adapted to perform at least one optically detectable detection reaction in the presence of the analyte.
[0019]
19. SYSTEM (110) according to claim 18, characterized in that the test system (110) still comprises at least one drilling element (140), in which the test system (110) is adapted to drill at least one portion of a user's skin by using the piercing element (140), whereby creating the body fluid sample, in which the test system (110) is further adapted to transfer the body fluid sample to the test field (162) of the test element (124).

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法律状态:
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优先权:

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EP12173121.0|2012-06-22|

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