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    • 专利摘要:
    SYSTEM AND METHOD FOR ACQUISITION OF IMAGE USING SUPERVISED SYNTHESIS OF HIGH QUALITY IMAGES. It is a capture system and method for image synthesis of biological samples disposed in a culture medium supported by a plate. The system has a calibration module, an image acquisition module and an image presentation module. When the system receives a culture plate for image synthesis, standard values for the plate and the culture medium are used to start image acquisition at a given time. The captured image is then used to create a pixel by pixel map of the image. The system inspects the pixel-by-pixel map for saturated pixels and the signal-to-noise ratio, and obtains a new image if the number of saturated pixels is at or above a predetermined threshold or the signal l-noise ratio for the pixel is below a predetermined threshold. From this inspection, a new photon flow value and / or exposure time is determined and a new image is captured using the new value, and the steps are repeated. When determining that (...).
    公开号:BR112016017321B1
    申请号:R112016017321-0
    申请日:2015-01-30
    公开日:2021-03-16
    发明作者:Raphael R. Marcelpoil；Cedrick Orny；Didier Morel
    申请人:Bd Kiestra B.V.；
    IPC主号:
    专利说明:

    CROSS REFERENCE TO RELATED ORDER
    [001] This application claims the benefit of the filing date of Provisional U.S. Application No. 61 / 933,426, filed on January 30, 2014, the disclosure of which is hereby incorporated by reference. BACKGROUND OF THE INVENTION
    [002] High Dynamic Range Imaging (HDR) is a digital imaging technique that captures a greater dynamic range between the lightest and darkest areas of an image. A process for automatically optimizing a dynamic range of pixel intensity acquired from a digital image is described in US Patent No. 7,978,258 to Christiansen et al. HDR takes several images at different exposure levels and uses an algorithm to stitch them together to create an image that has both dark and light spots, without compromising the quality of any one of them. However, HDR can present a distortion of reality, as it distorts the intensity of the image in general. Therefore, the search continues for HDR techniques that enhance contrast without distorting the intensity of the image.
    [003] Techniques for enhancing an image of a biological sample are described in WO 2012/152769 for Allano et al. Among the problems related to the imaging of such samples identified in Allano et al. they are: i) the size of the colonies being visualized; ii) the proximity of one colony to another; iii) the color mix of the colonies; iv) the nature of the Petri dish; and v) the nature of the culture medium; as well as other factors.
    [004] The proposed solution by Allano et al. for the problem of generating an image of a biological sample, it consists of preparing an original image created from the images acquired in each color, removing predetermined absorption effects for the culture medium and the culture vessel and determining a value for the flow of photons and the exposure time using a predetermined exposure to acquire an image that is then dissected in zones of brightness. From this, the brightness of the image is acquired and used to determine whether the value for the photon flow and the exposure time used was correct or whether a new value for the photon flow and the exposure time should be used for the image capture.
    [005] The problem with previous techniques is that they do not offer a system with the ability to provide imaging conditions that can detect very subtle changes in contrast that are necessary for the detection / identification of microbes based on images in growth medium. Since the evidence based on images of the microbes and / or their growth in the medium is (or at least can be) difficult to detect, more robust techniques are sought for the imaging of such samples. BRIEF SUMMARY OF THE INVENTION
    [006] Here is described a system and method that improve image capture for images with low contrast or variable contrast. An example of such a challenging imaging environment is that of bacterial colonies growing on agar growth plates. Bacterial colonies reflect light differently than agar. In addition, bacterial colonies can vary from light to dark colors and reflect light differently from agar. The time to capture an image of a colony is short (approximately one second). Typically, an image of the growth plate is acquired every 3 to 6 hours.
    [007] An image is acquired in a series of N image acquisitions in each “x” time interval (ie, t0, t1... Tx). The first acquisition (N = 1) uses standard values for light intensity and exposure time, here called "photon flow and exposure time". The photon flow value defines the number of photons arriving on the scene by time unit and area unit ((number of photons) • (time-1) • (area-1)). The time being the time of integration in the camera sensor. The exposure time determines the number of photons captured by the sensor for the acquisition of a frame. Put another way, the photon flux is the rate of photon flow from the light source and the exposure time influences the amount of photons received by the sensor for the image acquisition. a given flow of photons, the exposure time controls the intensity of the image.
    [008] An individual skilled in the art is aware of the many different ways of controlling the flow of photons to influence the intensity of the image. As noted above, a technique controls the exposure time of the image. There are other techniques that can be used to control the intensity of the light transmitted to the sensor. For example, filters, vents, etc. are used to control the flow of photons, which in turn controls the intensity. Such techniques are well known to those skilled in the art and are not described in detail here. For the purposes of the embodiments of the invention described here, the light intensity is defined as constant and the exposure time is the variable used to control the integration of the photon flow.
    [009] In embodiments in which the photon flow is controlled by controlling the exposure time, the initial exposure time values are acquired from the calibration of the system. The system is calibrated using a library of calibration plates. Baseline calibration is acquired depending on the type of plate and the type of medium. When the system is used to interrogate new growth plates, the calibration data for a particular plate type and media type is selected. In this regard, the growth plates can be: monoplates (that is, for a medium); biplates (two media); triplets (three media), etc. Each type of growth plate presents unique imaging challenges. Calibration provides a standard exposure time to capture the first image (image N = 1) of the growth plate. Calibration also makes it possible for the system (or system operator) to determine which parts of the image are plates (ie, not the background) and, of the plate parts of the image, which parts are the medium (the nutrients used to grow the colonies), and which parts are, at least potentially, colonies.
    [010] The N = 1 image of a growth plate is captured using the standard values acquired from the calibration. If an averaging technique is used to capture digital images from the growth plate, the light pixels will have a better signal-to-noise ratio (SNR) than that of the dark pixels. In the method described here, the signals are isolated for the individual pixels, regardless of whether the pixels are light or dark. For a predetermined number of pixels, the intensity, the exposure time and the SNR are determined. A “map” of these values in the context of the image is prepared. From this map, a new exposure time that preferably will not saturate more than a predetermined fraction of pixels is selected for the acquisition of N + 1 image. Preferably, an exposure time in which only a very small fraction of pixels (or less) are saturated is determined and used to capture the final image.
    [011] From this, a map of the SNR for each pixel where the SNR is updated (that is, the gray value is refined and the improved SNR for the unsaturated pixels) for each unsaturated pixel is generated. An image is simulated based on this map.
    [012] An optimization function algorithm is used to map each gray value intensity for each pixel to the corresponding necessary exposure time for the optimal SNR for the pixel. The optimization algorithm starts by looking at the initial image (N = 1), which was captured using the predetermined standard exposure time. An intensity, exposure and SNR map is generated for the entire image. The exposure time for each pixel is adjusted based on image N and another image (N + 1) is captured. As stated above, the new exposure time is chosen, which will saturate the signals from the dark parts, resulting in the overexposure of the light parts. The intensity map, the exposure map and the SNR map are updated for each pixel. This is an iterative process, and images are acquired until the maximum SNR for each pixel for the image is reached, or the maximum number of images is reached, or the maximum allocated time has been reached.
    [013] Essentially, the dark points remain dark, the light points remain light and the SNR is improved. The agar growth medium acts as the background for digital images. A pixel in the image that is different in some way (that is, a different intensity) from the previous images indicates either that the colony is growing or that there is contamination (for example, dust) on the plate. This technique can be used to look at multiple plates at once.
    [014] Since the SNR is significantly improved, it is possible to reveal details (with confidence) that could not be seen / trusted, allowing the detection of small colonies very early in the timed plate imaging. The systems and methods also provide images corresponding to an ideal exposure time that corresponds to the specific and controlled saturation on the scene or object of interest.
    [015] Once the image acquisition at time t0 is complete, the iterative image acquisition process is interrupted for that time interval. When the predetermined time interval from t0 to t1 has elapsed, the iterative image acquisition process is repeated until the desired confidence in the integrity of the image thus acquired has been acquired. The signal-to-noise ratio is inversely proportional to the standard deviation (ie, SNR = gv ’/ standard deviation). Therefore, an image acquisition that produces a maximum SNR per pixel (that is, a minimum standard deviation per pixel) will provide an image with a high confidence associated with a "Tx" time. For example, a high SNR image is acquired for a plate that has been incubated for four hours (T1 = 4 hours). Another high SNR image of the same plate is acquired after the plate has been incubated for an additional four hours (Tx = 8 hours).
    [016] Once an image associated with a subsequent time (Tx + 1) is acquired, that image (or at least the selected pixels of the image associated with an object of interest) can be compared with the image associated with the previous time ( Tx) to determine whether the subsequent image provides evidence of microbial growth and to determine further processing of the plate. BRIEF DESCRIPTION OF THE DRAWINGS
    [017] FIG. 1 is a schematic description of a three module system for image acquisition and presentation in accordance with an embodiment of the present invention;
    [018] FIG. 2 is a flow chart of the system operation for the three module system illustrated in FIG. 1;
    [019] FIG. 3 is a description of the functions of the calibration module illustrated in FIG. 1 for lighting calibration, optical calibration and camera calibration according to an embodiment of the present invention;
    [020] FIG. 4 is an illustration of the data determined from the calibration plates to calibrate the system of FIG. 1 according to one modality;
    [021] FIG. 5 is a description of the functions of the image acquisition module illustrated in FIG. 1 according to an embodiment of the present invention;
    [022] FIG. 6 is a schematic diagram of the image acquisition method using the system of FIG. 1 according to one modality;
    [023] FIG. 7 is a more detailed description of the functions performed by the image acquisition module illustrated in FIG. 5.
    [024] FIG. 8 illustrates the method for choosing the next image acquisition time according to a modality;
    [025] FIG. 9 is a description of the steps taken to complete the image acquisition; and
    [026] FIG. 10 is a schematic diagram of the process flow of how to determine the integrity of the system. DETAILED DESCRIPTION
    [027] The system described here is capable of being implemented in optical systems for the imaging of microbiological samples for the identification of microbes and the detection of microbial growth of such microbes. There are many commercially available systems of this type, which are not described in detail here. An example is the BD Kiestra® ReadA Compact imaging and intelligent incubation system (2nd generation BD Kiestra® incubator). Such optical imaging platforms have been commercially available for many years (originally the CamerA PrimerA from Kiestra® Lab Automation), and are therefore well known to those skilled in the art and are not described in detail here. In one embodiment, the system is a non-temporary, computer-readable medium (for example, a software program) that cooperates with an image acquisition device (for example, a camera), which offers high-quality imaging of an image through interaction to provide a maximum signal-to-noise ratio (SNR) for each pixel in the image. For each pixel and each color (for example, channel), the intensity and the exposure time are recorded and the system then predicts the next best exposure time to improve the SNR of the entire scene or the objects of interest in the scene. A person skilled in the art will appreciate that the multiple values acquired per pixel will depend on the pixels and the imaging system. For example, in an RBG imaging system, values are acquired for each channel (that is, red, green or blue). In other systems, values are acquired for different spectral bands or wavelengths.
    [028] Initially, the system is calibrated. Calibration of imaging systems, such as that described here, are well known to those skilled in the art. A variety of calibration approaches are known. Examples of system calibration that provide a baseline from which captured images are evaluated are described here. During calibration, calibration plates (for example, plates with medium, but without colonies) are used and the image acquisition of the system is calibrated based on the known input. A library of calibration values for each type of plate medium is created, and the calibration data used for a particular plate is selected based on the medium on the test plate. Both the system and the data are calibrated. For data calibration, SNR, Linearity, Black Level, etc. are determined. for each pixel of the image captured from the calibration plate. The calibration system includes, but is not limited to, lens distortion, chromatic aberrations, spatial resolution, etc.
    [029] After calibration, images of the new plates are acquired. The pixels in the image are analyzed in real time in order to estimate the exposure time that will improve the SNR of the pixels with an SNR that is below a predetermined threshold or for those pixels with the lowest SNR. Typical imaging systems only retain intensity values for the pixels in the image. In the embodiments described here, the intensity and the exposure time are recorded for each pixel. The same pixel is synthesized at different exposure times and the intensity information is combined to generate high SNR data. From that information, an image can be generated for any specified exposure time, or the best exposure time can be extracted to control pixel saturation.
    [030] From a quantitative aspect, thanks to the high SNR, confidence in subtle variations in intensity, colors and texture is greatly improved, allowing for better performance of subsequent object recognition or database comparison . The analysis is done on a gray scale compared to the gray value of the pixel in a previous image (that is, for the N image, the pixel value in the N-1 image). In addition to comparing the same pixel gray value in the previous image, the pixel gray value of the adjacent pixels is also compared with the pixel gray value to determine the differences (for example, the colony / medium interface).
    [031] The SNR of dark or colored objects is irregular in the different channels or very weak when compared to that of light objects. In order to improve this, the system and method described here implement an image detection module in which object detection is based on contrast, SNR, and size / resolution. SNR is perfected in both dark and light regions. The standard deviation is decreased, and therefore the local contrast becomes as significant in the light as it is in the dark. The goal here is to offer a system that detects even subtle differences between the x and x + 1 time interval images of a plate suspected of containing a growing culture. These differences should be distinguishable from the "noise" that results from signal variations, but not from changes in the sample that can be attributed to a growing culture. The systems and methods described here are especially valuable when the objects of interest in the scene may present very different colors and intensities (reflectance or absorbency).
    [032] Specifically, the system and method provide automatic adaptation of the dynamic range (extended dynamic range) to accommodate the scene. The system and method provide both the minimum exposure time to saturate the lightest pixel and the maximum exposure time to saturate the darkest pixel (within the physical and electronic restrictions of the image acquisition equipment (for example, the camera)) . The system and method provide faster convergence for a minimum SNR per pixel compared to the image average calculation. The system and method provide enhanced color confidence. Specifically, the SNR for the values of red, green and blue is homogenized, regardless of the disparities in intensity in the colors red, green and blue.
    [033] Intensity confidence intervals are known per pixel, which is very valuable for any subsequent classification effort. The SNR optimization offered by the system and method can be supervised (weighting of the detected objects of interest to calculate the exposure times of the next image acquisition).
    [034] The intensity, the exposure time and the estimated SNR are determined from the calibration and the physical theory for each pixel. To further improve image quality, chromatic aberration and lens distortion are also calibrated and corrected to make an image free from such defects.
    [035] The system and method can control the pixel SNR for the image in either an automatic or a supervised mode, in which certain parts of the image are of particular interest. In automatic mode, the entire image of the scene is optimized, and all pixels are treated equally. In supervised mode, the scene is further analyzed when acquired to detect the objects of interest. The maximization of the SNR favors the objects of the regions of interest.
    [036] In automatic mode, image acquisition will stop after the first of the following three conditions occurs: (1) a minimum SNR level is reached for each and every pixel; (2) a predetermined number of acquisitions were made in this scene; or (3) the maximum allowable acquisition time has been reached.
    [037] Referring to FIG. 1, a schematic view of the system of an embodiment is illustrated. System 100 has three modules. The first is a system 110 calibration module. The calibration module calibrates the image illumination, the optics used to collect the image, and the baseline data for the new plate under evaluation by the system.
    [038] The image acquisition module 120 is in communication with the system calibration module 110. The image acquisition module captures an image of the object under analysis. The image is captured using the exposure time and other criteria determined in a manner described in detail here later in the context of the specific examples. As discussed above, image acquisition proceeds iteratively until a predetermined SNR threshold is reached for each pixel or until a predetermined number of images has been captured. The image display module provides the image with the best dynamic range (that is, the lightest unsaturated pixels that are just below saturation), either globally (that is, in automatic mode) or restricted to objects of interest ( that is, in supervised mode).
    [039] Referring to FIG. 2, both external data and calibration plates (that is, the variety of combinations of test plates and culture media) are used to calibrate the system). From the calibration, both the system calibration and the data calibration are determined. The system and data calibration values are used when image acquisition for a new card. Calibration is used to validate the new image in terms of the image map (that is, which pixels are regions outside the plate, which are inside the plate, but media without colonies and which regions reveal colonies).
    [040] FIG. 3 additionally illustrates the specific aspects of the system equipment that are calibrated. For the lighting component (s) 111, the heating time, the intensity (À) = f (input power) and field homogeneity are determined. Again, for the test plates, the medium should be homogeneous for the applicable region (that is, the entire plate for a single plate, half of the plate for a biplane and a third of a plate for a triple plate). For optical calibration 112, alignment, chromatic aberrations and geometric distortions are determined. For camera 113 calibration, baseline levels are determined. Such baseline data are: warm-up time; linearity (fixed ratio of gray values and number of photos that reach the sensor) and black level as a function of exposure time, SNR as a function of pixel intensity; field homogeneity; chromatic aberrations; and geometric distortions, all being determined as a baseline against which the acquired image is evaluated. Such baseline data is well known to those skilled in the art and is not described in further detail.
    [041] FIG. 4 shows additional details about the entries in the calibration systems (ie system information, the library of calibration plates and other entries). For each calibration plate, an image is acquired and each pixel is assigned values for black level, SNR, linearity and lighting. For the system (that is, not pixel by pixel), model values that reflect system factors, such as distortion, chromatic aberrations, spatial resolutions and white balance are determined. All of these values are collected to provide a calibrated system and calibrated data for use in plate evaluation. As noted below, these values are used to finalize image acquisition.
    [042] More details on the image acquisition module are described in FIG. 5. In the first step, an image is acquired using default values. From that first image, the intensity, the exposure time and the SNR for each pixel are determined. The intensity is determined by subtracting the “black level” for the pixel from a measured intensity value. The black level and the SNR are acquired from the calibration described previously.
    [043] Image acquisition occurs in times to, ti, ... tx. Each time, an image is acquired through a series of N image acquisitions. The N series of image acquisitions iterates over an SNR to the acquired image that correlates with high confidence in image integrity.
    [044] Image acquisition at a given time (for example, to) and update is illustrated in FIG. 6. The image of a new 6io plate is acquired in step 62o. Image acquisition is informed by the calibration of the 63o system and 64o data. Plate traffic conditions (that is, number of plates per unit of time) are also used to calibrate and control the system. At a later point in time during the image acquisition process, a subsequent image is acquired 65o and compared with the previous image (both automatically and in a supervised manner). Typically, there will be approximately four to approximately ten image acquisitions in each time frame to acquire an image with acceptable confidence. Once the desired SNR for the selected object is acquired, the exposure time is determined for the acquisition of the final image 66 °.
    [045] According to one modality, the pixels are updated as follows. The gray value, the reference exposure time and the signal-to-noise ratio represent the information stored for each lighting configuration (top, side, bottom, or a mixture of them) per plate (image object). This information is updated after each new purchase. To begin with, this information is updated using the first image acquisition (N = i).
    [046] The gray value, the reference exposure time and the signal-to-noise ratio represent the information stored for each lighting configuration (top, side, bottom, or a mixture of them) per plate. This information is updated after each new purchase. To begin with, this information is initialized according to the first image acquisition (N = 1). In one mode, i is a gray value (gv) at the image position (x, y) corresponding to the 1st image capture (N = 1) of the plate using the exposure time and the respective Signal-to-Noise Ratio (- ■ '“:; - •). In this mode: • 5. is the black reference point at (x, y) corresponding to the exposure time; • ^ * is the reference time point updated at (x, y) after 1 acquisition; • ^ .. is the gray value updated at x, y after 1 acquisition in the equivalent exposure time ~. i; •>-'- .. - .1 is the SNR updated at x, y after 1 acquisition;

    [047] The black level is noisy and the iterative image acquisition process obtains an image that is "less noisy" (that is, an image with a higher level of confidence). The black value is a standard value that is not recalculated during image acquisition. The black value is a function of the exposure time.
    [048] SNR = 0 when a pixel is saturating for a given exposure time (therefore, no improvement in SNR) and light source intensity. Only the values of the unsaturated pixels are updated.
    [049] N = 1: The initial exposure time is the best known standard exposure time (a priori), or an arbitrary value (for example: (Max exposure time + Min exposure time) / 2). This is determined from the calibration for the plate and medium in particular for the new plate under analysis.
    [050] The gray value, the reference exposure time and the signal-to-noise ratio are updated after each new image acquisition (ie, N = 2, 3, 4.. N) according to the following modality . The gray value s'- for the image position (x, y) corresponds to the umpteenth image capture of the plate using the exposure time and the respective Signal-to-Noise Ratio (J - ■. ■). In this mode: • it is the black reference point at (x, y) corresponding to the exposure time; ': ■ :; • ^ is the reference time point updated at (x, y) after N acquisitions; • is the gray value updated in (x, y) after N acquisitions in the equivalent exposure time - ■. ■; and •>-'- is the SNR updated at x, y after N acquisitions.

    [051] Therefore, the updated SNR for a pixel in the umpteenth image acquisition is the square root of the updated signal-to-noise ratio squared of the current image acquisition. Each acquisition provides an updated value (for example, E’x, y, N) for each pixel. This updated value is then used to calculate the updated value for the next image acquisition. SNR = 0 for a pixel when a pixel is saturating for a given exposure time and intensity of the light source. Only unsaturated pixels are ‘updated. The umpteenth exposure time corresponds to a supervised optimization, whose objective is to maximize the SNR for the objects of interest. The object of interest can be the entire plate, the colonies, a part of the plate or the entire image.
    [052] After updating the image data with a new acquisition, the acquisition system is able to propose the next best acquisition time that would maximize SNR according to environmental restrictions (minimum required SNR, saturation restrictions, acquisition time maximum allowed, region of interest). In embodiments in which the image acquisition is supervised: x, y AND the object implies that, in the supervised mode, the pixels of the object are only considered for evaluations. In those embodiments where image acquisition is not supervised, the standard object is the entire image.
    [053] With reference to FIG. 7, from the analysis of the acquired image, the exposure time for the next image (N + 1) in the image acquisition series in a given time interval is determined using either the automatic mode or the supervised mode described above. Referring to FIG. 7, for the automated process, each pixel is weighted equally (that is, a value of 1 is designated). For the supervised approach, pixels associated with objects (for example, cultures) are weighted differently. The supervised process requires additional imaging steps. If a significant fraction (for example, greater than 1 in 100,000) of pixels is saturating and their weights are not equal to 0, then a new exposure time is proposed that is shorter (for example, 1/5) than the previous minimum exposure time used to capture the image. This adjustment improves the probability of acquiring unsaturated information for the saturated pixels. In alternative embodiments, a new exposure time is calculated. If there is no significant pixel saturation, then, for each pixel, from the exposure and intensity map, the maximum exposure time that will not result in pixel saturation is determined. From this, an exposure time for the image is determined, and an image of intensity is simulated. From there, the corresponding weighted SNR map is determined.
    [054] Referring to FIG. 8, the sample image is used to update the image data, pixel by pixel, on the image map. This sample data is then fed to the image analyzer and the image analysis is performed informed by predetermined restrictions in the SNR for each pixel, other saturation restrictions, object restrictions, etc. and time or traffic restrictions (ie, duration of capture and analysis).
    [055] In a specific modality, the acquired image is analyzed pixel by pixel in search of saturated pixels. If EN results in a pixel saturation that exceeds predetermined limits, a lower value for EN + 1 is selected. For example, the minimum exposure time has not been acquired yet and the percentage of saturated pixels (* v xy, NE. (Xv V ■ '' = çr.jat) exceeds the predetermined limit (for example> 1/105) a new exposure time is proposed in a predetermined increment (for example, one fifth of the minimum exposure time previously used. The lower limit (ie the minimum acceptable exposure time) is also predetermined. These restrictions on exposure time allow for convergence faster for image acquisition conditions without saturation.
    [056] A new image is acquired in the new exposure time. For the new image, the secondary checked restrictions are the desired minimum SNR per pixel (this is the lower SNR threshold) and the overall acquisition time (or Nmax) allowed for that image. If the overall acquisition time for this scene has reached the time limit, or if each updated SNR for each pixel is such that s then the image data is considered acceptable and the scene acquisition ends for the time interval (for example , t0). When image acquisition starts at time tx (for example, t1), the best exposure time (ENfinal) leading to under-saturation conditions from the previous acquisition exposure (for example, at time t0) is used as the initial value for E. The process for image acquisition in tx is, in other cases, identical to the process in time to.
    [057] If the saturation constraint is lifted (no significant saturation), the next optimal exposure time is determined and investigated. First, the exposure time limits are calculated on the region of interest. These exposure time limits are: I) the exposure time to saturate the lightest pixels; and ii) the exposure time to saturate the darkest pixels.
    [058] The exposure time to saturate the lighter unsaturated pixels,, is determined from the gray value that corresponds to the absolute maximum intensity and Es- „, (its related exposure time) from the following:

    [059] The exposure time to saturate the lighter unsaturated pixels,, is determined from the gray value that corresponds to the absolute maximum intensity and (its related exposure time) from the following:

    [060] The next ideal exposure time is chosen from among all candidate exposure times within Emax and Emin by simulation. Specifically, an exposure time is determined by simulation that will maximize the updated average SNR (for all pixels below the minimum signal-to-noise ratio threshold), after adding the simulated image at the tested exposure time ^ = -.- :, .-i. The simulated image is generated as follows (for each and every pixel). gv 't
    [061] The gray value "• -. Is pixel data corresponding to the current updated image data. If a new E∑es∑xi time point is selected, the expected gray value is:

    [062] After updating this heat with a value for the pixel from the simulated image in the image at the time point, the SNR for this pixel (x, y) will be:

    [063] The next best exposure time is then determined by:
    If image acquisition and analysis is supervised x, y and object, the SNR is integrated only for the objects of interest. ' In automatic mode, the object is the entire yacht.
    [064] FIG. 9 describes the final steps for image acquisition. These steps are conventional image processing techniques well known to those skilled in the art and are not described in detail here.
    [065] FIG. 10 illustrates the method by which the integrity of the systems is determined during image acquisition. Note that once the integrity of the system is verified, samples are loaded into the system and sample data is captured. The data capture is informed by the calibration information, as discussed above. The captured data is provided for both the system integrity check and a system event analyzer.
    [066] Once the image has been acquired as described above, it is compared to an image of the plate that has been incubated for a different period of time. For example, an image of a plate is acquired as described here after the plate has been incubated for four hours (T1 = 4). After four or more hours, another image of the plate is acquired as described above (Tx = 8 hrs). The high SNR image acquired at Tx + 1 can then be compared with the high SNR image at tX. The changes in the two images are evaluated to ascertain evidence of microbial growth. Decisions about further processing (for example, the plate is positive, the plate is negative, the plate requires additional incubation) are based on this comparison.
    [067] Although the invention presented here has been described with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the present invention. Therefore, it should be understood that several modifications can be made to the illustrative modalities and that other configurations can be designed without departing from the spirit and scope of the present invention, as defined by the appended claims.
    权利要求:
    Claims (14)
    [0001]
    1. System for imaging biological samples disposed in culture medium, the system FEATURED for comprising: a system calibration module that provides standard values for capturing an image of a biological sample disposed in culture medium disposed on a plate; an image acquisition module comprising a camera, in which the image acquisition module is adapted to acquire data for a series of images in a given time interval, in communication with the system calibration module, the configured image acquisition to: i) acquire data for a first image using standard values for photon flow and exposure time from the system calibration module and create a pixel by pixel map of the image data, each pixel associated with a signal-to-noise ratio (SNR), a photon flow and exposure time, and an intensity, ii) update the image acquisition time by analyzing image data to identify saturated pixels and select one from a new photon flow, a new one exposure time, or both, based on whether a ratio of saturated to unsaturated pixels is greater or less than a predetermined saturation threshold and, based on this determination, iii) use the new value for the flow d and photons, the exposure time, or both, to acquire data for a new image, and iv) update the image data map with the new values for the signal-to-noise ratio, the photon flow, the exposure time, or both, and the pixel intensity, at which the image acquisition module is configured to acquire data for a new image if the signal-to-noise ratio of the unsaturated pixels is less than a predetermined SNR threshold or if the number of pixels saturates exceed the predetermined saturation threshold, in which the image acquisition module finalizes the image acquisition when the signal-to-noise ratio of unsaturated pixels satisfies or exceeds the predetermined SNR threshold, a predetermined allocated time for image acquisition has elapsed, or a predetermined maximum number of images has been acquired; and an image presentation module that transforms the image data from the image acquisition module into an image for viewing or analysis.
    [0002]
    2. System, according to claim 1, CHARACTERIZED by the fact that the photon flow is a defined value and the system controls a camera sensor integration by controlling the exposure time, in which the calibration module determines flow of standard photons and exposure time for a variety of plates and culture media.
    [0003]
    3. System, according to claim 1, CHARACTERIZED by the fact that the signal-to-noise ratio is determined for at least part of the sample image arranged in the culture medium.
    [0004]
    4. System, according to claim 1, CHARACTERIZED by the fact that the image acquisition module acquires image data from the camera for at least one or more channels or one or more spectral bands.
    [0005]
    5. System, according to claim 1, CHARACTERIZED by the fact that the image acquisition module assigns a gray value for each pixel for each image acquisition, and the gray value for each pixel is updated after each image acquisition. Image.
    [0006]
    6. System, according to claim 5, CHARACTERIZED by the fact that the updated gray value is the previous gray value minus a predetermined reference value, where the predetermined reference value is a predetermined value based on the plate, the plate medium and the exposure time for the image acquisition module assign a gray value for each pixel for each acquisition, and the gray value for each pixel is updated after each image acquisition.
    [0007]
    7. System, according to claim 2, CHARACTERIZED by the fact that the new value for the photon flow is acquired using a new exposure time or a new light intensity value, or both.
    [0008]
    8. System, according to claim 1, CHARACTERIZED by the fact that the image acquisition module is configured to operate in at least one among an automatic mode where all pixels are treated equally or a supervised mode, where the analyzed pixels are those that have been identified as associated with one or more objects of interest.
    [0009]
    9. System, according to claim 1, CHARACTERIZED by the fact that the photon flow is a defined value and the system controls a camera sensor integration by controlling the exposure time.
    [0010]
    10. Method for imaging biological samples disposed in culture medium, the CHARACTERIZED method for understanding: determining standard values to acquire an image of a biological sample disposed in culture medium supported on a plate; acquire data corresponding to a series of images in a first time during a first time interval, the data of the first image in the series being acquired using predetermined patterns for photon flow and exposure time; create a pixel by pixel map of the image data; associate data for each pixel with a signal-to-noise ratio (SNR), a value for photon flow, exposure time, and an intensity; update at least one of the image photon flow value and the exposure time value by: i) analyzing the image data for saturated pixels and the signal-to-noise ratio for the pixels, and selecting a new value for at least one of the photon flux and exposure time based on whether a ratio of saturated to unsaturated pixels is greater or less than a predetermined threshold and whether the signal-to-noise ratio of unsaturated pixels meets or exceeds a predetermined SNR threshold and, based on this determination, use the new value for photon flow, exposure time, or both, to acquire a new image and update the image map with the new values for signal-to-noise ratio, photon flow and time of exposure, and pixel intensity, ii) acquire data for the new image using at least one new value for photon flow and exposure time, and iii) optionally, repeat steps i) and ii); finalize the acquisition of image data for the time interval when the image data is at or above the predetermined SNR threshold, a predetermined maximum allocated time for image acquisition has elapsed, or a predetermined maximum number of images has been acquired; repeat the steps of acquiring, creating, associating, updating and finalizing for a second time during a second time interval; and transform the image data acquired in the first and second time intervals into the first and second images, the first image acquired in the first time and the second image acquired in a second time.
    [0011]
    11. Method, according to claim 10, CHARACTERIZED by the fact that the value for photon flow is constant and the exposure time value updated, and in which the predetermined standard values are based on plate, plate culture and time exposure.
    [0012]
    12. Method, according to claim 10, CHARACTERIZED by additionally comprising determining the pixels for which the image map will be created, in which the pixels are associated with an object of interest.
    [0013]
    13. Method, according to claim 12, CHARACTERIZED by the fact that the standard values comprise a black level for the pixels associated with an object of interest at a standard exposure time.
    [0014]
    14. Method, according to claim 10, CHARACTERIZED by the fact that the pixel by pixel map is a gray value, the signal-to-noise ratio and the exposure time for each pixel.
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    法律状态:
    2020-05-26| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-01-26| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-02-23| B09W| Correction of the decision to grant [chapter 9.1.4 patent gazette]|Free format text: RETIFIQUE-SE, POR ERRO MATERIAL DA REIVINDICACAO 1 DO QUADRO ANTERIOR |
    2021-03-16| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 30/01/2015, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US201461933426P| true| 2014-01-30|2014-01-30|
    US61/933,426|2014-01-30|
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