Measurement of image quality of blood cell images

Abstract

By subjecting one or more images taken by a visual analysis system to a multi-layer analysis, such a visual analysis system may be automatically focused (or the focus of such a system may be automatically corrected). In such an analysis, cell boundaries may be identified in an input image based on the brightness values ​​of the pixels of the input image. Based on the identified cell boundaries, a predicted nominal focus value may be determined, which may give a focus distance (e.g., the distance between the focal plane of the camera used when the image was taken and the actual focal plane corresponding to the in-focus image). This focus distance may then be used to (re)focus the camera or for other purposes (e.g., generating an alert).

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from and is a formal application of Provisional Patent Application No. 63 / 305,890, entitled "Measure image quality of flow blood cell images," filed in the United States Patent Office on February 2, 2022, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Blood cell analysis is one of the most widely performed medical tests to provide an overview of a patient's health. A blood sample can be drawn from a patient's body and stored in a test tube that contains an anticoagulant to prevent clotting. A whole blood sample usually contains three major classes of blood cells, including red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Each class can be further divided into component subclasses. For example, the five major types or subclasses of white blood cells (WBCs) have different shapes and functions. White blood cells can include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. There are also subclasses of red blood cell types. The appearance of particles in a sample can vary depending on pathological conditions, maturity of the cells, and other causes. Subclasses of red blood cells can include reticulocytes and nucleated red blood cells.

[0003] This analysis may involve taking images of the sample, including blood cells; the higher the quality of these images, the more suitable the images are for analysis. However, taking high quality images presents many challenges. For example, ensuring that images are in focus can be difficult as changes in temperature or other factors related to the operation of the analyzer may require refocusing of previously focused optics. In addition, some types of focusing are not effective for all types of blood cells that may be found in the sample (e.g., focusing methods based on feature extraction may be suitable for red blood cells but not for white blood cells). Thus, there is a need for improved techniques related to detection of out-of-focus images and / or autofocusing of the analyzer's optics, including providing fast and reliable methods for assessing the quality of focus and / or for automatically refocusing when necessary. Summary of the Invention

[0004] Embodiments of the present disclosure may be used to determine a focal length of a camera based on an image representing one or more blood cells.

[0005] One embodiment may provide a system having a processor and an image capture device. Such a system may be configured to capture a plurality of cells using the image capture device, each of the plurality of images including at least one blood cell. Once captured, the system identifies a cell boundary in the at least one image. Based on the cell boundary, the system may generate a plurality of rings, each of the plurality of rings being offset from the cell boundary. Finally, the system may determine a predicted nominal focus value based on intensity values ​​of pixels located within the plurality of rings.

[0006] In a further embodiment, there may be a method in which a plurality of images are acquired from an image capture device. Each of the plurality of images includes an image of at least one blood cell. A cell boundary is then identified in at least one of the images. A plurality of rings are then generated based on the cell boundary, each of the plurality of rings being offset from the cell boundary. A predicted nominal focus value for the at least one image may then be determined based on intensity values ​​of pixels located within the plurality of rings. Other embodiments are also disclosed.

[0007] While the specification concludes with claims which particularly recite and distinctly claim the invention, it is believed the invention will be better understood from the following description of specific examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements, and in which: [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram, partially in cross section and not to scale, showing operational aspects of an exemplary flow cell, autofocus system and high optical resolution imaging device for analyzing sample images using digital image processing. [Diagram 2] FIG. 1 is a diagram of a slide-based vision inspection system, according to one embodiment. [Diagram 3] FIG. 2 illustrates a process that may be used to refocus an imaging device, according to one embodiment. [Figure 4] Figure 4A is an example image of a blood cell that is "positive" out of focus, Figure 4B is an example image of a blood cell that is in focus, and Figure 4C is an example image of a blood cell that is "negative" out of focus, according to one embodiment. [Diagram 5] 1 is a flowchart illustrating a method that may be used in an architecture associated with one embodiment. [Figure 6] 6A and 6B are diagrams illustrating the identification of approximate cell boundaries and the placement of rings on blood cell images, according to one embodiment. [Figure 7] 1 is a graphical representation of the average brightness value of a ring on a blood cell image, according to one embodiment. [Figure 8] 13 is a graphical representation of average brightness values ​​of rings on blood cell images of various focuses, according to one embodiment. [Figure 9] FIG. 13 is a detailed diagram of a graphical representation of the average brightness value of a ring on a blood cell image, according to one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be embodied in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings, which are incorporated in and form a part of this specification, illustrate several aspects of the invention and, together with the description, serve to explain the principles of the invention. It is understood, however, that the invention is not limited to the precise configurations shown.

[0010] The present disclosure relates to devices, systems, and methods for analyzing blood samples containing blood cells. In one embodiment, the disclosed technology may be used in the context of an automated imaging system that includes an analyzer, which may be, for example, a visual analyzer. In some embodiments, the visual analyzer may further include a processor that facilitates automated conversion and / or analysis of the image.

[0011] According to some aspects of the present disclosure, a system with a visual analyzer may be provided to obtain images of a sample containing particles (e.g., blood cells) suspended in a liquid. Such a system may be useful for characterizing particles in biological fluids, such as detecting and quantifying red blood cells, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including, for example, white blood cell differential counting, categorization and subcategorization, and analysis. Other similar applications are also contemplated, such as characterizing blood cells from other fluids.

[0012] Although discrimination of blood cells in a blood sample is an exemplary application for which the present subject matter is particularly suited, other types of bodily fluid samples may be used. For example, aspects of the disclosed technology may be used to analyze non-blood bodily fluid samples that contain blood cells (e.g., white blood cells and / or red blood cells), such as serum, bone marrow, lavage fluid, exudate, transudate, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid. It is also possible that the sample may be a solid tissue sample, such as a biopsy sample that has been processed to produce a cell suspension. The sample may also be a suspension obtained from processing a fecal sample. The sample may also be a laboratory sample or a manufacturing line sample that contains particles, such as a cell culture sample. The term sample may be used to refer to a sample obtained from a patient or laboratory, or a fraction, part, or aliquot thereof. The sample may be diluted, divided into several portions, or stained in some processes.

[0013] In some embodiments, the sample is automatically presented, imaged, and analyzed. In the case of blood samples, the sample may be significantly diluted with a suitable diluent or saline solution to reduce the degree to which some cells are obscured by other cells in the undiluted or less diluted sample. The cells may be treated with agents that enhance the contrast of some cellular aspects, such as using permeabilizing agents that permeabilize cell membranes and tissue stains that attach to and reveal features such as granules and nuclei. In some cases, it may be desirable to stain an aliquot of the sample for counting and characterizing particles including reticulocytes, nucleated red blood cells, and platelets, and for differentiation, characterization, and analysis of white blood cells. In other cases, samples containing red blood cells may be diluted prior to introduction into the flow cell and / or imaging in the flow cell or otherwise.

[0014] The details of sample preparation devices and methods for sample dilution, permeabilization and tissue staining may generally be accomplished using precision pumps and valves operated by one or more programmable controllers. Examples of these may be found in patents such as U.S. Pat. No. 7,319,907. Similarly, techniques for discriminating between specific cell categories and / or subcategories by cellular attributes such as relative size and color may be found in U.S. Pat. No. 5,436,978 in the context of white blood cells. The disclosures of these patents are incorporated herein by reference in their entirety.

[0015] Turning now to the drawings, Figure 1 shows a schematic of an exemplary flow cell 22 for conveying a sample fluid through a viewing zone 23 of a high optical resolution imaging device 24 in a configuration for imaging microscopic particles in a sample stream 32 using digital image processing. The flow cell 22 is coupled to a source 25 of sample fluid, which may have been treated, such as by contact with a particle contrast agent composition and heating. The flow cell 22 is also coupled to one or more sources 27 of particle and / or intracellular organelle alignment liquid (PIOAL), such as a clear glycerol solution having a higher viscosity than that of the sample fluid. One example of a PIOAL is disclosed in U.S. Patent Nos. 9,316,635 and 10,451,612, the disclosures of which are incorporated herein by reference in their entireties.

[0016] Once the PIOAL flow is substantially established, the sample fluid is injected into the interior of the flow cell 22 through a flat opening at the distal end 28 of the sample supply tube 29, resulting in a stable, symmetric laminar flow of the PIOAL above and below (or on either side of) the ribbon-like sample flow. The sample and PIOAL flows may be delivered by precision metering pumps that move the PIOAL along with the injected sample fluid along the significantly narrowing flow path. The PIOAL surrounds and compresses the sample fluid in the flow path narrowing zone 21. Thus, the reduction in flow path thickness in zone 21 may contribute to the geometric focusing of the sample flow 32. The sample flow 32 is surrounded and conveyed downstream of the narrowing zone 21 with the PIOAL, passing in front of or otherwise passing through the viewing zone 23 of the high optical resolution imaging device 24, where an image is collected using, for example, a CCD 48. The processor 18 may receive pixel data from the CCD 48 as input. The sample fluid ribbon flows with the PIOAL to the exhaust 33 .

[0017] As shown here, narrowing zone 21 can have a proximal flow path portion 21a having a proximal thickness PT and a distal flow path portion 21b having a distal thickness DT such that the distal thickness DT is less than the proximal thickness PT. Thus, sample fluid can be injected through distal end 28 of sample tube 29 at a location distal to proximal portion 21a and proximal to distal portion 21b. Thus, sample fluid can enter the enclosure of the PIOAL as the PIOAL flow is compressed by zone 21. Here, the sample fluid injection tube has a distal exit port through which sample fluid is injected into the flowing sheath fluid, the distal exit port being delimited by the reduced flow path size of the flow cell.

[0018] A digital high optical resolution imaging device 24 with an objective lens 46 is directed along an optical axis that intersects with the ribbon-like sample flow 32. The relative distance between the objective lens 46 and the flow cell 33 can be varied by operation of a motor drive 54 to resolve and collect a focused digital image on a photosensor array. Additional information regarding the construction and operation of an exemplary flow cell as shown in FIG. 1 is provided in U.S. Pat. No. 9,322,752, filed Mar. 17, 2014, entitled "Flowcell Systems and Methods for Particle Analysis in Blood Samples," the disclosure of which is incorporated herein by reference in its entirety.

[0019] Aspects of the disclosed technology may be applied in contexts other than a flow cell system as shown in FIG. 1. For example, FIG. 2 shows a slide-based visual inspection system 200 in which aspects of the disclosed technology may be used. In the system shown in FIG. 2, a slide 202 with a sample, such as a blood sample, is placed in a slide holder 204. The slide holder 204 may be adapted to hold several slides or only one slide as shown in FIG. 2. An image capture device 206 with an optical system 208 and an image sensor 210 is adapted to capture image data representative of the sample on the slide 202. Additionally, a light emitting device (not shown) may be used to control the light environment and thus obtain image data that is easier to analyze.

[0020] Image data captured by the image capture device 206 may be transferred to the image processing device 212. The image processing device 212 may be an external device, such as a personal computer, connected to the image capture device 206. Alternatively, the image processing device 212 may be incorporated into the image capture device 206. The image processing device 212 may include a processor 214 associated with a memory 216 configured to determine a change required to determine a difference between the actual focus and the correct focus of the image capture device 206. Upon determining the difference, an instruction may be sent to the steering motor system 218. The steering motor system 218 may change the distance z between the slide 202 and the optical system 208 based on the instruction from the image processing device 212.

[0021] In a system such as that shown in FIG. 1 or FIG. 2, components such as the motor drive 54 or steering motor system 218 may be used in a process such as that shown in FIG. 3 to continually refocus the imaging device (e.g., the digital high optical resolution imaging device 24 or image capture device 206) to maximize the quality of the images generated for later analysis. In a process such as that shown in FIG. 3, an image may be taken 301 and analyzed 302 to identify a misalignment between the expected focal plane (i.e., the plane on which the imaging device was focused when the image was taken) and the correct focal plane (i.e., the plane on which the imaging device needed to be focused to take a focused image). If the misalignment is zero (i.e., the plane on which the imaging device was focused when the image was taken was the correct focal plane), the process may continue taking images until all images required for analysis have been taken. Alternatively, if the misalignment is not zero (e.g., if there is a 2 μm gap between the expected focal plane and the correct focal plane), the focal plane of the imaging device may be adjusted 303 to account for the misalignment. For example, if the correct focal plane was 2 μm below the expected focal plane, components such as motor drive 54 or steering motor system 218 may be used to move the imaging device 2 μm closer to the sample, or the sample may be moved 2 μm closer to the imaging device (e.g., by moving a flow cell holding the sample) so that the expected and correct focal planes are the same for the next image. This process is then repeated until image capture is completed 304, thereby ensuring that deviations caused by factors such as temperature changes are detected and addressed.

[0022] As one skilled in the art will appreciate, a typical human white blood cell (WBC) can be considered as a sphere when recorded by the optical elements of a flow imaging device, and is captured as a two-dimensional (2D) circular cell image with internal structure. Alternatively, a typical human red blood cell (RBC) is a biconcave disk, with a thickness of 0.8-1 μm at the center of the disk and 2-2.5 μm at the edge, resulting in reduced brightness contrast between the center and the edge when viewed in correct focus. Thus, it is a challenging task to create a system that can image and analyze the different physical properties of both RBCs and WBCs.

[0023] One of the main obstacles in evaluating WBC images is that WBCs have various nuclei and granules at the cell boundaries, which can make the application of image pattern recognition difficult. With brief reference to Figures 4A-4C, when a WBC is captured in focus, the image contains relatively sharp cell boundaries and details inside the cell, as shown in Figure 4B. However, when the system captures out-of-focus images (e.g., Figures 4A and 4C), the boundaries may be blurred and the internal structure of the cell may be indistinguishable. Furthermore, since the system does not have a standard WBC template, the unknown nature of the internal structure complicates automated image analysis (e.g., determining whether the image is in focus or out of focus).

[0024] To overcome these problems, the systems and / or methods disclosed herein may rely on the interaction of the illumination device with the WBCs. For example, in some embodiments, the illumination device may generate various "halo" patterns around the cell boundaries as the quality of focus varies (see FIGS. 4A-4C). These "halo" patterns are independent of the internal structure of the WBCs, and thus, the "halo" patterns can be used during image processing (i.e., as discussed herein) to evaluate images of any / all WBC subtypes, e.g., neutrophils, monocytes, lymphocytes, etc. More specifically, in some embodiments, the "halo" patterns can be characterized by and analyzed using a ring-based feature extraction process. The feature extraction process may involve analyzing the image in a particular color space (e.g., a color space having brightness values).

[0025] For purposes of illustration only, this disclosure and most of the examples herein focus on HSV and / or HSL color spaces. However, it should be understood that these are non-limiting examples and that other color spaces, such as HSV, HSL, Munsell, LCh, NCS, CIELCH, etc., may also be used. uv , CIELC ab It should be understood that other color spaces may be used, such as RGB, CIECAM02, or current or future color spaces that evaluate lightness levels / values. Also, it should be understood that while the systems / methods discussed herein rely primarily on lightness levels, in some embodiments it may be possible to use color spaces that do not have a lightness factor. For example, in some embodiments the system may utilize / combine properties of the RGB color space to provide similar / equivalent information to lightness levels.

[0026] As discussed herein, the system captures images over time, such as using image capture device 24 of FIG. 1. If / when images are captured out of focus, the images are either "positively" or "negatively" out of focus, depending on whether the image capture device is too close or too far away. For example, FIG. 4A depicts a "positively" focused, i.e., out of focus image where the image capture device is too close to the cell compared to the ideal position, whereas FIG. 4C depicts a "negatively" focused, i.e., out of focus image where the image capture device is too far from the cell compared to the ideal position.

[0027] Referring now to FIG. 5, an exemplary process for measuring image quality of a flow blood cell image is shown. As mentioned above, before image analysis is performed, the image may be placed in a color space that facilitates extraction of brightness values. Thus, in some embodiments, the image capture device may be a specialized image capture device capable of capturing images in the correct color space 501. Additionally or alternatively, the system may convert the image captured by the image capture device to the correct color space 501 before analysis. Regardless of the method used, once the image is in the correct color space, the system may separate the foreground of the image from the background of the image 502, such as by evaluating brightness values ​​from the image (e.g., V in HSV or L in HSL). As discussed herein, it is possible to determine where the cells (i.e., foreground) end and the biological fluid (i.e., background) begin, even when the WBCs or RBCs are out of focus due to the "halo" effect (as shown in FIG. 4A, FIG. 4B, and FIG. 4C).

[0028] More specifically, to separate 502 the foreground from the background of an image, the system may evaluate each pixel of the captured image against a threshold brightness value (e.g., V=204). Referring briefly to FIG. 6A, in some embodiments, if a pixel is above the threshold, it is defined as foreground 602, and pixels below the threshold are defined as background 601. In some embodiments, the threshold may be product specific, or stated another way, two systems may have different thresholds based on factors of their respective designs. For example, two products may have different lighting devices or different image capture devices, and thus images captured by those systems may require different thresholds.

[0029] Thus, in some embodiments, the brightness value threshold may be determined / specified at product design or manufacturing time via machine learning / human training. In alternative embodiments, the system may determine the brightness threshold automatically (i.e., without human expertise) using one or more known image analysis techniques, such as by creating a histogram of brightness values ​​to determine the most accurate brightness value or range of brightness values ​​associated with the most significant brightness transitions.

[0030] In further embodiments, the system may start evaluating pixels from the center point of the image and then expand outward. Thus, since the image being analyzed should contain a single cell 501 (e.g., because it was captured as a single cell image or extracted from an image of multiple cells), starting the evaluation from the center may reduce the number of pixels that must be analyzed. In other words, once the system determines the boundary 603 of the entire cell, the system can proceed without having to analyze the outside of the rest of the image. It should be understood that various other edge detection or foreground segmentation methods may be used to separate 502 the two. In some embodiments, the interface or intersection of the background 601 and foreground 602 of the captured image is defined as the cell boundary 603. Thus, as discussed herein, the system may create and / or superimpose 503 a virtual cell boundary in the image based on the determined foreground and background.

[0031] Once the approximate cell boundary 603 is determined 503, multiple rings may be generated that are offset from the cell boundary 504. Thus, in some embodiments, and as shown in FIG. 6B, once the system determines the cell boundary 603 503, multiple rings (e.g., 610, 611, 612, 613, 614, and 615) may be generated 504. It should be understood that rings 610, 611, 612, 613, 614, and 615 are for illustrative purposes only and should not be considered the only rings identified / created by the system. Thus, while only six total rings are shown, in some alternative embodiments there may be many more rings (e.g., 7, 8, 9, 10, 50, 100, 1,000, 10,000, 100,000, etc.). In other embodiments, fewer rings may be used (e.g., 2-5 rings).

[0032] As shown in FIG. 6B, the system may utilize rings that are one, two, three, etc. levels smaller than the cell boundary (e.g., 613, 614, and 615, respectively) and rings that are one, two, three, etc. levels larger than the cell boundary (e.g., 612, 611, and 610, respectively). In some embodiments, the location and / or size of the rings are based on the approximate cell boundary 603. For example, one method of creating / identifying the one or more smaller rings is to apply morphological image erosion from the cell boundary. Alternatively, creating / identifying the one or more larger rings may involve applying morphological image dilation from the cell boundary. In yet another embodiment, the system may fit the cell boundary to an approximate ellipse. The approximate ellipse is then expanded and / or contracted to generate the rings. In further embodiments, the expansion and contraction of the approximate ellipse may be based on an offset distance or factor. For example, the approximate ellipse may be scaled based on units of measurement (eg, 1 μm, 0.1 μm, etc.) or pixel values ​​(eg, 1 pixel, 5 pixels, 10 pixels, etc.).

[0033] Returning to FIG. 5, once multiple rings are generated around the cell boundaries 504, the system then determines an average brightness value for each group of pixels between all adjacent rings 505. Specifically, the system determines an average brightness value of all pixels captured between the two rings 505. For example, all brightness values ​​of pixels between ring 610 and ring 611 are averaged 505 to determine a single average brightness value for the area between 610 / 611 (i.e., bin index 7 in FIG. 7). Similar calculations are performed for the areas between ring 611 and ring 612, between ring 612 and ring boundary 603, between ring boundary 603 and ring 513, between rings 613 and 614, and between rings 614 and 615.

[0034] Once all the average brightness values ​​for each pair of adjacent rings are calculated 505, the system identifies various image characteristics based on the average brightness values ​​506. In some embodiments, the system may plot the average brightness values ​​(e.g., in a Cartesian coordinate system) versus the bin index, as shown in FIG. 7. Although various plots / graphs are discussed and illustrated in this disclosure, it should be understood that they are for illustrative purposes only. Thus, as will be appreciated by those skilled in the art, in practice such plots / graphs may not be generated, but instead functions or other numerical representations relating index to brightness may be created and analyzed using the disclosed techniques. In other words, the disclosed systems and / or methods may or may not provide any kind of externally visible plots, such as those shown in FIGS. 7-8.

[0035] As a non-limiting example, FIG. 7 illustrates an embodiment in which the area inside the smallest circle (i.e., ring 615) is assigned a bin index value of 1 and a brightness value (i.e., V value) of approximately 0.5 is determined. The determined average brightness value is then put into a plot and associated with bin index 1 at 701. In a further embodiment, the remaining areas (e.g., areas between rings 611 and 612, ring 612 and ring boundary 603, ring boundary 603 and ring 613, rings 613 and 614, and rings 614 and 615) are added to the plot with their respective values ​​as shown. A brightness curve is created 710 by plotting the bin index on the X-axis and the corresponding brightness values ​​on the Y-axis. In an alternative embodiment, the system may plot the bin index on the Y-axis and the corresponding brightness values ​​on the X-axis. Additionally, in some embodiments, the values ​​on the X-axis may increase from the outside rather than from the inside.

[0036] 8, three cell images (i.e., 810, 820, and 830) are shown side-by-side with their respective graph plots (i.e., 811, 821, and 831). As shown, image 810 represents a positively defocused (i.e., "positively focused" where the image capture device is too close to the cell compared to the ideal position), image 820 represents a focused image, and image 830 represents a negatively defocused (i.e., "negatively focused" where the image capture device is too far from the cell compared to the ideal position). Thus, in some embodiments, when the brightness values ​​are plotted as a function of bin index as shown, a different brightness curve is created for each focus level.

[0037] Thus, in some embodiments, the system can analyze the function of each curve (e.g., 811, 821, and 831) to determine whether the image is out of focus, and if so, the degree and direction (i.e., positive or negative). Thus, the system can identify various image characteristics (e.g., inflection points, left marks, and right marks) 506 based on the function / curve of the average brightness value compared to the number of bin indexes. As a non-limiting example, FIG. 9 shows an expanded representation of plot 821 (i.e., a graphical plot of the in-focus image 820).

[0038] In some embodiments, as shown in FIG. 9, an inflection point 901 may be identified / determined as the point where the V value (i.e., brightness value) changes the fastest, which may be determined by taking the first derivative of the V curve. In a further embodiment, a left mark 902 may be identified / determined as the valley to the left of the inflection point. In an alternative embodiment, or if no valley is found to the left of the inflection point, the left mark may be calculated as the peak of the second derivative of the V curve to the left of the inflection point 901. In another embodiment, a right mark 903 may be identified / determined as the peak to the right of the inflection point. In an alternative embodiment, or if no peak is found to the right of the inflection point, the right mark may be calculated by taking the second derivative of the V curve to the right of the inflection point 901.

[0039] In some embodiments, once the inflection point 901, the left mark 902 and the right mark 903 are determined, the system may then calculate several numerical features. More specifically, the system may calculate the following: "Inflection_right_mark_distance", which is equal to the distance between the inflection point 901 and the right mark 902 on the V curve. b. "Left_right_mark_distance", which is equal to the distance between the left and right marks on the V curve. c."V_right_mark_value", which is equal to the V value of the right mark. d. "V_left_mark_value", which is equal to the V value of the left mark.

[0040] Once the characteristics are identified 506 and / or the numerical features are calculated, in some embodiments, the system may calculate a predicted nominal focus value 507. As a non-limiting example, if the determined nominal focus is equal to 0.0 μm, it may be assumed that the imaging device is in perfect focus (i.e., the system is in focus). Alternatively, when the quality of focus degrades and the nominal focus moves from 0.0 μm in either a positive or negative direction. The system may use the above four numerical features (i.e., inflection_right_mark_distance, left_right_mark_distance, V_right_mark_value, and V_left_mark_value) to correlate with the nominal focus and measure the quality of the focus. In other words, the system may predict the nominal focus using the above numerical features.

[0041] In some embodiments, the system may mathematically define a function with the numerical features as input and the predicted nominal focus as output in the following form: predicted nominal focus = f(inflection_right_mark_distance, lef_right_mark_distance, V_right_mark_value, V_left_mark_value). The specific process that such a function may use to provide the output may vary from case to case. For example, it may be defined using a neural network, a support vector machine, polynomial regression, linear regression, etc. As a non-limiting example, the system may use a second order polynomial regression defined as follows: Expected nominal focus = -6.8694e-01 * inflection_right_mark_distance + 7.1640e-02 * left_right_mark_ distance + -9.8570e+02 * V_right_mark_value + 1.6144e+01 * V_left_mark_value + 2.9647e-02 * inflection_right_mark_distance * inflection_right_mark_distance + -7.2789e-03 * left_right_mark_distance * left_right_mark_distance + 5.6250e+02 * V_right_mark_value * V_right_mark_value + -1.0997e+01 * V_left_mark_value * V_left_mark_value + 4.2827e+02 Other similar equations may also be used. For example, instead of using the exact values ​​shown in the example equation above, a second order polynomial may be expressed in the following form: F Predicted =LC IRMD * inflection_right_mark_distance + LC LRMD * left_right_mark_distance + LC VRMV* V_right_mark_value + LC VLMV * V_left_mark_value + SC IRMD * inflection_right_mark_distance 2 + SC LRMD * left_right_mark_distance 2 + SC VRMV * V_right_mark_value 2 + SC VLMV * V_left_mark_value 2 In the above exemplary formula, F Predicted is the expected nominal focus. LC IRMD is -6.8*10 -1 ~-6.9*10 -1 LC is a real number. LRMD is 7.1*10 -2 ~7.2*10 -2 LC is a real number. VRMV is -9.8*10 2 ~-9.9*10 2 LC is a real number. VLMV is 1.6*10 1 ~1.7*10 1 SC is a real number. IRMD is 2.9*10 -2 ~3.0*10 -2 SC is a real number. LRMD is 7.2*10 -3 ~7.3*10 -3 SC is a real number. VRMV is 5.6*10 2 ~5.7*10 2 is a real number, and SC VLMV is 4.2*10 2 ~4.3*10 2 Other mathematical expressions and classes of expressions may also be used, and thus the above mathematical expressions, whether expressed as ranges or as discrete values, should be understood to be illustrative only and should not be treated as limiting.

[0042] Whether from polynomial regression, neural networks, or otherwise, information that can be used as a basis for deriving a formula as shown above can be obtained in various ways. For example, ground truth images for training the system to generate predicted nominal focus values ​​can be obtained through human annotation of images generated during normal operation of the analyzer (e.g., a human inspects the images and then, based on his or her experience, labels the images with any differences between the actual and optimal focus planes, training the system to distinguish between in-focus and out-of-focus cell images), but can also be obtained in other ways. For example, the analyzer can be used to take images that are in focus and images that are out of focus by a known amount, by intentionally changing the relationship between the image and the sample being imaged after the in-focus image is taken.

[0043] Table 1 provides examples of how deliberate offsetting of the ideal focal length can be used as part of a training procedure. In various examples, the camera or camera lens is set to a first ideal focus position to capture in-focus blood cells. The camera or camera lens is then offset in either direction to establish a training set for the out-of-focus data. For example, the camera or camera lens may start at position X that correlates to the ideal focus quality position (e.g., offset zero). It may then be offset in both directions at fixed intervals (e.g., 0.1 micron, 0.2 micron, 0.3 micron, 0.4 micron, or 0.5 micron intervals), e.g., -1 to +1 micron in each direction, -2 to +2 microns in each direction, -3 to +3 microns in each direction, -4 to +4 microns in each direction, or -5 to +5 microns in each direction. In the context of Table 1, X indicates the starting position and n indicates the offset increment (e.g., 0.3 microns) that defines the fixed interval the camera is offset by for each sample run. Other approaches are possible, such as moving in variable increments, moving in different increments in different directions (e.g., 0.3 micron increments away from the flow cell and 0.2 micron increments closer to the flow cell), obtaining images from a different number of positions than those shown in Table 1 (e.g., 6n closer to the flow cell and 4n farther from the flow cell), etc. Different types of training data creation are also possible, such as providing a set of images to a human reviewer and asking the reviewer to specify the offset distance for each image. Thus, the description of how an intentional offset of the ideal focal length can be used as part of the training procedure should be understood to be merely illustrative and should not be treated as implying a limitation on the protection provided by this or related documents.

[0044] The training data may be used to establish a source of truth (e.g., either an image correlating to an ideal focus position or an image correlating to a degree of offset) as described above. The associated formula or algorithm used to establish the expected nominal focus is trained, whereby the parameters / function elements that make up the expected nominal focus formula or algorithm are refined over the training process to match the trusted source. In this manner, a final, confirmed expected nominal focus formula or algorithm is established.

[0045] In some examples, this training step is performed for each distinct grouping of blood cells, such as red blood cells in a first sample and white blood cells in another sample, so that the system is trained to identify focus quality from smaller cells (e.g., red blood cells) and larger cells (e.g., white blood cells). The different types of cells used to train the system may include red blood cells, platelets, and various groupings of white blood cells (neutrophils, lymphocytes, monocytes, eosinophils, and basophils). In other examples, the system is trained only on certain types of cells (e.g., only red blood cells, only white blood cells, or only certain types of white blood cells, such as only neutrophils).

[0046] [Table 1]

[0047] Thus, in some embodiments, a predicted nominal focus may be calculated for every single WBC image in a blood sample, and thus the system can measure the focus / image quality of every single WBC image. In further embodiments, the predicted nominal focus may then be used in downstream components. As a non-limiting example, the downstream component may be an image classification algorithm that can label the WBC images based on their biological properties. Furthermore, if the system determines that the focus / image quality is poor in a single image or across multiple images, the system may flag and / or invalidate the classification result. Indeed, the inventors contemplate that the focus assessment and / or autofocus as described herein may be used to improve image quality in any type of device that captures flow images of blood cells. It should also be understood that the disclosed systems and / or methods may utilize images taken of cells as they are presented through an imaging region (e.g., in a flow cell-based system, using an imaging device configured to take images of cells as they pass through an analysis region of a flow cell), where the cells may be one at a time or may include multiple cells presented and imaged in a single frame, and then a computational methodology is used to separate the cell images on a cell-by-cell basis. Thus, the above figures and their associated discussion should not be used to suggest a limitation on the scope of protection provided by this document or any other document that claims the benefit of or is otherwise related to this document.

[0048] In another embodiment, the predicted nominal focus may be utilized at the sample level. Additionally, statistics such as the mean, median, percentiles, and / or other averages of the predicted nominal focus of all or a portion of the WBC images may indicate whether a systematic shift in focus occurred during the imaging process. Additionally, the variability or scatter of the predicted nominal focus of all or a portion of the WBC images may provide information related to the stability of the flow during image acquisition. Thus, as described above, various metrics associated with an image or multiple images may be stored to enhance the usefulness of the images downstream.

[0049] In another embodiment, the system may automatically take proactive steps to correct the predicted nominal focus. For example, the system may perform an autofocus process, whereby the image capture device or other components are adjusted to improve the nominal focus. For example, the system may adjust the position of the image capture device using a mechanical part based on the predicted nominal focus. Thus, the predicted nominal focus value can serve as an indicator of the focusing quality. That is, when the absolute value of the nominal focus is closer to 0.0 μm, the focusing quality is better. In some embodiments, the sign of the predicted nominal focus value may indicate the direction of the system being out of focus.

[0050] The architecture as illustrated and discussed in the context of Figures 1-9 may be implemented in a variety of ways. For example, it is possible that such an architecture may be implemented as a convolutional neural network (CNN) that utilizes regression methods to better measure focus quality. In this type of implementation, the specific values ​​used to analyze the image and generate the output may be optimized by using blood cell images with known focus positions to train the neural network to minimize the regression error between the known focus positions and the calculated focus positions.

[0051] Variations in the method may be possible that may utilize focusing techniques as described herein. For example, an autofocus process as described herein may be implemented to run a series of samples to determine how the camera should be focused, and adjust the focus on a run-by-run basis rather than an image-by-image basis. Similarly, rather than automatically refocusing the camera, a certain focus position may be used to generate a warning (e.g., if the difference between the expected and correct focus plane exceeds a threshold or indicates a trend that the focus is wandering), after which point the user may decide whether to refocus the analyzer or continue the current imaging operation. Autofocus as described herein may also / alternatively be included in a periodic (e.g., routine) quality control process. Data collected with autofocus may then be used to improve the operation of the system. For example, if adjustments made during autofocus are found to be consistently unidirectional, it may be used as a diagnostic indicator that there is a system defect in the mechanical or optical components of the analyzer that, if corrected, would reduce the need for automatic refocusing. As another example of how autofocus as described herein may be applied, consider that in some cases, even if the focus is acceptable, different focus positions within the tolerance range may result in different features being more or less sharply perceivable in the image. In such cases, the focus information may be used to characterize the image taken by the system (e.g., as closer to or further from the sample while still being within the tolerance range) so that downstream processing may be optimized as necessary depending on which features are being detected (e.g., by applying a sharpening kernel where a particular feature may be more difficult to identify based on the characterization). Thus, the image-by-image autofocus described above should be understood as merely exemplary and should not be treated as implying any limitation on the protection provided by this or related documents.

[0052] Variations in how the focusing method as described herein may be implemented are also possible. For example, in some cases, the method as shown in FIG. 5 may include additional steps to facilitate processing and utilization of the image. An example of such additional steps may be pre-processing or reformatting the image before subjecting it to analysis. In an example where the processing is performed using a CNN trained to receive as input a particular type of image (e.g., a 128×128×3 RGB image), this may include resizing the image to match the training input and converting the image to the correct color space before subjecting the image to analysis. Thus, specific implementations may vary from case to case to suit the potential contexts and applications in which the disclosed technology may be used, and the examples, figures, and descriptions set forth herein should be understood as non-limiting.

[0053] As further illustration of possible implementations and applications of the disclosed technology, the following examples are provided as non-exhaustive ways in which the teachings herein may be combined or applied. It should be understood that the following examples are not intended to limit the scope of the claims that may be presented at any time during this application or in a later filing of this application. No disclaimer is intended. The following examples are provided for illustrative purposes only. It is contemplated that the various teachings herein may be configured and applied in numerous other ways. It is also contemplated that some variations may omit certain features mentioned in the following examples. Thus, unless specifically and expressly indicated as such at a later date by the inventors or their successors in interest, none of the aspects or features mentioned below should be considered essential. If a claim is presented in this application or a later application related to this application that includes additional features beyond those mentioned below, those additional features should not be considered added for reasons related to patentability.

[0054] Example 1 1. A system comprising: a processor; an image capturing device; and a non-transitory computer-readable medium having stored thereon instructions that cause the processor to perform a set of operations including: acquiring a plurality of images from the image capturing device, each of the plurality of images including a blood cell; identifying cell boundaries in at least one of the images; generating a plurality of rings based on the cell boundaries, each of the plurality of rings being offset from the cell boundaries; and determining a predicted nominal focus value for the at least one image based on intensity values ​​of pixels located within the plurality of rings.

[0055] Example 2 The system of example 1, wherein determining the predicted nominal focus value for the at least one image is further based on intensity values ​​of pixels located between each pair of adjacent rings of the plurality of rings.

[0056] Example 3 The system of Example 1, wherein acquiring the plurality of images further comprises converting each of the plurality of images into a color space having intensity values.

[0057] Example 4 The system of Example 1, wherein identifying cell boundaries in the at least one image further comprises separating the at least one image into foreground and background based on predetermined intensity values.

[0058] Example 5 The system of Example 1, wherein generating the plurality of rings further includes at least one of: generating at least one larger ring using morphological dilation of cell boundaries, where the additional larger ring is generated using morphological dilation on a previously generated larger ring; and generating at least one smaller ring using morphological erosion of cell boundaries, where the additional smaller ring is generated using morphological erosion on a previously generated smaller ring.

[0059] [Example 6] The system of Example 1, wherein generating the plurality of rings further includes at least one of: identifying a best fitting elliptical shape based on cell boundaries and generating at least one larger ring based on an offset distance, where the additional larger ring is generated from the previously generated larger ring based on the offset distance; and identifying a best fitting elliptical shape based on cell boundaries and generating at least one smaller ring based on an offset distance, where the additional smaller ring is generated from the previously generated smaller ring based on the offset distance.

[0060] Example 7 The system of Example 1, wherein determining a predicted nominal focus value for at least one image based on intensity values ​​of pixels disposed within the plurality of rings further includes identifying the plurality of characteristics based on the intensity values ​​by performing operations including: generating a V curve of average intensity values ​​associated with each area between two adjacent rings for a known distance of each of the plurality of rings from a cell boundary; identifying an inflection point of the V curve, the inflection point being equal to a peak of a first derivative of the V curve; identifying a left mark of the V curve, the left mark being equal to a peak of a second derivative of the V curve to the left of the inflection point; and identifying a right mark of the V curve, the right mark being equal to a valley of a second derivative of the V curve to the right of the inflection point.

[0061] Example 8 The system of Example 7, wherein determining a predicted nominal focus value for the at least one image further includes calculating a distance between the inflection point and the right mark, a distance between the left mark and the right mark, a V value for the right mark, and a V value for the left mark, wherein the predicted nominal focus is a function of the distance between the inflection point and the right mark, the distance between the left mark and the right mark, the V value for the right mark, and the V value for the left mark.

[0062] Example 9 The system of example 1, wherein the set of actions further comprises invalidating at least one image based on a predicted nominal focus value.

[0063] Example 10 The system of Example 1, wherein the set of operations further includes obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image among the plurality of images, determining a median value of the plurality of predicted nominal focus values, and translating the image capturing device based on the median value of the plurality of predicted nominal focus values.

[0064] Example 11 The system of Example 1, wherein the set of operations further includes obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image among the plurality of images, determining a median value of the plurality of predicted nominal focus values, and evaluating stability of the flow of the blood sample containing blood cells based on the median value of the plurality of predicted nominal focus values.

[0065] Example 12 1. A method comprising: acquiring a plurality of images from an image capturing device, each of the plurality of images including a blood cell; identifying a cell boundary in at least one of the images; generating a plurality of rings based on the cell boundary, each of the plurality of rings being offset from the cell boundary; and determining a predicted nominal focus value for the at least one image based on intensity values ​​of pixels located within the plurality of rings.

[0066] Example 13 The method of example 12, wherein determining the predicted nominal focus value for the at least one image is further based on intensity values ​​of pixels located between each pair of adjacent rings of the plurality of rings.

[0067] Example 14 The method of Example 12, wherein identifying cell boundaries in the at least one image further comprises separating the at least one image into foreground and background based on predetermined intensity values.

[0068] Example 15 The method of Example 12, wherein generating the plurality of rings further includes at least one of: generating at least one larger ring using morphological dilation of cell boundaries, where the additional larger ring is generated using morphological dilation on a previously generated larger ring; and generating at least one smaller ring using morphological erosion of cell boundaries, where the additional smaller ring is generated using morphological erosion on a previously generated smaller ring.

[0069] Example 16 The method of Example 12, wherein generating the plurality of rings further includes at least one of: identifying a best fitting elliptical shape based on cell boundaries and generating at least one larger ring based on an offset distance, where the additional larger ring is generated from the previously generated larger ring based on the offset distance; and identifying a best fitting elliptical shape based on cell boundaries and generating at least one smaller ring based on an offset distance, where the additional smaller ring is generated from the previously generated smaller ring based on the offset distance.

[0070] Example 17 13. The method of example 12, wherein determining a predicted nominal focus value for the at least one image further based on brightness values ​​of pixels disposed within the plurality of rings further includes identifying the plurality of characteristics based on the brightness values ​​by performing operations including: generating a V-curve of average brightness values ​​associated with each area between two adjacent rings for a known distance of each of the plurality of rings from a cell boundary; identifying an inflection point of the V-curve, the inflection point being equal to a peak of a first derivative of the V-curve; identifying a left mark of the V-curve, the left mark being equal to a peak of a second derivative of the V-curve to the left of the inflection point; and identifying a right mark of the V-curve, the right mark being equal to a valley of a second derivative of the V-curve to the right of the inflection point.

[0071] Example 18 The method of Example 17, wherein determining a predicted nominal focus value for the at least one image further includes calculating a distance between the inflection point and the right mark, a distance between the left mark and the right mark, a V value for the right mark, and a V value for the left mark, wherein the predicted nominal focus is a function of the distance between the inflection point and the right mark, the distance between the left mark and the right mark, the V value for the right mark, and the V value for the left mark.

[0072] Example 19 The method of example 12, further comprising invalidating at least one image based on a predicted nominal focus value.

[0073] Example 20 The method of Example 12, further comprising obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image among the plurality of images, determining a median value of the plurality of predicted nominal focus values, and translating the image capture device based on the median value of the plurality of predicted nominal focus values.

[0074] Example 21 The method of Example 12, further comprising: obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image among the plurality of images; determining a median value of the plurality of predicted nominal focus values; and evaluating stability of the flow of the blood sample containing blood cells based on the median value of the plurality of predicted nominal focus values.

[0075] Example 22 A machine comprising a camera and means for determining a focal length of the camera based on an image representing one or more blood cells.

[0076] Each of the calculations or operations described herein may be performed using a computer or other processor having hardware, software, and / or firmware. Various method steps may be performed by modules, which may include any of a variety of digital and / or analog data processing hardware and / or software configured to perform the method steps described herein. Modules optionally comprise data processing hardware adapted to perform one or more of those steps by having appropriate machine programming code associated with those steps, and modules for two or more steps (or portions of two or more steps) may be integrated on a single processor board or separated on different processor boards in any of a variety of integrated and / or distributed processing architectures. These methods and systems often employ tangible media embodying machine-readable code having instructions for performing the above method steps. Suitable tangible media may include memory (including volatile and / or non-volatile memory), storage media (such as magnetic recording on floppy disks, hard disks, tapes, etc., optical memory such as CDs, CD-R / Ws, CD-ROMs, DVDs, or any other digital or analog storage media, etc.), etc.

[0077] All patents, patent publications, patent applications, journal articles, books, technical references, and the like, discussed in this disclosure are hereby incorporated by reference in their entirety for all purposes.

[0078] Different configurations of the components shown in the figures or described above, as well as components and steps not shown or described, are possible. Similarly, some features and subcombinations are useful and may be employed without reference to other features and subcombinations. Although embodiments of the invention have been described for purposes of illustration and not limitation, alternative embodiments will become apparent to the reader of this patent. In some cases, method steps or operations may be performed or executed in a different order, or operations may be added, deleted, or modified. It will be understood that in certain aspects of the invention, a single component may be replaced with multiple components, and multiple components may be replaced with a single component, to provide an element or structure or to perform one or more given functions. Except where such a substitution would not be operative to implement a particular embodiment of the invention, such substitution is deemed to be within the scope of the invention. Thus, the claims should not be treated as limited to the examples, drawings, embodiments, and descriptions provided above, but should be understood to have the scope provided when the terms are given their broadest reasonable interpretation as provided by common dictionaries, and should be understood to have the meaning that they have when used in the claims, unless a term or phrase is indicated to have a specific meaning under the heading "Explicit Definition."

[0079] Explicit definition It should be understood that in the above examples and in the claims, the phrase "based on" something else should be understood to mean that it is at least partially determined by the thing it is shown to be based on. To indicate that something must be completely determined based on something else, it is said to be "based only on" the thing that is completely determined by it.

[0080] In the above examples and claims, the phrase "means for determining a focal length of the camera based on an image representing one or more blood cells" should be understood to be a means-plus-function limitation under 35 U.S.C. §112(f), where the function is "determining a focal length of the camera based on an image representing one or more blood cells," and the corresponding structure is a computer configured to use the algorithm as shown in FIGS. 5-9 and described in the accompanying description.

[0081] It should be understood that in the above examples and in the claims, the term "set" is to be understood as one or more items grouped together. [Explanation of symbols]

[0082] 18 Processors 21 Zone 21a Proximal flow path section 21b Distal flow channel portion 22 Flow Cell 23 Vision Zones 24 Imaging Device 25 Source of sample fluid 28 Distal end 29 Sample supply tube 32 Sample flow 33 Emission 46 Objective Lens 48 CCD 54 Motor Drive 200 Visual Inspection System 202 Slides 204 Slide Holder 206 Image capture device 208 Optical system 210 Image Sensor 212 Image Processing Device 214 processor 216 Memory 218 Steering Motor System 601 Background 602 Foreground 603 Cell Boundary 610, 611, 612, 613, 614, 615 Rings 810, 820, 830 Cell images 811, 821, 831 Graph plots, curves 901 Inflection Point 902 Left Mark 903 Right Mark

Claims

1. A processor; An image capture device; A non-transitory computer readable medium, comprising: acquiring a plurality of images from the image capture device, each of the plurality of images including blood cells; identifying cell boundaries in at least one image; generating a plurality of rings based on the cell boundaries, each of the plurality of rings being offset from the cell boundaries; determining a predicted nominal focus value for the at least one image based on intensity values ​​of pixels located within the plurality of rings; A non-transitory computer readable medium storing instructions for performing a set of operations including: A system comprising:

2. The system of claim 1 , wherein acquiring a plurality of images further comprises converting each of the plurality of images into a color space having intensity values.

3. The system of claim 1 , wherein identifying the cell boundaries in the at least one image further comprises separating the at least one image into foreground and background based on a predetermined brightness value.

4. generating the plurality of rings, generating at least one larger ring using morphological dilation of the cell boundaries, the additional larger ring being generated using morphological dilation on a previously generated larger ring; generating at least one smaller ring using morphological erosion of the cell boundaries, the additional smaller ring being generated using morphological erosion on a previously generated smaller ring; The system of claim 1 , further comprising at least one of:

5. generating the plurality of rings, identifying a best fitting elliptical shape based on the cell boundaries and generating at least one larger ring based on an offset distance, the additional larger ring being generated from a previously generated larger ring based on the offset distance; identifying a best fitting elliptical shape based on the cell boundaries and generating at least one smaller ring based on an offset distance, where the additional smaller ring is generated from a previously generated smaller ring based on the offset distance; The system of claim 1 , further comprising at least one of:

6. determining the predicted nominal focus value for the at least one image based on intensity values ​​of pixels located within the plurality of rings; generating a V-curve of mean intensity values ​​associated with each area between two adjacent rings for a known distance of each of the plurality of rings from the cell boundary; identifying an inflection point of a V-curve, said inflection point being equal to a peak of a first derivative of said V-curve; identifying a left mark on the V-curve, the left mark being equal to a peak of a second derivative of the V-curve to the left of the inflection point; identifying a right mark on the V-curve, the right mark being equal to a valley of the second derivative of the V-curve to the right of the inflection point; The system of claim 1 , further comprising: determining a plurality of characteristics based on the lightness value by performing operations including:

7. 7. The system of claim 6, wherein determining the predicted nominal focus value for the at least one image further comprises calculating a distance between the inflection point and the right mark, a distance between the left mark and the right mark, a V value for the right mark, and a V value for the left mark, wherein the predicted nominal focus is a function of the distance between the inflection point and the right mark, the distance between the left mark and the right mark, the V value for the right mark, and the V value for the left mark.

8. The system of claim 1 , wherein the set of actions further comprises invalidating the at least one image based on the predicted nominal focus value.

9. The set of operations is obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image of the plurality of images; determining a median of the plurality of predicted nominal focus values; translating the image capture device based on the median value of the plurality of predicted nominal focus values; The system of claim 1 further comprising:

10. The set of operations is obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image of the plurality of images; determining a median of the plurality of predicted nominal focus values; assessing flow stability of the blood sample containing blood cells based on the median value of the plurality of predicted nominal focus values; The system of claim 1 further comprising:

11. acquiring a plurality of images from an image capture device, each of the plurality of images including blood cells; identifying cell boundaries in at least one image; generating a plurality of rings based on the cell boundaries, each of the plurality of rings being offset from the cell boundaries; determining a predicted nominal focus value for the at least one image based on intensity values ​​of pixels located within the plurality of rings; The method includes:

12. The method of claim 11 , wherein identifying the cell boundaries in the at least one image further comprises separating the at least one image into foreground and background based on a predetermined brightness value.

13. generating the plurality of rings, generating at least one larger ring using morphological dilation of the cell boundaries, the additional larger ring being generated using morphological dilation on a previously generated larger ring; generating at least one smaller ring using morphological erosion of the cell boundaries, the additional smaller ring being generated using morphological erosion on a previously generated smaller ring; The method of claim 11 , further comprising at least one of:

14. generating the plurality of rings, identifying a best fitting elliptical shape based on the cell boundaries and generating at least one larger ring based on an offset distance, the additional larger ring being generated from a previously generated larger ring based on the offset distance; identifying a best fitting elliptical shape based on the cell boundaries and generating at least one smaller ring based on an offset distance, where the additional smaller ring is generated from a previously generated smaller ring based on the offset distance; The method of claim 11 , further comprising at least one of:

15. determining the predicted nominal focus value for the at least one image further based on intensity values ​​of pixels located within the plurality of rings; generating a V-curve of mean intensity values ​​associated with each area between two adjacent rings for a known distance of each of the plurality of rings from the cell boundary; identifying an inflection point of a V-curve, said inflection point being equal to a peak of a first derivative of said V-curve; identifying a left mark on the V-curve, the left mark being equal to a peak of a second derivative of the V-curve to the left of the inflection point; identifying a right mark on the V-curve, the right mark being equal to a valley of the second derivative of the V-curve to the right of the inflection point; The method of claim 11 , further comprising determining a plurality of characteristics based on the brightness values ​​by performing operations including:

16. 17. The method of claim 16, wherein determining the predicted nominal focus value for the at least one image further comprises calculating a distance between the inflection point and the right mark, a distance between the left mark and the right mark, a V value for the right mark, and a V value for the left mark, wherein the predicted nominal focus is a function of the distance between the inflection point and the right mark, the distance between the left mark and the right mark, the V value for the right mark, and the V value for the left mark.

17. The method of claim 11 , further comprising invalidating the at least one image based on the predicted nominal focus value.

18. obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image of the plurality of images; determining a median of the plurality of predicted nominal focus values; translating the image capture device based on the median value of the plurality of predicted nominal focus values; The method of claim 11 further comprising:

19. obtaining a plurality of predicted nominal focus values, each nominal focus value corresponding to a different image of the plurality of images; determining a median of the plurality of predicted nominal focus values; assessing flow stability of the blood sample containing blood cells based on the median value of the plurality of predicted nominal focus values; The method of claim 11 further comprising:

20. A camera and and means for determining a focal length of the camera based on an image representing one or more blood cells.

Images