Lensless imaging particle detection device

Abstract

Lensless Imaging Particle Detection Device A lensless imaging particle detection device includes a memory (4) arranged to receive a plurality of z-images obtained from lensless imaging of a biological sample, a preparer (6) arranged, from said z-images, to determine a focus image in which each pixel is associated on the one hand with a z-image and on the other hand with the intensity of that pixel in that z-image, the preparer (6) being arranged to determine the z-image for a given pixel by calculating, for each of the z-images and for the given pixel, a sparsity measure from the intensity of the given pixel and the intensities of neighboring pixels, and by selecting the z-image whose sparsity measure is the highest, a selector (10) arranged to determine, for each pixel of the focus image,if this pixel is a maximum in a local neighborhood centered on this pixel in the focus image or in the z-image associated with this pixel in the focus image, and, if so, to store this pixel in a list of selected sites, and a calculator (12) arranged to calculate a metric value for each pixel in the list of selected sites based on the intensity of these pixels to enable the production of an image of metric values ​​enabling the distinction of the particles associated with each pixel in the list of selected sites from each other.

Description

Description Title of the invention: Device for detecting particles in lensless imaging

[0001] — The invention relates to the field of sample image processing, in particular of biological samples, by lensless imaging.

[0002] = Lensless imaging involves placing a sample between a light source and an image sensor without an optical magnification device between the sample and the image sensor. The image sensor thus collects an image of the light intensity transmitted by the sample, also called hologram, which is formed of figures interference between on the one hand the light wave emitted by the light source and transmitted by the sample, and on the other hand diffraction waves resulting from the dif- fraction by the sample of the light wave emitted by the light source.

[0003] These interference patterns have been the subject of research in order to reconstruct an image allowing the information they contain on the observed sample to be exploited. This technique, due to its material simplicity linked to the absence of optics, is considered as particularly promising, and has been the subject of numerous developments in the field of hematology.

[0004] — Counting red blood cells (hereinafter "RBC") is possible within the framework of the Platelet Rich Plasma (hereinafter “PRP”) control, because the number of RBCs is suf- extremely weak so as not to mask other particles. In the case of whole blood, it it is necessary to lyse them, and it is then possible to count only the populations platelets (hereinafter "PLT") and white blood cells (hereinafter "WBC"), with the difficulty additional to have a background noise from lysed RBCs.

[0005] — The patents published under numbers FR 3 034 196 and FR 3 049 348 teach a calculation of statistics from detected cells, in particular from z-profiles, for counting and differentiating GB. Unlike the state of the art, these methods require a prerequisite to reconstruct the image from the image of diffraction with a precise method, suitable for preserving the local information of the quasi-point particles and not to leave any major artifacts. For example, the patent FR 3 049 347 teaches such a reconstruction method.

[0006] To date, only the GB-PLT method described in patent FR 3 082 943 makes it possible to count PLTs on PRP and in lysed blood (with different settings), and work additional information would be necessary to know how to discriminate GR (in PRP) from others particles. This method is based on the detection of particles in a stack of images reconstructed around a mean plane; the detection planes are then merged to establish a detection map. This map is directly used for the particle counting. In the case where several types of particles are present, it is appropriate to adapt this general strategy to each type of particle. On each object (connected component) of the detection mask thus calculated, metrics are calculated in order to determine the nature of the objects or their number. This last point constitutes an extremely penalizing limitation: in the case of an image of a large number of particles, many particle multiplets are present (statistically) and the method does not address this problem in the case where several types of particles are present. Finally, attempts to use this method on lysed blood have been unsuccessful. Another method was developed and is based on a deep neural network (DNN) type model. The learning was done on an augmented sample base to have enough inputs, taking into account the counts given by a reference machine. The performances were very good, they are limited by the assumption specific to this type of method: having enough training data with a sufficiently reliable reference. Data acquisition is a long and expensive process, and this is the limiting factor for the application of DNN-based technologies. In addition, learning is strongly influenced by the reference machine, which is problematic given the variability of measuring machines. In addition, since learning is carried out on images with noise, it therefore induces learning of the noise of lysis and not only of counting and classification.Finally, this learning allows for counting, but not classification of particles because there is no constructible reference. No solution therefore offers full satisfaction for PLT counting in the context of lens-free whole blood imaging. The invention improves the situation. To this end, it proposes a device for detecting particles in lensless imaging, comprising a memory arranged to receive a plurality of z-images obtained from lensless imaging of a biological sample, a preparer arranged, from said z-images, to determine a focus image in which each pixel is associated on the one hand with a z-image and on the other hand with the intensity of this pixel in this z-image, the preparer being arranged to determine the z-image for a given pixel by calculating, for each of the z-images and for the given pixel, a sparsity measure from the intensity of the given pixel and the intensities of neighboring pixels, and by selecting the z-image whose sparsity measure is the greatest, a selector arranged to determine, for each pixel of the focus image,whether this pixel is a maximum in a local neighborhood centered on this pixel in the focus image or in the z-image associated with this pixel in the focus image, and, if so, to store this pixel in a list, of selected sites, and a calculator arranged to calculate a metric value for each pixel in the list of selected sites based on the intensity of those pixels to enable the production of an image of metric values ​​for distinguishing particles associated with each pixel in the list of selected sites from each other. This device is particularly advantageous because it allows for reliable PLT counting and can be integrated into a laboratory device. According to various embodiments, the invention may have one or more of the following characteristics: -the calculator is arranged to implement a metric being chosen from a metric based on the modeling of the intensities of a neighborhood of each pixel of the list of selected sites in the z-image associated with this pixel in the focus image by a Gaussian, by combining one or more of the covariance, the height and the eccentricity factor of this Gaussian, and a metric based on the average of the z-gradients of the intensity of the pixels of a neighborhood centered on each pixel of the list of selected sites, which gradient is evaluated for the z-image corresponding to each pixel of the list of selected sites in the focus image, - the preparer is arranged to use a sparsity measure based on the mean pq where the intensities are modulated by a Gaussian window centered on the given pixel, - the preparer is arranged to use an average pq with p equal to 1 / 2 and q equal to 3, a square neighborhood of 17 pixels, and a Gaussian with a standard deviation equal to 2.5, - the selector is arranged to browse the list of selected sites, and, when two pixels are immediately adjacent, to keep the one whose associated intensity in the focus image is the greatest, and - the device further comprises a filter arranged to recalculate the focus image by recalculating the sparsity measure for each given pixel by restricting the neighborhood of pixels to pixels whose associated z-image in the focus image is identical to that associated with the given pixel or immediately neighboring in z. The invention also relates to a method for detecting particles in lensless imaging, comprising the following operations: a) receiving a plurality of z-images obtained from lensless imaging of a biological sample, b) determining a focus image in which each pixel is associated on the one hand with an image in z and on the other hand with the intensity of this pixel in this image in z, this determination being carried out, for a given pixel, b1) by calculating, for each of the images in z and for the given pixel, a measure of sparsity from the intensity of the given pixel and the intensities of neighboring pixels, and b2) by selecting the z-image with the largest sparsity measure, (c) determining, for each pixel in the focus image, whether that pixel is a maximum in a local neighborhood centered on that pixel in the focus image or in the z-image associated with that pixel in the focus image, and, if so, to store that pixel in a list of selected sites, and d) calculating a metric value for each pixel in the list of selected sites based on the intensity of those pixels to enable the production of an image of metric values ​​enabling the particles associated with each pixel in the list of selected sites to be distinguished from each other. According to various embodiments, the invention may have one or more of the following characteristics: - in operation d), the metric is chosen from a metric based on the modeling of the intensities of a neighborhood of each pixel of the list of selected sites in the z-image associated with this pixel in the focus image by a Gaussian, by combining one or more of the covariance, the height and the eccentricity factor of this Gaussian, and a metric based on the average of the z-gradients of the intensity of the pixels of a neighborhood centered on each pixel of the list of selected sites, which gradient is evaluated for the z-image corresponding to each pixel of the list of selected sites in the focus image, - operation b1) uses a sparsity measure based on the mean pq where the intensities are modulated by a Gaussian window centered on the given pixel, - operation bl) uses as a measure of parsimony the mean pq with p equal to 1 / 2 and q equal to 3, a square neighborhood of 17 pixels, and a Gaussian with a standard deviation equal to 2.5, - operation c) includes traversing the list of selected sites, and, when two pixels are immediately adjacent, to keep the one whose associated intensity in the focus image is the greatest, and - operation b) further comprises b3) recalculating the focus image by recalculating the sparsity measure for each given pixel by restricting the pixel neighborhood to pixels whose associated z-image in the focus image is identical to that associated with the given pixel or immediately neighboring in z. The invention also relates to a computer program comprising instructions for executing the method according to the invention, a data storage medium on which such a computer program is recorded and a computer system comprising a processor coupled to a memory, the memory having recorded such a computer program. Other characteristics and advantages of the invention will appear more clearly on reading the following description, taken from examples given for illustrative and non-limiting purposes, taken from the drawings in which: - [Fig.1] represents a generic diagram of a device according to the invention, - [Fig.2] represents an example of an operating loop of the device of [Fig.1], - [Fig.3] represents an example of implementation of a function implemented in a first operation of [Fig.2], - [Fig.4] represents an example of implementation of a function implemented in a second operation of [Fig.2], - [Fig.5] represents an example of implementation of a function implemented in a third operation of [Fig.2], - [Fig.6] represents an image without a lens, and z-images derived from it, - [Fig.7] represents a focus image obtained by the function of [Fig.3], - [Fig.8] represents a focus image obtained by the function of [Fig.4], - [Fig.9] represents a focus image obtained by the function of [Fig.5], - [Fig.10] represents an image of first metric values ​​obtained from an image obtained by the function of [Fig.5], and - [Fig.11] represents an image of second metric values ​​obtained from an image obtained by the function of [Fig.5]. The drawings and the description below contain, for the most part, elements of a certain character. They may therefore not only serve to better understand the present invention, but also contribute to its definition, if necessary. This description may contain elements that are subject to copyright protection. The rights holder has no objection to anyone reproducing this patent document or its description in the same form as it appears in the official files. He reserves his rights in full for all other purposes. [Fig. 1] represents a generic diagram of a device 2 according to the invention. The device 2 comprises a memory 4, a preparer 6, a filter 8, a selector 10 and a calculator 12. As will be seen below, the filter 8 is optional. The memory 4 receives the data that are the subject of the functions and calculations implemented by the device 2. The device 2 mainly processes reconstructed images whose focus is taken at various depths, obtained from lensless imaging. The patent published under number FR 3 082 944 describes an example of implementation of this type of images. More precisely, this patent describes how, from lensless imaging, a plurality of images that will be called below “z-images” or “z-focus images” are reconstructed and correspond to a focusing of the lensless imaging at a particular depth. [Fig.6] shows an acquisition image from lensless imaging, and modulus and phase images that are reconstructed from the acquisition image. As will be seen below, images that combine the modulus and the phase are used as z-images in the context of the invention. Alternatively, only the phase image or only the modulus image could be used when it carries the information of interest. The difficulty in processing these images lies in the fact that the platelets constitute elements whose size is of the order of a pixel, whose number is very high, all in an image whose background is very noisy due to the lysis of the RBCs, which makes it impossible to differentiate with the naked eye between the PLTs on the one hand and the RBC debris on the other. The data of the z images include on the one hand pixels identified by their coordinates, and on the other hand an intensity value associated with these pixels. In the example described here, the images have a resolution of 3840x2748 pixels, and the intensity is modified: the z image is processed in the form |I-1| instead of [I because the image without particles after reconstruction must theoretically have a constant value of 1. Thus, by working on [I-1 we consider the amplitude of the disturbance induced locally by the diffraction. Memory 4 can be any type of data storage suitable for receiving digital data: hard disk, flash memory hard disk, flash memory in any form, RAM, magnetic disk, locally or cloud distributed storage, etc. In the example described here, the memory 4 receives all the data described above, and more generally all the data which concerns the device 2, that is to say the programs and software instantiating the preparer 6, the filter 8, the selector 10 and the calculator 12, their parameters, the data received at input, the data at output, as well as the data stored in buffer memory. The data calculated by the device can be stored on any type of memory similar to the memory 4, or on it. This data can be erased after the device has carried out its tasks or retained. The preparer 6, the filter 8, the selector 10 and the calculator 12 directly or indirectly access the memory 4. They can be implemented in the form of appropriate computer code executed on one or more processors. By processors, it is meant any processor suitable for the calculations described below. Such a processor can be implemented in any known manner, in the form of a microprocessor for a personal computer, laptop, tablet or smartphone, a dedicated chip of the FPGA or SoC type, a computing resource on a grid or in the cloud, a cluster of graphics processing units (GPUs), a microcontroller, or any other form suitable for providing the computing power necessary for the implementation described below. One or more of these elements can also be implemented in the form of specialized electronic circuits such as an ASIC. A combination of processor and circuits electronics can also be considered. Processors dedicated to machine learning could also be considered. The preparer 6, the filter 8, the selector 10 and the calculator 12 are presented here separately because they perform distinct functions. This modular description aims to better understand the functional blocks implemented by the device 2. It goes without saying that two or more of these elements could nevertheless be grouped together as long as the functional relationships remain comparable. [Fig.2] represents an example of an operating loop of the device of [Fig.1] allowing a better understanding of the respective functions of the preparer 6, the filter 8, the selector 10 and the calculator 12. The loop begins in an operation 200 in which the preparer 6 executes a Focus() function. The Focus() function accesses the z-images from the same lensless imaging, and returns an image called a focus image, in which for each pixel are associated on the one hand a z-image index and on the other hand the intensity of this pixel in this z-image. The purpose of the Focus() function is to identify, for each pixel, what is the best focus depth, in order to find the most relevant plane to deduce the nature of each pixel. This function will be described further with [Fig.3]. [Fig.7] shows an example of the focus image obtained. Then, the focus image is reprocessed in an optional operation 210 in which filter 8 executes a Refine() function. As will be seen below, the Focus() function uses a sparsity measure coupled with a Gaussian window, which allows for a good quality focus image while preserving a certain regularity, otherwise the pixels of the same cell are not necessarily at the same focus. However, in the context of platelet detection this regularity comes at a price. Indeed, due to the large number of cells present in the chamber, the distance between two cells can be very small, and a cell can easily be in the neighboring window and disrupt the sparsity measure. The Gaussian window reduces this phenomenon, but if two cells are a few pixels apart, the more “intense” one will take precedence over the other and the less “intense” one will probably have a suboptimal focus.The Refine() function aims to solve this problem by refining the focus image from the focus planes found in operation 200, and by only considering pixels in identical or very close planes for the sparsity measurement of each pixel. This function will be described further with [Fig.4]. [Fig.8] shows an example of a refined focus image. Selector 10 then uses the focus image—possibly refined—to choose the pixels that form likely platelet sites. To do this, selector 10 executes a Selec() function in an operation 220. This function favors a pixel detection and not by connected component, in order to be able to best handle multiplet cases as simply as possible. The important thing is not to "forget" any sites, and to consider the objects to be searched for as single pixels, in order to relegate the operation of distinguishing between object pixels (platelets) and background pixels to the final operation. This function will be described further with [Fig.5]. [Fig.9] shows an example of the resulting image of sites. Finally, in an operation 230, the calculator 12 uses the list of sites determined by the selector 10, and executes a Class() function in which metrics are calculated in order to produce an image making it possible to distinguish between platelet pixels and background pixels. This operation therefore also makes it possible to count the PLTs and estimate their concentration. Figures 10 and 11 show classification images obtained for two distinct metrics. Figure 3 represents an example of implementation of the Focus() function executed by the preparer 6. As explained above, the purpose of this function is, from the z images, to determine for each pixel the z focus plane that is most suitable for it. To this end, the Applicant has discovered that a sparsity measure applied to a neighborhood of points of each pixel makes it possible to obtain unparalleled results. The sparsity measure has the advantage of being adapted to pixel objects because it seeks to maximize the level of a single pixel to the detriment of all the others. Several sparsity measures exist, and the Focus() function described here uses a modified version of the pq-mean measure (or pq mean). The pq mean has good theoretical properties while being fairly simple to calculate, which makes it easy to embed in a machine.Thus, in the example described here, the measure used has the following formula: Pars(X) =1- l GX ; where X re- . CORNE ES ex“ presents a neighborhood comprising all the intensities around a given pixel, G is a Gaussian window which is applied to the intensities of the pixels in the neighborhood, * denotes the convolution product, and p and q are the parameters of the sparsity measure, with O <p<I<a. In the example described here, the Applicant has identified that p=1 / 2 and q=3 give the best results. Other combinations nevertheless offer good results, such as p=1 / 3 and q=4 or p=1 / 2 and q=4. In the case of pixel searches, the dimension of the neighborhood was determined here from the Gaussian. Thus, starting from a standard deviation of 2.5, for a Gaussian covering 3 sigmas — which represents the classic approach — we obtain a neighborhood of 17 pixels (3 times 2.5 rounded to 8, times 2 plus the central pixel). Other Gaussian window dimensions could be retained. Thus, in a first operation 300, a first current pixel cp is initialized with the first pixel cp0 in the coordinates of the images in z, then in a operation 310, a Wind() function retrieves all neighbors in a 17-pixel square window centered on pixel cp and stores their intensities in a variable W. The variable W receives the neighborhoods for all z-images, so that all z-images are processed at once. Alternatively, the z-images could be processed sequentially or in parallel, and the focus choice would then be made after all these calculations. Then, the sparsity measure is calculated by a Pars() function in an operation 320. The result is stored in an array CP[] which therefore receives, for each focus depth z corresponding to an image in z, on the one hand the sparsity measure of pixel cp in the image in z considered, and on the other hand by the intensity of pixel cp in the image in z considered. The preparer 6 then executes a Max() function in an operation 330. The Max() function iterates through the CP[] array and returns the (z:I) cut (where I is the intensity stored in the CP[] array for the relevant z-image) for which the sparsity measure is the highest. Indeed, the z-image for which the sparsity measure is the highest is the image in which this pixel has the best focus. Alternatively, it would be possible to determine the z corresponding to the maximum intensity by interpolating the curve of values ​​I, and, if necessary, interpolate intensity values ​​in this plane for the rest of the calculations. In the latter case, this new plane can be introduced into the z-image data, or only a neighborhood of the point considered can be retained, this neighborhood being described below. The triplet (cp;z;1) is then stored in an array FocImg[] in an operation 340. As it appears, the array FocImg[] contains all the information of the focus image and is progressively built. In the following, the array FocImg[] can designate the focus image. Finally, the next pixel is retrieved in a 350 operation, and the Focus() function resumes with the 310 operation, unless all pixels have been processed, in which case it ends in a 399 operation. In the above, the function Focus() processes the pixels sequentially. It goes without saying that a parallelized version of this processing is possible. Thus, the focus image FocImg[] has a frame 8 pixels thick in which no z is identified since all the measurements are zero. This has the advantage of limiting the risks of edge effects. At the end of the processing by the device 2, a frame 35 pixels thick is removed during the counting. This nevertheless remains negligible compared to the dimensions of the images considered (10 MP). The Applicant has also identified that the average pq is not the only measure of sparsity that gives satisfactory results. Thus, the Gini measure (or Gini index), also coupled with Gaussian windowing, is a variant that is part of the scope of the invention. [Fig.4] shows an example of the implementation of the optional Refine() function executed by filter 8. In fact, this function is extremely close to the Focus() function. Also, similar or identical operations have reference numbers where only the hundreds digit changes, and only the differences will be described. The only difference between the Focus() function and the Refine() function lies in the definition of the neighborhood window. Thus, in operation 410, filter 8 executes a Wind2() function that defines the window W differently. Indeed, since the goal is to only consider neighbors that are in the same focus plane as the pixel cp or close (e.g., within one plane), the Wind2() function defines the window W in a similar way to the Wind() function, but, at each pixel, it retrieves the z value of this pixel in the focus image FocImg[], and discards the pixel if this z is distinct from the z of the z-image of the current pixel cp. Thus, for each z-image, the sparsity measure is refined for each pixel with only the pixels that are potentially relevant. The Refine() function can be repeated until a threshold of modification of the sparsity measures is reached. Alternatively, this function can be repeated a fixed number of times. The Applicant has discovered that 4 executions offer the best ratio between improvements and computational cost. The main gain offered by the Refine() function is the improvement of the bias in the linear regression of measurements by the device 2. Indeed, the tests on existing databases carried out by the Applicant have shown that the bias (ordinate at the origin of the linear regression) goes from 35 gigaplatelets per L to 24 gigaplatelets per L with the Refine() function, this bias being ideally 0. In the context of an embedded use, the Refine() function can nevertheless be omitted because its computational cost is very high.Gaussian windowing is of less interest in the Refine() function and can be relaxed, either fixedly or gradually, at each iteration. [Fig.5] shows an example of the implementation of the Selct() function executed by the selector 10. Now that the focus image is determined, the goal is to identify among the pixels those which correspond to potential platelet sites. This amounts to performing a search for local maxima. Thus, in a first operation 500, a first current pixel cp is initialized with the first pixel cpO in the coordinates of the z images, then two operations 510 and 520 are launched in parallel. In operation 510, a function MaxLoc() determines a Boolean value indicating whether the current pixel cp constitutes a local maximum in the z image whose depth is that associated with the current pixel cp in the focus image FocImg[]. This is achieved by comparing the intensity I of the current pixel cp to the intensity of all related pixels. Operation 520 is very close, except that the neighborhood is taken from the focus image FocImg[] itself, and no longer the z-image of the focus of the current pixel cp. Then, the values ​​m1 from operation 510 and m2 from operation 520 are compared with a logical OR in an operation 530. This test is therefore positive if one of the two values ​​m1 and m2 is 1. Indeed, two neighboring pixels do not necessarily have the same focus, and it is possible to have pixels that are local maxima on the focus image but not on the image of their focus plane. If the test in operation 530 is positive, then pixel cp is added in operation 540 to an array SS[] of selected sites, then a test in operation 550 determines whether there are any current pixels left to process. If so, the Select() function resumes with operations 510 and 520 for the new current pixel. Otherwise, a Sngl() function is executed in operation 560. The Sngl() function is used to take into account the fact that, among the selected pixels, it is quite common for two pixels to be on the same particle. Therefore, once the list SS[] is stopped, the Sng!() function scans it to identify pairs of directly neighboring and selected pixels, and keeps the one with the greatest intensity in the focus image FocImg[]. Finally, the Select() function ends in operation 599. In the above, the Select() function processes the pixels sequentially. It goes without saying that a parallelized version of this processing is possible. Furthermore, to provide a visually meaningful representation, the output SS[] list can be thought of as a site image in which the selected sites have a value of 1 and the other pixels have a value of 0. Here again, the site image and the SS[] list can be used interchangeably or together, as they designate the same thing. Alternatively, operation 560 could be omitted. Once the image of the sites has been determined, it remains to apply a metric to classify the pixels as particles or as background. The Applicant has carried out extensive research and has identified two families of metrics that allow the particles to be discriminated. The first family is Gaussian in nature, and includes two metrics: - a first metric in which for each site, a neighborhood centered on the site of dimensions 3x3 (alternatively, a 5x5, 7x7 or other window can be used to detect particles of different sizes) of the intensities in the z-image associated with the site in the focus image FocImg[] is modeled by a Gaussian whose inverse of the standard deviation constitutes the metric, - a second metric in which for each site, a 3x3 neighborhood of the intensities in the z-image associated with the site in the focus image FocImg[] is modeled by a Gaussian whose height constitutes the metric. Alternatively, the metric can also be based on the variance divided by the height. Figures 10 and 11 show how these two metrics separate PLT, GB, and background, with added separation zones. The second family is based on the z-gradient. In this version, the z-gradient is calculated for each pixel in a 3x3 site-centered neighborhood, with the intensities of these pixels in the respective z-images, this gradient being evaluated around the z-depth associated with the site in the focus image FocImg[]. The metric is then defined as the average of the calculated gradients. The device and method according to the invention can be used on other types of samples that one wishes to characterize. These samples can in particular comprise a medium in which particles are bathed. The medium can be a liquid medium. It can comprise a bodily fluid, obtained for example from blood or urine or lymph or cerebrospinal fluid. It can also be a culture medium, comprising nutrients allowing the development of microorganisms or cells. By particle, we mean in particular, and in a non-exhaustive manner: - a cell, whether a cultured cell or a body cell, for example a blood cell; - a microorganism, for example a bacterium or a yeast or a microalgae; - a solid particle, for example a microbead, the microbead being able to be functionalized, so as to promote grafting with an analyte; or - a particle forming an emulsion in the medium, in particular a particle insoluble in the medium, an example being a lipid droplet in an aqueous medium.

Claims

Claims

1. Lensless cn imagcric particle detection device, comprising a memory (4) arranged to receive a plurality of z-images obtained from lensless imaging of a biological sample, a preparer (6) arranged, from said images in z, to determine a focus image (FocImg[]) in in which each pixel is associated on the one hand with an image in z and on the other part to the intensity of this pixel in this image in z, the preparer (6) being arranged to determine the image in z for a given pixel in calculating, for each of the images in z and for the given pixel, a sparsity measure from the intensity of the given pixel and the in- intensities of neighboring pixels, and by selecting the image in z whose measurement of parsimony is the most important, a selector (10) arranged to de- terminate, for each pixel of the focus image (FocImg[]), if this pixel is a maximum in a local neighborhood centered on that pixel in the image focus (FocImg[]) or in the z-image associated with this pixel in the focus image (FocImeg[]), and, if so, to store that pixel in a list of selected sites (SS[]), and a calculator (12) arranged for calculate a metric value for each pixel in the site list se- selected based on the intensity of these pixels to enable the production an image of metric values ​​to distinguish the particles associated with each pixel in the selected site list between they.

2. A lensless imaging particle detection device wherein the calculator (12) is arranged to implement a metric being chosen from a metric based on the modeling of the intensities of a neighborhood of each pixel in the list of selected sites in the image in z associated with this pixel in the focus image by a Gaussian, in combining one or more of covariance, height and factor eccentricity of this Gaussian, and a metric based on the mean z gradients of the pixel intensity of a neighborhood centered on each pixel in the selected site list, which gradient is evaluated for the z-image corresponding to each pixel in the list of sites se- selected in the focus image (FocImg[]).

3. Device according to claim 1 or 2, in which the preparer (6) is arranged to use a mean-based sparsity measure pq where the intensities are modulated by a centered Gaussian window on the given pixel.

4. Device according to claim 3, in which the preparer (6) is arranged to use an average pq with p equal to 1 / 2 and q equal to 3, a square neighborhood of 17 pixels, and a Gaussian of standard deviation equal to 2.

5.

5. Device according to one of the preceding claims, in which the selector (10) is arranged to browse the list of selected sites, and, when two pixels are immediately adjacent, to keep the one whose associated intensity in the focus image (FocImg[]) is the most im- bearing.

6. Device according to one of the preceding claims, comprising in besides a filter (8) arranged to recalculate the focus image (FocImg[]) in recalculating the sparsity measure for each given pixel by re- restricting the neighborhood of pixels to pixels whose associated z-image in the focus image (FocImg[]) is identical to that associated with the pixel given or immediately neighboring in z.

7. A method of detecting particles in lensless imaging, comprising the following operations: a) receiving a plurality of z-images obtained from an imaging without lens of a biological sample, b) determine a focus image (FocImg[]) in which each pixel is associated on the one hand with an image in z and on the other hand with the intensity of this pixel in this image in z, this determination being carried out, for a given pixel, bl) by calculating, for each of the images in z and for the given pixel, a measure of sparsity from the intensity of the given pixel and the in- intensities of neighboring pixels, and b2) by selecting the image in z whose sparsity measure is the more important, c) determine, for each pixel of the focus image (FocImg[]), whether this pixel is a maximum in a local neighborhood centered on that pixel in the focus image (FocImg[]) or in the z-image associated with this pixel in the focus image (FocImg[]), and, if so, to store that pixel in a list of selected sites (SS[]), and d) calculate a metric value for each pixel in the site list selected based on the intensity of these pixels to allow produce an image of metric values ​​that can distinguish between particles associated with each pixel in the selected site list between they.

8. A method according to claim 7, wherein in step d), the metric is chosen from a metric based on the modeling of the intensities of a neighborhood of each pixel in the list of selected sites in the z-image associated with this pixel in the focus image by a Gaussian, by combining one or more of the covariance, the height and the eccentricity factor of this Gaussian, and a metric based on the average of the z gradients of the pixel intensity of a neighborhood centered on each pixel of the selected site list, which gradient is evaluated for the image in z corresponding to each pixel of the list of sites selected in the focus image (FocImg[]).

9. Method according to claim 7 or 8, in which operation b1) uses a sparsity measure based on the mean pq where the intensities are modulated by a Gaussian window centered on the given pixel.

10. A method according to claim 9, wherein operation bl) uses as a measure of parsimony the mean pq with p equal to 1 / 2 and q equal at 3, a square neighborhood of 17 pixels, and a Gaussian of equal standard deviation to 2.

5.

11. Method according to one of claims 7 to 10, in which operation c) includes browsing the list of selected sites, and, when two pixels are immediately neighbors, to preserve the one whose intensity associated in the focus image (FocImg![]) is the most important.

12. Method according to one of claims 7 to 11, in which operation b) further includes b3) recalculate the focus image (FocImg[]) by re- calculating the sparsity measure for each given pixel in re- restricting the neighborhood of pixels to pixels whose associated z-image in the focus image (FocImg[]) is identical to that associated with the pixel given or immediately neighboring in z.

13. | Computer program comprising instructions for executing the method according to one of claims 7 to 12 when implemented by computer.

14. Data storage medium on which the program is recorded computer according to claim 13.