Virus infection positioning detection method and system based on diffractive optics

Abstract

The invention discloses a virus infection positioning detection method and system based on diffractive optics. The method comprises the following steps: acquiring diffraction fingerprints corresponding to all to-be-detected cells in an imaging area by utilizing a diffraction imaging principle; respectively extracting texture features of each diffraction fingerprint in the imaging area by using a gray level co-occurrence matrix method; the extracted texture features are compared with standard texture features pre-stored in a relation model of cell morphological change and diffraction fingerprints, so that the'cell state 'of each cell to be detected in the imaging area can be obtained, and the virus infection state of each cell in the imaging area can be positioned and identified. The efficient light diffraction spectrum system is used for virus infection detection, and the requirement for professionals and expensive and complex professional field detection equipment is eliminated. The continuous and high-throughput evaluation on the cell condition after virus infection is met, and a foundation is laid for popularization and application of a virus infection instant detection platform.

Description

Technical Field

[0001] The present invention relates to the field of virus detection, and specifically to detecting the virus infection status of cells based on diffraction fingerprints. Background Art

[0002] Currently, many viral diseases lack specific drug treatments. Therefore, relying on timely detection methods is essential to mitigate the risks and damage caused by epidemics. Cell research, as the basic unit of life activities, is an important object for analyzing the essence of life processes.

[0003] The traditional in vitro detection process of the degree of viral stress on cells is long, requires many reagents, and is difficult to automate, which will cause irreversible damage to the cells after the test. The currently commonly used instrument detection method is limited by the defects of the detection means and cannot realize the information extraction of specific cell samples. The new on-site instant detection method is of great significance for controlling and preventing the spread of viral diseases.

[0004] Therefore, the development of highly automated, high-throughput, and cost-effective cell virus infection detection technology has important research significance and practical value. Summary of the invention

[0005] Aiming at the difficulty in real-time positioning and visualizing the infection status in the field of virus detection, the present invention provides a virus infection positioning detection method and system based on diffraction optics, which can achieve both high resolution and wide field of view, monitor the virus status of cell infection, and realize non-destructive virus infection detection.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A method for localizing and detecting virus infection based on diffraction optics comprises the following steps:

[0008] S1. Using the diffraction imaging principle, the diffraction fingerprints corresponding to all cells to be tested in the imaging area are collected;

[0009] S2, using the gray level co-occurrence matrix method to extract the texture features of each diffraction fingerprint in the imaging area;

[0010] S3. Compare the extracted texture features with the standard texture features pre-stored in the relationship model between cell morphological changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area, and locating and identifying the virus infection state of each cell in the imaging area.

[0011] Furthermore, the gray level co-occurrence matrix method is used to extract the texture features of the diffraction fingerprint. The specific process is as follows:

[0012] S2.1, constructing a gray level co-occurrence matrix based on the obtained diffraction fingerprint in the imaging area;

[0013] S2.2. Two features related to the texture information of the diffraction fingerprint image are selected from multiple statistical features of the gray-level co-occurrence matrix, namely, the CON feature and the IDM feature, which are expressed as:

[0014]

[0015] Where L is the maximum grayscale level of the image, i and j represent the grayscale levels of two pixels, P(i, j, d, θ) is the joint probability density, and P(i, j) is the probability of grayscale level j appearing starting from grayscale level i, which constitutes the elements of the grayscale co-occurrence matrix.

[0016] Furthermore, the mathematical expression of the joint probability density is:

[0017] P(i, j, d, θ)={(x, y), (x+a, y+b)∈NN×NN|f(x, y)=i, f(x+a, y+b)=j}

[0018] Where (x, y) is any point in the image, x is the x-coordinate of any point in the image, y is the y-coordinate of any point in the image, a is the deviation of the x-coordinate, b is the deviation of the y-coordinate, NN is the global space, and f(x, y) is the grayscale value at any point (x, y) in the image.

[0019] Furthermore, the method for constructing the relationship model between cell morphological changes and diffraction fingerprints is as follows:

[0020] S3.1. Conduct virus infection experiments on animal cells, record the cell states at different infection times and collect diffraction fingerprints;

[0021] S3.2, using the gray-level co-occurrence matrix method to extract the texture features corresponding to the diffraction fingerprints of cells at different infection durations;

[0022] S3.3. A standard database is formed based on "infection duration-cell morphology-texture features" to obtain a relationship model between cell morphological changes and diffraction fingerprints.

[0023] Furthermore, if there are multiple cells to be tested in the imaging area, the infection duration and cell morphology of each cell to be tested are obtained by comparing the texture features of each cell to be tested, and the cells to be tested in the imaging area are located and identified.

[0024] A virus infection positioning detection system based on diffraction optics, comprising: a diffraction imaging device, a diffraction fingerprint analysis module, and a diffraction fingerprint evaluation module;

[0025] The diffraction imaging device comprises a diffraction box, a monochromatic light source, and an image sensor; a micro-hole is provided on the upper part of the diffraction box; the monochromatic light source provides light to the bottom of the diffraction box through the micro-hole; the image sensor is arranged at the bottom of the diffraction box to capture the diffraction image in the imaging area;

[0026] The diffraction fingerprint analysis module receives the diffraction image input by the image sensor and extracts the texture features of the diffraction fingerprint;

[0027] The diffraction fingerprint evaluation module receives the texture features extracted by the diffraction fingerprint analysis module and compares them with the standard texture features pre-stored in the relationship model between cell morphological changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area.

[0028] Furthermore, the diffraction fingerprint analysis module and the diffraction fingerprint evaluation module are both mounted in the processor, which is STM32H7 with a main frequency of 480MHz, an operating voltage of 3.3~5V, and is connected to the computer through a full-speed USB interface.

[0029] Furthermore, by adjusting the heights of the monochromatic light source and the diffraction box, the imaging area in the diffraction box is covered by light.

[0030] Furthermore, a sample platform is provided above the image sensor for placing samples.

[0031] Furthermore, the image sensor adopts a CMOS image sensor with an active imaging size of 5.70mm×4.28mm and a pixel size of 2.2μm.

[0032] Beneficial effects of the present invention:

[0033] 1. The present invention is based on the characteristic that cells exhibit specific morphologies at specific stages after being stimulated by viruses. The diffraction fingerprints of cells are collected through the principle of diffraction imaging, and then the texture features corresponding to the diffraction fingerprints are extracted using the gray-level co-occurrence matrix method (GLCM). On the one hand, the "infection duration-cell morphology-texture features" are correlated with each other, and the state of cell infection with the virus and the duration of infection can be confirmed only based on the changes in cell morphology; on the other hand, the feature information is quantified for easy comparison.

[0034] 2. The present invention can obtain the diffraction fingerprint of cells in real time only through the diffraction imaging device. The diffraction fingerprint analysis module and the diffraction fingerprint evaluation module are realized by using algorithms. The cost of the constructed platform is much lower than other platforms. No additional operation is required during the detection process, and the samples after detection are still operable. This method has a high degree of automation, saves time and effort, and has strong continuity, eliminating the need for professionals and expensive and complex professional on-site detection equipment.

[0035] 3. Compared with the cell viability test results obtained by the "gold standard" MTT method, the correlation of the present invention reached 98.9% and passed the 99% significance test; this shows that the method and system proposed in the present invention can be used as a determination of the virus stress cell situation, and can be widely used in the detection of human or animal viral diseases and the study of virus transmission between humans and animals. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of a virus infection positioning detection system based on diffraction optics of the present invention;

[0037] Figure 2 Schematic diagram of cell morphology changes at different times after virus stress of the present invention;

[0038] Figure 3 This is a schematic diagram of the impact of samples of different shapes on the optical path;

[0039] Figure 4 It is the sample image and its diffraction fingerprint collected by the present invention.

[0040] In the figure:

[0041] Monochromatic light source 101 , 300 μm microhole 102 , height adjustment module 103 , sample 104 , image sensor 105 .

[0042] The morphology of cells in good growth state is 201, the morphology of cells infected with viruses is 202, the morphology of cells seriously infected is 203, and the morphology of dead suspended cells is 204.

[0043] Light source 301, light path 302, diffraction fingerprint 303

[0044] Microscopic image 401 of cells not infected with the virus, microscopic image 402 of cells infected with the virus for 12 hours, microscopic image 403 of cells infected with the virus for 24 hours, microscopic image 404 of cells infected with the virus for 36 hours, diffraction image 405 of cells not infected with the virus, diffraction image 406 of cells infected with the virus for 12 hours, diffraction image 407 of cells infected with the virus for 24 hours, and diffraction image 408 of cells infected with the virus for 36 hours. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] According to research, adherent cells exhibit specific morphologies at specific stages after being stimulated by viruses. As time passes after virus infection, the cell body changes from its original relatively stretched flat shape to gradually wrinkled edges, bulging in the center, shrinking its coverage area, and becoming more and more spherical in overall shape, with a smaller volume and poor adhesion, until it completely falls off the substrate and floats in the culture medium. Figure 2 The schematic diagram of cell morphology changes at different times after virus stress is shown, from left to right, the morphology of cells in good growth state 201, the morphology of cells infected with viruses 202, the morphology of cells seriously infected 203, and the morphology of dead suspended cells 204. Through the analysis of the relationship between cell morphological changes and diffraction fingerprints, it can be seen that cells of different morphologies have certain differences in their influence on the light path, that is, each cell corresponds to a unique diffraction fingerprint.

[0047] Based on the above characteristics, the present invention proposes a virus infection location detection method based on diffraction optics, comprising the following steps:

[0048] S1. Using the diffraction imaging principle, the diffraction fingerprints corresponding to all cells to be tested in the imaging area are collected;

[0049] S2, using the gray level co-occurrence matrix method (GLCM) to extract the texture features of each diffraction fingerprint in the imaging area;

[0050] S3, compare the extracted texture features with the standard texture features pre-stored in the relationship model between cell morphology changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area, and locating and identifying the virus infection state of each cell in the imaging area. More specifically, the diffraction fingerprint is an image with clear texture. The present invention uses the gray level co-occurrence matrix method (GLCM) to extract the texture features of the diffraction fingerprint. The specific process is as follows:

[0051] S2.1. Based on the acquired diffraction fingerprint in the imaging area, a grayscale co-occurrence matrix is ​​constructed. The element P(i, j|d, θ) in the grayscale co-occurrence matrix represents the joint distribution of the grayscales of two pixels with a certain spatial position relationship, that is, the probability (normalizing the frequency, i.e. dividing by the sum of all frequencies) of the grayscale j (column) with i as the starting point (row) when the spatial distance d and direction θ are given, which constitutes the element P(i, j|d, θ) of the grayscale co-occurrence matrix.

[0052] Any point (x, y) in the image and a point (x+a, y+b) deviating from it constitute a point pair. Assume that the gray value of the point pair is (f1, f2). Assuming that the maximum gray level of the image is L, there are L*L combinations of f1 and f2. For the entire image, count the number of occurrences of each (f1, f2) value and arrange them into a square matrix. Then normalize the total number of occurrences of (f1, f2) to obtain the probability P(f1, f2), thereby generating a gray-level co-occurrence matrix. GLCM is defined by calculating the joint probability density of the gray levels of two pixels. The mathematical expression of the joint probability density is:

[0053] P(i, j, d, θ)={(x, y), (x+a, y+b)∈N×N|f(x, y)=i, f(x+a, y+b)=j}

[0054] Among them, (x, y) is any point in the image, x is the x-coordinate of any point in the image, y is the y-coordinate of any point in the image, a is the deviation of the x-coordinate, b is the deviation of the y-coordinate, N is the global space, f(x, y) is the grayscale value at any point (x, y) in the image, at a given spatial distance d and direction θ, the grayscale starts from i, and the probability of grayscale j appearing constitutes the element P(i, j, d, θ) of the grayscale co-occurrence matrix.

[0055] S2.2. Two features related to the texture information of the diffraction fingerprint image are selected from multiple statistical features of the gray level co-occurrence matrix. The present invention selects the CON feature and the IDM feature, which are respectively expressed as follows:

[0056]

[0057] Among them, CON feature is used to measure the grayscale change between the reference pixel and the adjacent pixel. The more drastic the local grayscale change on the image, the deeper the texture groove, the clearer the visual effect, and the greater the contrast, which indicates the clarity of the image and the depth of the texture groove. IDM feature is used to reflect the clarity and regularity of the texture. When the texture is clear, regular, and easy to describe, the value is larger; P(i,j) is the probability of grayscale j starting from grayscale i, which constitutes the element of the grayscale co-occurrence matrix.

[0058] More specifically, the method for constructing the relationship model between cell morphological changes and diffraction fingerprints is as follows:

[0059] S3.1. Conduct virus infection experiments on animal cells, record the cell states at different infection times and collect diffraction fingerprints;

[0060] S3.2, using the gray level co-occurrence matrix method (GLCM) to extract the texture features corresponding to the diffraction fingerprints of cells at different infection durations;

[0061] S3.3. A standard database is formed based on "infection duration-cell morphology-texture features" to obtain a relationship model between cell morphological changes and diffraction fingerprints.

[0062] More specifically, based on the above-mentioned relationship model between cell morphological changes and diffraction fingerprints, once the texture features of the cells to be tested are obtained, the texture features of the cells to be tested are compared with the data in the relationship model between cell morphological changes and diffraction fingerprints. If they match, information such as the infection duration of the cells to be tested can be obtained.

[0063] More specifically, since there may be multiple cells to be tested in the imaging area, by comparing the texture features of each cell to be tested, information such as the infection duration of each cell to be tested can be obtained, and the cells to be tested in the imaging area can be located and identified.

[0064] In order to realize the above-mentioned virus infection positioning detection method based on diffraction optics, the present invention also proposes a virus infection positioning detection system based on diffraction optics, including: a diffraction imaging device, a diffraction fingerprint analysis module, and a diffraction fingerprint evaluation module.

[0065] The structure of the diffraction imaging device is as follows Figure 1 As shown, it mainly consists of three parts: a diffraction box, a monochromatic light source 101, and an image sensor 105. In this embodiment, the monochromatic light source 101 is a monochromatic LED lighting lamp with a wavelength of 650nm. A microhole 102 is opened on the upper part of the diffraction box, and the diameter of the microhole 102 is 300μm. The monochromatic light source 101 is arranged toward the microhole 102, which helps to obtain a suitable light intensity and a partially coherent light source, thereby reducing interference. The image sensor 105 and the sample platform are arranged in sequence from bottom to top at the bottom of the diffraction box; the sample platform is used to place the sample, and the image sensor 105 is arranged toward the sample platform to capture the diffraction image in the imaging area.

[0066] In the diffraction fingerprint analysis module, the gray level co-occurrence matrix method (GLCM) is used to extract the texture features of each diffraction fingerprint in the imaging area.

[0067] The diffraction fingerprint evaluation module compares the extracted texture features with the standard texture features pre-stored in the relationship model between cell morphology changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area, and locating and identifying the virus infection state of each cell in the imaging area. The "cell state" is to estimate the state of cell infection and infection duration based on cell morphology changes.

[0068] Furthermore, the above-mentioned diffraction fingerprint analysis module and diffraction fingerprint evaluation module are both installed in the processor, which is STM32H7 with a main frequency of 480MHz and an operating voltage of 3.3-5V. It is connected to a computer through a full-speed USB (12Mbs) interface, and automatically records and captures diffraction images on the computer. The gray-level co-occurrence matrix method (GLCM) is used to extract the texture features of the diffraction fingerprint, and the state of cell infection with the virus is identified based on different features.

[0069] Furthermore, the height of the monochromatic light source 101 and the diffraction box can be manually adjusted to provide uniform illumination, and the entire imaging area can be adjusted in real time to be covered by light. In this embodiment, the light source is set at a height of about 6 cm directly above the sample plane.

[0070] Furthermore, the culture dish containing cells is fixed on the sample platform so that the image sensor 105 can clearly capture the image in the imaging area. The distance between the bottom plane of the culture dish and the imaging plane is about 1 mm.

[0071] Furthermore, in this embodiment, a 5-megapixel CMOS image sensor is used, the active imaging size is 5.70 mm × 4.28 mm, and the pixel size is 2.2 μm, that is, the large field of view covers an area of ​​up to 24.396 mm2, which is about 100 times that of a conventional optical microscope of ×400. If a CMOS image sensor with a larger photosensitive area is used, the field of view can be further expanded. The resolution of the image capture lens is 1920*1080 dpi.

[0072] Furthermore, since mammalian cells have no cell walls and their cell bodies are colorless and transparent, the amplitude modulation effect of the cell body on the incident light can be ignored during the diffraction experiment, and the cell can be approximately regarded as a completely transparent pure phase body. Phase modulation is a modulation method in which the deviation of the phase of the carrier from its reference phase changes proportionally with the instantaneous value of the modulation signal. Its effect mainly depends on the refractive index distribution in the cell body and the path taken by the transmitted light in the cell. The modulation effect of the sample in this article on the incident light can be regarded as pure phase modulation. That is, a phase shift is produced. There is no amplitude attenuation, that is, a(x, y) = 0. Compared with the pure culture fluid area, the optical path OPL corresponding to the scattering points in the cell-covered area is different, and the phase of the object wave formed is different. Therefore, the transmittance function of the entire sample area can be expressed as:

[0073]

[0074] Among them, c is the cell coverage area, that is, the wavefront in the cell coverage area undergoes certain distortion and deformation, such as Figure 3 As shown in the figure, light source 301, light path 302, and diffraction fingerprint 303. Assume that the cell thickness at a point in the cell coverage area c is h c (x, y), the cytoplasmic refractive index is nc (x, y), the overall thickness of the sample is h and is the same everywhere, and the refractive index of the culture medium is the same everywhere and is n m Then the optical path length OPL(x, y) corresponding to the point (x, y) where the transmitted light passes through the cell-covered area is different from the optical path length OPL0 corresponding to the point where the transmitted light passes through the pure culture medium area. The optical path difference is:

[0075] ΔOPL=OPL(x,y)-OPL0=n m [hh c (x,y)]+n c (x,y)h c (x, y)-n m h

[0076] Relative to the pure culture solution region, the phase shift at this point is:

[0077]

[0078] The optical model of unstained adherent cultured mammalian cells was constructed as follows:

[0079]

[0080] In order to verify the feasibility of the solution proposed in the present invention, the present invention uses pig testicle cell line (ST) and pseudorabies virus (PRV) to conduct cell infection virus experiments.

[0081] First, ST cells were inoculated with PRV, the cell states at different times were recorded and diffraction fingerprints were collected, the diffraction fingerprint characteristics were analyzed, and the performance of the proposed method was verified. Studying the effects of virus stress on cells will help to better understand the changes in cell states after ST cells are infected with PRV, and directly show the resistance of cells to viruses. ST cells were cultured in DMEM / HIGHGLUCOSE medium (XY Biotechnology), supplemented with 10% fetal bovine serum (Capricorn), and then added with 1% penicillin-streptomycin mixture (double antibody, XY Biotechnology). The cells were cultured in a water-jacketed CO2 incubator (Heal Force, HF160W) at 37°C in an atmosphere of 5% CO2 and 99% relative humidity. ST cells were cultured for 48 hours to allow them to grow adherently, and fresh culture medium was replaced every 24 hours. 0.25% Trypsin-EDTA was used to detach the cells from the wall, and the cells were continued to be cultured using a homemade culture dish. The culture dish was 3D printed with a fully transparent photosensitive resin material around it, and the bottom of the culture dish was made of quartz (JGS1), with a culture area of ​​25 cm 2, a total of 16 culture dishes were used in the experiment, divided into 4 groups, 4 in each group. ST cells were injected into the four groups of culture dishes in equal amounts. The 4 culture dishes in the first group were not inoculated with virus, which was recorded as 0 hours of virus inoculation. PRV was diluted to 10 of the original concentration. -3 , the diluted virus was inoculated into the ST cells that had been detached from the wall, and the culture was continued. The 4 culture dishes in the second group were rinsed with PBS (Phosphate Buffer Solution) 12 hours after the virus was inoculated, and the state of the cells growing on the wall and their diffraction fingerprints were observed. At this time, the cell growth state was good, but there were phenomena such as cell elongation and obvious intercellular gaps. The 4 culture dishes in the third group were observed for the state of the cells and their diffraction fingerprints 24 hours after the virus was inoculated. At this time, the cells were partially detached from the wall, but some cells still grew on the wall. The 4 culture dishes in the fourth group were observed for the state of the cells and their diffraction fingerprints 36 hours after the virus was inoculated. At this time, the cells were almost completely detached from the wall, that is, a large number of cells died. The process of cell death after virus inoculation is accompanied by changes in cell morphology. Therefore, exploring the relationship between cell morphology and diffraction can further analyze the virus infection corresponding to the diffraction fingerprint.

[0082] In the virus infection cell experiment, the cell samples were treated with PRV and images were captured every 6 hours using a microscope, e.g. Figure 4 As shown. The results show that the cell morphology is closely related to the length of time the cells are infected with the virus. When there is no virus infection, the cells grow well, they are stretched and have a good flat morphology, and are attached to the bottom of the culture dish 401; under the action of the virus, the cells are elongated and the boundaries between cells gradually become clear 402; as time goes by, the activity of the cells decreases after being infected by the virus, the cells shrink, become round, and their adhesion decreases, and they fall off from the bottom of the culture dish and suspend in the culture medium 403-404. The diffraction fingerprints of cells in specific areas at different times are then collected through the diffraction platform, and 405-408 correspond to the diffraction fingerprints of 401-404 respectively.

[0083] In order to evaluate the effectiveness of the proposed method, the MTT method was used to conduct a comparative test on the same samples. ST cells were cultured in culture medium for 24 hours until the cells were well attached. They were divided into 3 groups, each with 64 samples. The cells that were not infected with the virus were marked as group 1. Then the cells were infected with 0.1% PRV virus and continued to be cultured for 24 hours and 36 hours, which were recorded as group 2 and group 3, respectively. To ensure the accuracy of the experimental results, each figure corresponds to 16 groups of data, and each group of data takes the average value of 4 culture chambers. As the virus infection time increases, the cell activity gradually weakens.

[0084] Compared with the MTT method, the method has fewer reagents and consumables. A single detection process does not require any additional processing. It takes about 10 minutes to collect images and perform automatic analysis. The MTT method requires reagent processing and the chemical reaction time is as long as 4 hours. When there are many types of doses to be detected, the advantages of the method will be more obvious. At the same time, the detection equipment of the MTT method is an enzyme reader, which generally costs $3,000-4,000, while the diffraction imaging platform costs less than $300.

[0085] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A virus infection location detection method based on diffraction optics, characterized in that: The steps include: S1. Using the diffraction imaging principle, the diffraction fingerprints corresponding to all cells to be tested in the imaging area are collected; S2, using the gray level co-occurrence matrix method to extract the texture features of each diffraction fingerprint in the imaging area; S3. Compare the extracted texture features with the standard texture features pre-stored in the relationship model between cell morphological changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area, and locating and identifying the virus infection state of each cell in the imaging area.

2. A virus infection location detection method based on diffraction optics according to claim 1, characterized in that: The texture features of diffraction fingerprints are extracted using the gray level co-occurrence matrix method. The specific process is as follows: S2.1, constructing a gray level co-occurrence matrix based on the obtained diffraction fingerprint in the imaging area; S2.

2. Two features related to the texture information of the diffraction fingerprint image are selected from multiple statistical features of the gray-level co-occurrence matrix, namely, the CON feature and the IDM feature, which are expressed as: Among them, L is the maximum grayscale level of the image, i and j represent the grayscales of two pixels, P(i,j,d,θ) is the joint probability density, and P(i,j) is the probability of grayscale j appearing starting from grayscale i, which constitutes the elements of the grayscale co-occurrence matrix.

3. A virus infection location detection method based on diffraction optics according to claim 2, characterized in that: The mathematical expression of the joint probability density is: P(i,j,d,θ)= { (x,y),(x+a,y+b)∈N×N | f(x,y)=i,f(x+a,y+b)=j} Where (x, y) is any point in the image, x is the x-coordinate of any point in the image, y is the y-coordinate of any point in the image, a is the deviation of the x-coordinate, b is the deviation of the y-coordinate, N is the global space, and f(x, y) is the grayscale value at any point (x, y) in the image.

4. The method for locating and detecting virus infection based on diffraction optics according to claim 1, characterized in that: The method for constructing the relationship model between cell morphological changes and diffraction fingerprints is as follows: S3.

1. Conduct virus infection experiments on animal cells, record the cell states at different infection times and collect diffraction fingerprints; S3.2, using the gray-level co-occurrence matrix method to extract the texture features corresponding to the diffraction fingerprints of cells at different infection durations; S3.

3. A standard database is formed based on "infection duration-cell morphology-texture features" to obtain a relationship model between cell morphological changes and diffraction fingerprints.

5. The method for locating and detecting virus infection based on diffraction optics according to claim 1, characterized in that: If there are multiple cells to be tested in the imaging area, the infection duration and cell morphology of each cell to be tested are obtained by comparing the texture features of each cell to be tested, and the cells to be tested in the imaging area are located and identified.

6. A virus infection location detection system based on diffraction optics, characterized in that: For implementing a virus infection location detection method based on diffraction optics as claimed in claim 1, the system comprises: a diffraction imaging device, a diffraction fingerprint analysis module, and a diffraction fingerprint evaluation module; The diffraction imaging device comprises a diffraction box, a monochromatic light source (101), and an image sensor (105); a microhole (102) is provided on the top of the diffraction box; the monochromatic light source (101) provides light to the bottom of the diffraction box through the microhole (102); the image sensor (105) is arranged at the bottom of the diffraction box to capture a diffraction image in an imaging area; The diffraction fingerprint analysis module receives the diffraction image input by the image sensor (105) and extracts the texture features of the diffraction fingerprint; The diffraction fingerprint evaluation module receives the texture features extracted by the diffraction fingerprint analysis module and compares them with the standard texture features pre-stored in the relationship model between cell morphological changes and diffraction fingerprints, thereby obtaining the "cell state" of each cell to be tested in the imaging area.

7. A virus infection location detection system based on diffraction optics according to claim 6, characterized in that: The diffraction fingerprint analysis module and the diffraction fingerprint evaluation module are both installed in the processor, which is STM32H7, with a main frequency of 480MHz, an operating voltage of 3.3~5V, and is connected to the computer through a full-speed USB interface.

8. The virus infection location detection system based on diffraction optics according to claim 6, characterized in that: By adjusting the heights of the monochromatic light source (101) and the diffraction box, the imaging area in the diffraction box is covered by light.

9. The virus infection location detection system based on diffraction optics according to claim 6, characterized in that: A sample platform is arranged above the image sensor (105) for placing samples.

10. The virus infection location detection system based on diffraction optics according to claim 9, characterized in that: The image sensor (105) adopts a CMOS image sensor, the active imaging size is 5.70 mm×4.28 mm, and the pixel size is 2.2 μm.

Images