Fluorescence point counting method and device

Abstract

The present application provides a method and device for counting fluorescent points. The method provided in the present application includes: screening the fluorescent point image according to the area of the fluorescent points to obtain the optimal focus image; performing image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image; performing secondary image processing on the composite image to enhance the fluorescent point features corresponding to the sample to be detected in the composite image to obtain a result image, and classifying the result image according to the contour area of the fluorescent points in the result image to obtain a classification result; screening the fluorescent points in the result image according to the classification result and the contour area of the fluorescent points to obtain an enhanced image; traversing all the fluorescent points in the enhanced image, screening the fluorescent points in the enhanced image according to the color of each fluorescent point to obtain an optimized image; and counting the fluorescent points in the optimized image. The fluorescent point counting method and device provided in the present application can accurately count the fluorescent points and improve the accuracy of fluorescent point counting.

Description

Technical Field

[0001] The present application relates to the field of single molecule detection technology, and in particular to a fluorescence point counting method and device. Background Art

[0002] During the preparation of the test sample, due to factors such as the imbalance in the control of the labeling reagent concentration and the inaccurate control of the labeling time, the fluorescent label is unevenly distributed in the sample. At the same time, the inconsistent sample thickness will cause the fluorescent signals to overlap with each other, and the originally discrete fluorescent points will also overlap. In the prior art, focusing is usually achieved by adjusting the distance between the lens of the microscope objective lens and the micro-pits of the microfluidic chip. Due to the poor flatness of the microfluidic chip, it is difficult to achieve precise focusing by adjusting the distance between the lens of the microscope objective lens and the micro-pits of the microfluidic chip. Furthermore, when counting fluorescent points, various errors generated in the preparation process make it difficult for the existing counting method to accurately count the fluorescent points in the test sample, and the counting accuracy is low. Summary of the Invention

[0003] In view of this, the present application provides a fluorescent spot counting method and device to solve the problem of inaccurate counting when counting fluorescent spots.

[0004] Specifically, this application is implemented through the following technical solutions:

[0005] A first aspect of the present application provides a fluorescent spot counting method, the method comprising:

[0006] Placing a sample to be tested on a silicon-based chip and capturing multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and each fluorescent point image is captured at a different focal length;

[0007] screening the fluorescent spot image according to the area of the fluorescent spot to obtain the optimal focus image;

[0008] Obtaining N images before and after the optimal focus image according to the index of the optimal focus image, performing image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer;

[0009] performing secondary image processing on the composite image to enhance the fluorescent spot features corresponding to the sample to be detected in the composite image to obtain a result image, and classifying the result image according to the contour areas of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image;

[0010] screening the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image;

[0011] Traversing all fluorescent points in the enhanced image, screening the fluorescent points in the enhanced image according to the color of each fluorescent point, and obtaining an optimized image; wherein, fluorescent points in the enhanced image with a green color are retained;

[0012] The fluorescent points in the optimized image are counted; wherein, during the counting process, only the fluorescent points in the optimized image whose areas are within a predetermined pixel range are counted.

[0013] A second aspect of the present application provides a fluorescent point counting device, the device comprising a shooting module, a selection module and a counting module;

[0014] The shooting module is used to place the sample to be tested into a silicon-based chip and capture multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and the focal length of each fluorescent point image is different;

[0015] The selection module is used to filter the fluorescent spot images according to the areas of the fluorescent spots to obtain the optimal focus image;

[0016] The selection module is further configured to obtain N images before and after the optimal focus image according to the index of the optimal focus image, and perform image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer;

[0017] The selection module is further configured to perform secondary image processing on the composite image to enhance the fluorescent spot features corresponding to the sample to be detected in the composite image to obtain a result image, and classify the result image according to the contour area of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image;

[0018] The selection module is further configured to filter the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image;

[0019] The selection module is further configured to traverse all fluorescent points in the enhanced image and screen the fluorescent points in the enhanced image according to the color of each fluorescent point to obtain an optimized image; wherein the fluorescent points in the enhanced image having a predetermined color are retained;

[0020] The counting module is used to count the fluorescent points in the optimized image; wherein, during the counting process, only the fluorescent points in the optimized image whose areas are within a predetermined pixel range are counted.

[0021] The present application provides a method and device for counting fluorescent dots. First, using a silicon chip as a carrier, the method captures fluorescent dot images of a test sample at multiple focal lengths and then selects the optimal focus image based on the number of fluorescent dots. This method ensures shooting accuracy by selecting the optimal focus image with the most comprehensive information after capturing images at multiple focal lengths. Furthermore, the method ensures comprehensiveness by fusing multiple images, effectively preventing the omission of some fluorescent dots caused by capturing images at a single focal length. This solves the problem of a single focal length being unable to achieve both shooting accuracy and comprehensiveness. Second, the method further enhances the fluorescent dot features by performing secondary image processing on the composite image. Furthermore, the image is filtered based on the area of the fluorescent dot contours to obtain an enhanced image. The enhanced image is traversed, retaining only the portions of the fluorescent dots with a predetermined color, thereby obtaining an optimized image. Within the optimized image, only fluorescent dots with an area within a predetermined pixel range are counted. This improves the image feature difference between bubbles and fluorescent dots, allowing for accurate identification of fluorescent dots in the image and eliminating various possible interference factors, such as bubbles. This improves counting accuracy, achieving more precise fluorescent dot counting and significantly improving the accuracy and reliability of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of Example 1 of the fluorescent point counting method provided in this application;

[0023] Figure 2 This is a structural schematic diagram of Example 1 of the fluorescent point counting device provided in this application. DETAILED DESCRIPTION

[0024] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] Specific embodiments are given below to introduce the technical solutions of the present application in detail.

[0028] Figure 1 This is a flow chart of Example 1 of the fluorescence point counting method provided in this application. Please refer to Figure 1 The method provided in this embodiment may include:

[0029] S 1 0 1. Place a sample to be tested into a silicon-based chip, and capture multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and the focal length of each fluorescent point image is different.

[0030] Specifically, the sample to be tested refers to a biological sample that has been fluorescently labeled. A silicon-based chip with a surface roughness of less than 5 microns is selected. The silicon-based chip has high flatness, which can ensure that the fluorescent points in the sample to be tested are basically on the same horizontal plane. When a fluorescence microscope is used to photograph the sample to be tested with the silicon-based chip as the carrier, all the fluorescent points can be clearly photographed in the same image, achieving accurate counting.

[0031] Furthermore, during the shooting process, the sample to be detected is shot using different focal lengths, thereby obtaining multiple fluorescent point images of the sample to be detected at different focal lengths.

[0032] S102. Filter the fluorescent spot image according to the fluorescent spot area to obtain an optimal focus image.

[0033] Specifically, before filtering the fluorescent spot image according to the fluorescent spot area, the method further includes:

[0034] (1) Grayscale conversion and filtering are sequentially performed on the plurality of fluorescent point images to obtain a plurality of first processed images.

[0035] Specifically, multiple fluorescent point images are color images. To reduce the complexity of data processing, reduce the amount of data for subsequent processing, and improve the detection speed, it is necessary to perform grayscale conversion on the fluorescent point images. In specific implementation, the RGB of each pixel in the fluorescent point image can be processed using methods such as component method, average method, or weighted average method, and the RGB component of each pixel in the fluorescent point image can be converted into a single grayscale value.

[0036] Furthermore, the fluorescent spot image after grayscale conversion is filtered using methods such as mean filtering, median filtering, or Gaussian filtering. The specific implementation of the filtering process is described in the related art and will not be further described here. After grayscale conversion and filtering are sequentially performed on the fluorescent spot image, a first processed image with low data processing volume and low noise is obtained.

[0037] (2) Screening processing objects according to the area of the fluorescent spot contour in each first processing image, performing convex hull completion on the processing objects, and obtaining multiple second processing images.

[0038] Specifically, for each first processed image, the first processed image can be read through the OpenCV library in the Python code, and then the findcontours function in the OpenCV library is used to find the contour of the fluorescent point therein. For the found fluorescent point contour, the cv2.contourArea function is used to calculate the area of the fluorescent point contour.

[0039] Furthermore, the processing objects are screened according to the area of the fluorescent spot contours. The processing objects refer to the fluorescent spots in the multiple first processed images whose fluorescent spot contour areas are larger than a preset threshold. The convex hull completion operation is performed on the processing objects. The convex hull completion can fill the holes or incomplete parts in the fluorescent spot contours, making the shape of the fluorescent spots more complete.

[0040] During the convex hull completion process, the first step is to locate holes and incomplete parts in the fluorescent spot outline. In the first processed image, the fluorescent spot outline can be viewed as a curve composed of a series of connected pixels. Starting from the starting point of the outline, the connectivity between adjacent pixels is checked in sequence. If the distance between two adjacent pixels is too large, exceeding the normal spacing range of fluorescent spot outline pixels, or if there are obvious discontinuous areas, these areas may be holes or incomplete parts.

[0041] After determining the possible holes and incomplete parts, the contour positions of these parts are calculated. For areas suspected of being holes, analysis is performed based on the pixels around the hole. Starting from a certain point on the edge of the hole, trace the pixels along the edge in a clockwise or counterclockwise direction, and record the coordinates of each edge pixel to determine the contour position of the hole. For incomplete parts, also starting from its boundary point, according to the connection relationship of the pixels, extend the trace to the missing part until it is connected to the contours of other parts, thereby clarifying the contour position of the incomplete part. Furthermore, a dedicated algorithm can be used, such as in a common image processing library (such as OpenCV), to calculate the minimum convex polygon containing these contour positions through related functions. The convex polygon will cover the holes and incomplete parts, fill these areas, and make the contour of the fluorescent spot more complete. The first processed image set after the convex hull completion processing is put together to form a second processed image set.

[0042] (3) Performing flat-field correction on the plurality of second processed images to obtain a plurality of binary images.

[0043] Specifically, a flat field correction process is performed on multiple second processed images in the second processed image set. The purpose of the flat field correction process is to reduce the non-uniformity of each pixel in the second processed image and compensate for the brightness difference of the second processed image caused by factors such as the optical system and lighting conditions.

[0044] Furthermore, when performing flat-field correction on multiple second-processed images, a background image is first acquired. The background image refers to an image of the silicon chip taken with a fluorescence microscope without the sample to be tested. Grayscale conversion and filtering are then performed on the background image and the multiple second-processed images. The average grayscale value of each pixel in the background image is calculated and used as a correction factor. For each pixel in each of the multiple second-processed images, the grayscale value of each pixel is divided by the correction factor to correct the pixel. After flat-field correction, multiple binary images are obtained. The binary images simplify the pixel values in the image to two states (0 and 1), facilitating the subsequent identification and processing of fluorescent spots.

[0045] (4) Performing morphological processing and hole filling on the plurality of binary images to obtain a plurality of pre-processed first images.

[0046] Specifically, morphological processing includes erosion and dilation operations. Erosion can remove small noise and isolated pixels in an image, while dilation can enhance the outlines of fluorescent spots, making them more continuous and complete. By performing morphological processing on multiple binary images and filling holes in the morphologically processed binary images, the circular fluorescent spots in the multiple binary images can be transformed into circular dots.

[0047] Furthermore, the fluorescent point contours of each fluorescent point obtained in step (2) are traversed, and fluorescent points with circular ring-like contours in the binary image are screened according to the geometric features of each contour. An erosion operation is performed on the located circular ring area so that the inner and outer edges of the circular ring shrink inward at the same time, and then an expansion operation is performed on the eroded circular ring-like fluorescent point to make the circular edge smoother and more continuous.

[0048] Furthermore, hole filling of the binary image after morphological processing includes: creating an all-zero mask image, which is the same size as the binary image after morphological processing, using the floodFill function to fill from the boundary of the mask image, marking the outer area of the mask image, inverting the filled mask image to obtain a mask representing the holes, and performing a bitwise OR operation on the binary image after morphological processing and the hole mask to achieve hole filling. The multiple binary images after morphological processing and hole filling are determined as multiple first images. It should be noted that in the process of preprocessing the multiple fluorescent point images, the processing of the multiple fluorescent point images is multi-threaded, and the detection speed is accelerated by processing the multiple fluorescent point images at the same time.

[0049] Furthermore, the specific implementation steps of screening the fluorescent spot image according to the fluorescent spot area include:

[0050] (1) finding the contours of all fluorescent spots in the plurality of first images and calculating the fluorescent spot area of each fluorescent spot;

[0051] Specifically, referring to the above description, the fluorescent spot area of each fluorescent spot in the plurality of first images is calculated, which will not be described in detail here.

[0052] (2) converting the contour area of each fluorescent point in the plurality of first images into the number of fluorescent points, and determining the first image with the largest number of fluorescent points as the optimal focus image

[0053] Specifically, it should be noted that the unit of the fluorescent spot outline area is pixel. During implementation, the relationship between the area range value and the fluorescent spot can be determined based on empirical values. For example, in one embodiment, 20 pixels are considered as one fluorescent spot. If the outline area of a fluorescent spot is 100 pixels, then the outline area of the fluorescent spot is 5 fluorescent spots. Thus, each fluorescent spot outline in multiple first images is converted, and the number of fluorescent spots in each first image is calculated. The first image with the largest number of fluorescent spots is determined as the optimally focused image. This is because the image with the largest number of fluorescent spots means that more fluorescent spots can be captured under the given focusing conditions, and its focused position is more conducive to subsequent fluorescent spot counting and analysis.

[0054] Furthermore, when calculating the number of fluorescent spots in each first image, a unique index number is assigned to each first image according to the order in which the images were taken, for ease of subsequent searching.

[0055] S103. Obtain N images before and after the optimal focus image according to the index of the optimal focus image, perform image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer.

[0056] Specifically, the steps for implementing image fusion processing on the optimal focus image and the N images before and after it include:

[0057] (1) According to the index number of the optimal focus image, N first images before and after the optimal focus image are acquired.

[0058] Specifically, the index number is an identifier for identifying and locating an image in an image sequence. Through this number, the corresponding image can be found in a data set or list storing images. Using the index number of the optimal focus image as the index, search for the N first images before and after the optimal focus image. It should be noted that the value of N is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, N=10. These images contain information on fluorescent spots of the sample to be detected at different focus positions. Although the optimal focus image can present more fluorescent spots, other images may contain fluorescent spots that do not appear in the optimal image. Acquiring these images prepares for subsequent image fusion.

[0059] (2) performing image preprocessing on the optimal focus image and the N first images before and after it to obtain 2N+1 preprocessed images.

[0060] Specifically, the optimally focused image and the N preceding and succeeding first images are read one by one and then subjected to image preprocessing to improve image quality, enhance subsequent image overlay, and facilitate accurate extraction of fluorescent spot information. Common preprocessing operations include grayscale conversion, Gaussian filtering, and binarization. After these preprocessing operations, 2N+1 preprocessed images are obtained, which are more suitable for overlay processing in terms of brightness, clarity, and noise control.

[0061] (3) Using the sample to be detected as a superposition reference, 2N+1 pre-processed images are superimposed on the same blank image to obtain the composite image.

[0062] Specifically, a blank image is created, the sample to be tested is selected as the superposition reference, and 2N+1 pre-processed images are read into the blank image in sequence. By using the sample to be tested as the superposition reference, it can be ensured that the fluorescent points in different images can be accurately aligned when superimposed, reflecting the true distribution of the fluorescent points in the sample. In specific implementation, the corresponding pixel values of each pre-processed image can be added to the corresponding position of the blank image, and then the image superposition is achieved by accumulating the image pixel values to obtain a composite image. The composite image integrates the fluorescent point information at different focus positions, contains more effective fluorescent points, avoids the loss of some fluorescent points in the counting stage due to different focal lengths, and provides a more comprehensive data basis for subsequent accurate fluorescent point counting and analysis.

[0063] Furthermore, the vertex of each of the 2N+1 preprocessed images can be used as a fixed reference point, and the vertex position of all 2N+1 preprocessed images can be unified. For each fluorescent point in the preprocessed image, the distance between the center pixel of the fluorescent point and the center pixel of the fluorescent point superimposed on the blank image is calculated. The distance is compared with a distance threshold. For fluorescent points with a distance less than the threshold, the maximum contour is used as the basis for processing; for fluorescent points with a distance greater than or equal to the threshold but a contour distance less than the threshold, fusion is performed; for fluorescent points with both a distance and a contour distance greater than the threshold, fusion is not performed, and the corresponding fluorescent point in the optimal focus image is used as the reference. Using the maximum contour as the basis can retain the most complete information about the fluorescent point; while fusion when the contour distance is less than the threshold can integrate subtle differences in the same fluorescent point in different images, resulting in a more accurate fluorescence point shape and range.

[0064] S104. Perform secondary image processing on the comprehensive image to enhance the fluorescent spot features corresponding to the sample to be detected in the comprehensive image to obtain a result image, and classify the result image according to the contour area of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image.

[0065] Specifically, performing secondary image processing on the comprehensive image includes:

[0066] (1) Using a dynamic threshold function to perform binarization processing on each pixel in the integrated image, distinguish the fluorescent spot from the background, eliminate the background pixel points, and obtain a grayscale image.

[0067] Specifically, the dynamic threshold function can automatically adjust the threshold according to the characteristics of different areas of the image. Compared with the fixed threshold, it can more flexibly and accurately distinguish between fluorescent spots and background. After being processed by the dynamic threshold function, the composite image is converted into a grayscale image. In the grayscale image, the fluorescent spots and the background show different grayscale values. Generally, the grayscale value of the fluorescent spots is higher and the grayscale value of the background is lower. This lays the foundation for the subsequent separation of the fluorescent spots and the background. For example, in practical applications, if the grayscale values of the fluorescent spots are concentrated in a certain higher range and the grayscale value of the background is in a lower range, the dynamic threshold function will determine a suitable threshold based on the grayscale distribution of the image, and set the pixels with grayscale values higher than the threshold to white (or higher grayscale value) representing the fluorescent spots, and the pixels with grayscale values lower than the threshold to black (or lower grayscale value) representing the background, thereby clearly distinguishing the fluorescent spots from the background, and then eliminating the background pixels to highlight the characteristics of the fluorescent spots.

[0068] (2) performing a dilation operation on the grayscale image, and processing the grayscale image after the dilation operation using a convex hull function, and filtering out convex hulls larger than the area threshold in the grayscale image after the convex hull function processing according to a convex hull area threshold, to obtain an eliminated image.

[0069] Specifically, in the dilation operation, the fluorescent point in the grayscale image is taken as the center, and its surrounding pixels are also included in the fluorescent point range, making the outline of the fluorescent point thicker. This can connect some parts that were originally separated but belong to the same fluorescent point, avoiding misjudgment of the fluorescent point in subsequent processing.

[0070] Furthermore, the grayscale image processed by the dilation operation is processed using a convex hull function. This function generates a convex polygon containing all fluorescent points based on their outlines. By calculating the area of the convex hull, fluorescent points can be further screened. By setting an area threshold, convex hulls larger than the convex hull area threshold can be removed from the grayscale image processed by the convex hull function. This is because convex hulls with excessively large areas are likely to contain interference factors such as background impurities or large bubbles, and are not actual fluorescent points. After removing these large convex hulls, a de-interpolated image is obtained. This de-interpolated image effectively reduces interference information in the image, making subsequent analysis of fluorescent points more accurate.

[0071] (3) performing an erosion operation on the eliminated image, and performing an AND operation on the grayscale image and the eliminated image after the erosion operation to obtain a result image.

[0072] Specifically, the erosion operation is opposite to the dilation operation. It will shrink the boundary of the object, remove some isolated, tiny noise points in the image or redundant pixels introduced by the dilation operation, and make the outline of the fluorescent point in the image more accurate.

[0073] Furthermore, an AND operation is performed on the grayscale image and the post-erosion image. This AND operation is a logical operation. Only when the pixel values at corresponding positions in the grayscale image and the post-erosion image are both non-zero will the pixel values at the corresponding positions in the resulting image be non-zero. This AND operation, resulting in a resulting image, further highlights the true contours of the fluorescent spots in the eliminated image, removes any interference that may remain during the dilation and convex hull processing, and makes the features of the fluorescent spots in the resulting image more distinct. It should be noted that the grayscale image used in the AND operation is the original grayscale image, which has not been processed by the dilation and convex hull functions.

[0074] S105 , screening the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image.

[0075] The specific implementation may include:

[0076] (1) If the classification result is the large bubble image, the fluorescent spots with an area larger than a threshold in the large bubble image are screened out according to the contour area of the fluorescent spots to obtain an enhanced image.

[0077] Specifically, the contour area of each fluorescent point in the result image is compared with the threshold. When there is a fluorescent point with a contour area larger than the threshold in the result image, the result image is determined to be a large bubble image. The large bubble image means that there is a large bubble area with a large area in the result image that will affect the accurate counting of fluorescent points. According to the contour area of the fluorescent point, the fluorescent points with an area larger than the threshold in the large bubble image are screened out. It should be noted that the threshold is pre-set according to actual needs and is not limited in this embodiment.

[0078] Based on the previous steps, the outline area of each fluorescent spot in the result image has been obtained. In this step, the outline area of each fluorescent spot is compared with the threshold. Fluorescent spots with an area larger than the threshold are removed from the large bubble image. These spots are likely to be erroneous fluorescent spot information caused by large bubbles. This screening operation removes the interference caused by large bubbles, making the fluorescent spot information in the image more accurate. Furthermore, the large bubble image after removing fluorescent spots with an area larger than the threshold is determined to be the enhanced image.

[0079] (2) If the classification result is a normal image, the normal image is determined as the enhanced image.

[0080] Specifically, based on the above description, if the outline area of all fluorescent spots in the resulting image is less than or equal to the threshold, the resulting image is classified as a normal image. A normal image classification indicates that there are no large areas of interference from large bubbles. In this case, no additional fluorescent spot screening is required; the normal image is directly classified as an enhanced image. Because the fluorescent spot information in the normal image itself is relatively accurate and free of significant large bubble interference, using it directly as an enhanced image preserves the complete fluorescent spot information in the image, providing a reliable data foundation for subsequent fluorescent spot counting and analysis.

[0081] S106. Traverse all fluorescent points in the enhanced image, and screen the fluorescent points in the enhanced image according to the color of each fluorescent point to obtain an optimized image; wherein, fluorescent points in the enhanced image having a predetermined color are retained.

[0082] Specifically, in experiments such as biological detection, in order to simultaneously observe and distinguish different biological molecules, cell structures, or targets, a variety of different fluorescent dyes or fluorescent proteins are used as markers. In fluorescence microscopy, by adjusting the excitation wavelength and selecting different filters, the same fluorescent substance can display different colors under different observation conditions. Different fluorescent substances have their own specific excitation wavelength and emission wavelength ranges. In complex experimental systems, multiple fluorescent substances emit light of different colors after being excited by different excitation lights, thus forming fluorescent spots of various colors. Common fluorescent spot colors include red, green, blue, yellow, and other composite colors.

[0083] Furthermore, all fluorescent points in the enhanced image are traversed, and only those with the predetermined color are retained, while those with other colors are removed to obtain an optimized image. This further improves the accuracy of the fluorescent point counting and reduces interference from other fluorescent points of other colors. It should be noted that the predetermined color is set based on actual needs and is not limited in this embodiment.

[0084] S107. Count the fluorescent points in the optimized image. During the counting process, only the fluorescent points whose areas in the optimized image are within a predetermined pixel range are counted.

[0085] Specifically, the specific steps for counting fluorescent points in the optimized image include:

[0086] (1) Find the contours of all fluorescent spots in the optimized image and calculate the area of each fluorescent spot.

[0087] Specifically, the boundaries of fluorescent spots in the image can be identified and optimized through image processing algorithms to separate each fluorescent spot from the background. These boundary information constitute the contour of the fluorescent spot, and the contourarea function is further used to calculate the pixel area of the area enclosed by the contour of each fluorescent spot.

[0088] (2) Retain the fluorescent spots whose areas are within the predetermined pixel range, count the fluorescent spots according to the area of every M pixels, and obtain the counting results.

[0089] Specifically, during the counting process, a predetermined pixel range is set, and only those fluorescent spots within the optimized image whose areas fall within this range are counted. It should be noted that the predetermined pixel range is set based on practical needs and is not limited in this embodiment. For example, in one embodiment, fluorescent spots with areas less than 5 pixels and greater than 1,000 pixels are excluded from the counting process, while those with areas between 5 pixels and 1,000 pixels are included in the counting process. Furthermore, for each fluorescent spot included in the counting process, a count is performed for every M pixel areas. That is, during the counting process, the area of a fluorescent spot is divided by M, and the resulting value is the count value for that fluorescent spot. It should be noted that the value of M is set based on practical needs and is not limited in this embodiment. For example, in one embodiment, M is 150. Finally, the count value of the counted spot is marked with a red numeral in the upper right corner of the corresponding fluorescent spot in the original image, completing the fluorescent spot counting process and achieving accurate statistics of the number of fluorescent spots in the sample to be tested.

[0090] Furthermore, after counting the fluorescent points of the optimized image, the method further includes:

[0091] (1) marking the count value of the fluorescent spot at the upper right corner of the corresponding fluorescent spot in the optimized image;

[0092] (2) The optimized image is named according to the number of code-fluorescent points, and the named optimized image is stored in the folder where the best focus image is located.

[0093] The fluorescence point counting method provided in this embodiment uses a silicon-based chip with a surface roughness of less than 5 microns to capture fluorescence point images at different focal lengths. The clarity and number of fluorescence points in the image vary at different focal lengths. Multi-focal length shooting can more comprehensively capture the fluorescence points in the sample, avoiding the omission of some fluorescence points due to single-focal length shooting, providing a rich data foundation for subsequent accurate analysis. These images are processed, and the number of valid fluorescence points is counted to select the optimally focused image. Then, based on its index, N previous and subsequent images are fused to form a composite image. The composite image is subjected to secondary image processing to enhance the fluorescence point features. Based on the area of the fluorescence point contours, it is divided into large bubble images and ordinary images, and each is screened to obtain an enhanced image. The enhanced image is traversed to retain green fluorescence points to obtain an optimized image. Finally, the fluorescence points within the predetermined pixel range in the optimized image are counted. This series of operations, combined with comprehensive capture of fluorescence point information and elimination of various interference factors, achieves more accurate fluorescence point counting, significantly improving the accuracy and reliability of detection.

[0094] Corresponding to the aforementioned embodiment of a fluorescent point counting method, the present application also provides an embodiment of a fluorescent point counting device.

[0095] Figure 2 This is a structural diagram of the embodiment 1 of the fluorescent point counting device provided by this application. Figure 2 The device provided in this embodiment includes a shooting module 210, a selection module 220 and a counting module 230;

[0096] The shooting module 210 is used to place the sample to be tested into a silicon-based chip and shoot multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and the shooting focal length of each fluorescent point image is different;

[0097] The selection module 220 is configured to perform image processing on the plurality of fluorescent spot images to obtain an optimally focused image;

[0098] The selection module 220 is further configured to obtain N images before and after the optimal focus image according to the index of the optimal focus image, and perform image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer;

[0099] The selection module 220 is further configured to perform secondary image processing on the composite image to enhance the fluorescent spot features corresponding to the sample to be detected in the composite image to obtain a result image, and classify the result image according to the contour area of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image;

[0100] The selection module 220 is further configured to filter the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image.

[0101] The selection module 220 is further configured to traverse all fluorescent points in the enhanced image and screen the fluorescent points in the enhanced image according to the color of each fluorescent point to obtain an optimized image; wherein the fluorescent points in the enhanced image with a green color are retained;

[0102] The counting module 230 is configured to count the fluorescent points in the optimized image. During the counting process, only the fluorescent points whose areas in the optimized image are within a predetermined pixel range are counted.

[0103] Furthermore, the device also includes a reaction cup entry module, a sample entry module, a reagent chamber module, a sample addition, incubation and elution module, and a detection waiting module. The reaction cup entry module contains a material bin, a lifting mechanism, a crawler and a reaction cup transfer mechanism, which can automatically transport the reaction cup; the sample entry module can add samples; the reagent chamber module has a shell, a gear disk, a reagent turntable and a transmission mechanism, which can make the reagent tube rotate and control the temperature; the sample addition, incubation and elution module can clean, add liquid, incubate and mix to ensure sufficient reaction; the detection waiting module has a rotatable middle turntable for temporarily storing reaction cups, and the fluorescent point counting device is used to detect samples in the detection waiting module.

[0104] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0105] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0106] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0107] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A fluorescent point counting method, characterized in that: The method comprises: Placing a sample to be tested on a silicon-based chip and capturing multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and each fluorescent point image is captured at a different focal length; screening the fluorescent spot image according to the area of the fluorescent spot to obtain the optimal focus image; Obtaining N images before and after the optimal focus image according to the index of the optimal focus image, performing image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer; performing secondary image processing on the composite image to enhance the fluorescent spot features corresponding to the sample to be detected in the composite image to obtain a result image, and classifying the result image according to the contour areas of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image; screening the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image; Traversing all fluorescent points in the enhanced image, screening the fluorescent points in the enhanced image according to the color of each fluorescent point, and obtaining an optimized image; wherein the fluorescent points in the enhanced image having a predetermined color are retained; The fluorescent points in the optimized image are counted; wherein, during the counting process, only the fluorescent points in the optimized image whose areas are within a predetermined pixel range are counted.

2. The method according to claim 1, characterized in that Before screening, the method at least includes: performing image processing on the plurality of fluorescent spot images; including: performing grayscale conversion and filtering processing on the plurality of fluorescent point images in sequence to obtain a plurality of first processed images; screening processing objects according to the area of the fluorescent spot contour in each first processed image, performing convex hull completion on the processing objects to obtain a plurality of second processed images; performing flat-field correction on the plurality of second processed images to obtain a plurality of binary images; Morphological processing and hole filling are performed on the multiple binary images to obtain multiple pre-processed first images.

3. The method according to claim 2, characterized in that The method of screening the fluorescent spot image according to the fluorescent spot area comprises: Finding the outlines of all fluorescent spots in the plurality of first images, and calculating the fluorescent spot area of each fluorescent spot; The contour area of each fluorescent point in the plurality of first images is converted into the number of fluorescent points, and the first image with the largest number of fluorescent points is determined as the optimal focus image.

4. The method according to claim 2, characterized in that The image fusion processing of the optimal focus image and the N images before and after it includes: According to the index number of the optimal focus image, obtain N first images before and after the optimal focus image; Performing image preprocessing on the optimal focus image and N preceding and following first images to obtain 2N+1 preprocessed images; Taking the sample to be detected as the superposition basis, 2N+1 pre-processed images are superimposed on the same blank image to obtain the composite image.

5. The method according to claim 1, wherein The performing secondary image processing on the integrated image comprises: Using a dynamic threshold function to perform binarization processing on each pixel in the integrated image, distinguish the fluorescent spot from the background, eliminate the background pixels, and obtain a grayscale image; Performing a dilation operation on the grayscale image, processing the grayscale image after the dilation operation using a convex hull function, and filtering out convex hulls larger than an area threshold in the grayscale image after the convex hull function processing according to the convex hull area to obtain an eliminated image; An erosion operation is performed on the eliminated image, and an AND calculation is performed on the grayscale image and the eliminated image after the erosion operation to obtain a result image.

6. The method according to claim 1, characterized in that Counting the fluorescent points on the optimized image comprises: Finding the contours of all fluorescent spots in the optimized image and calculating the area of each fluorescent spot; The fluorescent spots whose areas are within the predetermined pixel range are retained, and each fluorescent spot is counted according to the area of every M pixels to obtain a counting result.

7. The method according to claim 1, characterized in that The step of screening the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image comprises: If the classification result is the large bubble image, filtering out the fluorescent spots with an area larger than a threshold in the large bubble image according to the contour areas of the fluorescent spots to obtain an enhanced image; If the classification result is a normal image, the normal image is determined as the enhanced image.

8. The method according to claim 1, characterized in that After counting the fluorescent points of the optimized image, the process includes: Mark the count value of the fluorescent spot at the upper right corner of the corresponding fluorescent spot in the optimized image; The optimized image is named according to the number of code-fluorescent points, and the named optimized image is stored in the folder where the best focus image is located.

9. A fluorescent point counting device, characterized in that: The device includes a shooting module, a selection module and a counting module; The shooting module is used to place the sample to be tested into a silicon-based chip and capture multiple fluorescent point images of the sample to be tested; wherein the surface roughness of the silicon-based chip is less than 5 microns, and the focal length of each fluorescent point image is different; The selection module is used to filter the fluorescent spot images according to the areas of the fluorescent spots to obtain the optimal focus image; The selection module is further configured to obtain N images before and after the optimal focus image according to the index of the optimal focus image, and perform image fusion processing on the optimal focus image and the N images before and after it to obtain a composite image, where N is a positive integer; The selection module is further configured to perform secondary image processing on the composite image to enhance the fluorescent spot features corresponding to the sample to be detected in the composite image to obtain a result image, and classify the result image according to the contour area of the fluorescent spots in the result image to obtain a classification result; wherein the classification result includes a large bubble image and a normal image; The selection module is further configured to filter the fluorescent spots in the result image according to the classification result and the contour area of the fluorescent spots to obtain an enhanced image; The selection module is further configured to traverse all fluorescent points in the enhanced image and screen the fluorescent points in the enhanced image according to the color of each fluorescent point to obtain an optimized image; wherein the fluorescent points in the enhanced image having a predetermined color are retained; The counting module is used to count the fluorescent points in the optimized image; wherein, during the counting process, only the fluorescent points in the optimized image whose areas are within a predetermined pixel range are counted.

10. The device according to claim 9, characterized in that The device also includes a reaction cup entry module, a sample entry module, a reagent compartment module, a sample loading, incubation and elution module, and a detection waiting module; wherein, The cuvette feeding module is responsible for providing empty cuvettes and transferring them to the sample loading, incubation and elution module to prepare reaction vessels for the detection process; The sample input module is used to place the sample to be tested, move the sample tube rack through the sample rack driving device, and cooperate with the sample needle to draw the sample and add it to the reaction cup; The reagent compartment module is used to store reagents. It uses a special transmission structure to continuously rotate the reagent tubes to prevent magnetic bead precipitation, while providing the reagents required for detection to the sample loading, incubation and elution modules. The sample addition, incubation and elution module is used to complete the sample and reagent addition, incubation and mixing operations, and uses a cleaning mechanism and a resuspension solution to elute and clean impurities; The detection waiting module is used to temporarily store the processed reaction cups and transport the reaction cups to a designated position through the rotation of the turntable to wait for optical detection.

Images