Fluorescent dot counting method and device

Abstract

The invention provides a fluorescent dot counting method and device. The method provided by the invention comprises the steps of screening a fluorescent dot image according to a fluorescent dot area to obtain an optimal focusing image; performing image fusion processing on the optimal focusing image and N images before and after the optimal focusing image to obtain a comprehensive image; performing secondary image processing on the comprehensive image, enhancing fluorescent dot features corresponding to the to-be-detected sample in the comprehensive image to obtain a result image, and classifying the result image according to the fluorescent dot contour area in the result image to obtain a classification result; screening fluorescent dots in the result image according to the classification result and the fluorescent dot contour area to obtain an enhanced image; traversing all fluorescent dots in the enhanced image, and screening the fluorescent dots in the enhanced image according to the color of each fluorescent dot to obtain an optimized image; and carrying out fluorescent dot counting on the optimized image. According to the fluorescent dot counting method and device provided by the invention, the fluorescent dots can be accurately counted, and the fluorescent dot counting accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of single molecule detection, and particularly to a fluorescence spot counting method and device. Background Art

[0002] In the preparation process of the test sample, due to factors such as the out-of-control of the labeling reagent concentration and the inaccuracy of the labeling time control, the distribution of the fluorescent labels in the sample is uneven. At the same time, the inconsistency of the sample thickness will cause the fluorescence signals to overlap with each other, and the originally discrete fluorescence spots will also overlap. And in the prior art, usually, the focus is achieved by adjusting the distance between the lens of the microscope objective 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 and the micro-pits of the microfluidic chip. Further, when carrying out the fluorescence spot counting work, various errors generated in the preparation process make it difficult for the existing counting methods to accurately count the fluorescence spots in the test sample, and the counting accuracy is relatively low. Summary of the Invention

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

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

[0005] The first aspect of the present application provides a fluorescence spot counting method, and the method includes:

[0006] Put the test sample to be detected into a silicon-based chip, and take multiple fluorescence spot images of the test sample to be detected; wherein, the surface roughness of the silicon-based chip is less than 5 microns, and the shooting focal lengths of each fluorescence spot image are different;

[0007] Screen the fluorescence spot images according to the fluorescence spot area to obtain the optimal focus image;

[0008] Obtain the 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;

[0009] Perform secondary image processing on the composite image to enhance the fluorescence spot features corresponding to the test sample in the composite image to obtain a result image, and classify the result image according to the fluorescence spot contour area in the result image to obtain a classification result; wherein, the classification result includes large bubble images and ordinary images;

[0010] Screen the fluorescence spots in the result image according to the classification result and the fluorescence spot contour area to obtain an enhanced image;

[0011] Traverse all the fluorescent dots in the enhanced image, and screen the fluorescent dots in the enhanced image according to the color of each fluorescent dot to obtain an optimized image; wherein, the fluorescent dots with green color in the enhanced image are retained;

[0012] Perform fluorescent dot counting on the optimized image; wherein, during the counting process, only the fluorescent dots with an area within a predetermined pixel range in the optimized image are counted.

[0013] The second aspect of the present application provides a fluorescent dot counting device, which includes a shooting module, a selection module and a counting module;

[0014] Wherein, the shooting module is used to place the sample to be detected on a silicon-based chip and shoot multiple fluorescent dot images of the sample to be detected; wherein, the surface roughness of the silicon-based chip is less than 5 microns, and the shooting focal lengths of each fluorescent dot image are different;

[0015] The selection module is used to screen the fluorescent dot images according to the fluorescent dot area to obtain an optimal focus image;

[0016] The selection module is further used 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 N images before and after it to obtain a comprehensive image, where N is a positive integer;

[0017] The selection module is further used to perform secondary image processing on the comprehensive image, enhance the fluorescent dot 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 fluorescent dot contour area 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 used to screen the fluorescent dots in the result image according to the classification result and the fluorescent dot contour area to obtain an enhanced image;

[0019] The selection module is further used to traverse all the fluorescent dots in the enhanced image, and screen the fluorescent dots in the enhanced image according to the color of each fluorescent dot to obtain an optimized image; wherein, the fluorescent dots with a predetermined color in the enhanced image are retained;

[0020] The counting module is used to perform fluorescent dot counting on the optimized image; wherein, during the counting process, only the fluorescent dots with an area within a predetermined pixel range in the optimized image are counted.

[0021] The fluorescence dot counting method and device provided by the present application. In the first aspect, with a silicon-based chip as the carrier, fluorescence dot images of a detection sample at multiple focal lengths are captured, and then the optimal focus image is selected based on the number of fluorescence dots. The selection of the optimal focus image with the most complete information after shooting at multiple focal lengths ensures the shooting accuracy. Further, the shooting comprehensiveness is ensured through the fusion of multiple images, effectively preventing the situation of missing some fluorescence dots caused by shooting with a single focal length, and solving the problem that a single focal length cannot balance shooting accuracy and shooting comprehensiveness. In the second aspect, the fluorescence dot features are further enhanced by performing secondary image processing on the comprehensive image. And according to the size of the fluorescence dot contour area, the images are screened respectively to obtain enhanced images. The enhanced images are traversed, and only the parts with the fluorescence dot color being the predetermined color are retained, thereby obtaining an optimized image. For the optimized image, only the fluorescence dots with an area within a predetermined pixel range are counted, improving the image feature difference between bubbles and fluorescence dots, and then accurately identifying the fluorescence dots in the image, excluding various possible interference factors such as bubbles, improving the counting accuracy, achieving more accurate fluorescence dot counting, and significantly improving the accuracy and reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the first embodiment of the fluorescence dot counting method provided by the present application;

[0023] Figure 2 It is a schematic structural diagram of the first embodiment of the fluorescence dot counting device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0025] The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "the" and "said" used in the present 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" 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, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

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

[0028] Figure 1 It is a flowchart of the first embodiment of the fluorescence spot counting method provided by this application. Please refer to Figure 1 This method provided by this embodiment may include:

[0029] S101. Place the sample to be detected on a silicon-based chip and take multiple fluorescence spot images of the sample to be detected; wherein, the surface roughness of the silicon-based chip is less than 5 microns, and the shooting focal lengths of each fluorescence spot image are different.

[0030] Specifically, the sample to be detected refers to a biological sample labeled with fluorescence. A silicon-based chip with a surface roughness less than 5 microns is selected. The silicon-based chip has high flatness, which can ensure that the fluorescence spots in the sample to be detected are basically on the same horizontal plane. Furthermore, when using a fluorescence microscope to take pictures of the sample to be detected carried by the silicon-based chip, all the fluorescence spots can be clearly captured in the same image to achieve accurate counting.

[0031] Furthermore, during the shooting process, the sample to be detected is photographed with different focal lengths, thereby obtaining multiple fluorescence spot images of the sample to be detected at different focal lengths.

[0032] S102. Screen the fluorescence spot images according to the fluorescence spot area to obtain the optimal focus image.

[0033] Specifically, before screening the fluorescence spot images according to the fluorescence spot area, it further includes:

[0034] (1) Sequentially perform gray conversion and filtering processing on the multiple fluorescence spot images to obtain multiple first processed images.

[0035] Specifically, the multiple fluorescence spot images are color images. To reduce the complexity of data processing, reduce the amount of data for subsequent processing, and improve the detection speed, gray conversion processing needs to be performed on the fluorescence spot images. Specifically, when implementing, methods such as the component method, the average value method, or the weighted average method can be used to process the RGB of each pixel in the fluorescence spot image, and the RGB components of each pixel in the fluorescence spot image are converted into a single gray value.

[0036] Further, perform filtering processing on the fluorescence dot image after grayscale conversion processing. Mean filtering, median filtering, Gaussian filtering or other methods can be used for filtering processing. For the specific implementation process of the filtering processing, please refer to the description in the related technology, and it will not be elaborated here. After sequentially performing grayscale conversion and filtering processing on the fluorescence dot image, a first processed image with low data processing volume and low noise can be obtained.

[0037] (2) Screen the processing objects according to the area of the fluorescence dot contours in each first processed image, and perform convex hull completion on the processing objects to obtain multiple second processed images.

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

[0039] Further, screen the processing objects according to the area size of the fluorescence dot contours. The processing objects refer to the fluorescence dots with the area of the fluorescence dot contours greater than a preset threshold in multiple first processed images. Perform convex hull completion operations on the processing objects. Convex hull completion can fill the holes or incomplete parts in the fluorescence dot contours to make the shape of the fluorescence dots more complete.

[0040] During the convex hull completion process, first locate the holes and incomplete parts in the fluorescence dot contours. In the first processed image, the fluorescence dot contours can be regarded as curves composed of a series of connected pixel points. Starting from the starting point of the contour, check the connection situation between adjacent pixel points in sequence. If it is found that the distance between two adjacent pixel points is too large, exceeding the spacing range of the normal fluorescence dot contour pixel points, or there are obvious discontinuous regions, then these regions may be holes or incomplete parts.

[0041] After identifying the possible holes and incomplete parts, calculate the contour positions of these parts. For the areas suspected of being holes, analyze them based on the pixel points around the holes. Starting from a certain point on the edge of the hole, trace the pixel points along the edge in a clockwise or counterclockwise direction, and record the coordinates of each edge pixel point to determine the contour position of the hole. For the incomplete parts, also start from their boundary points, and extend and trace according to the connection relationship of the pixel points towards the missing parts until they are connected to the contours of other parts, thereby clarifying the contour positions of the incomplete parts. Further, 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 relevant functions. This convex polygon will cover the holes and incomplete parts, filling these areas to make the fluorescence point contours more complete. Group the first processed image sets after the convex hull completion process together to form the second processed image set.

[0042] (3) Perform flat-field correction processing on multiple said second processed images to obtain multiple binary images.

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

[0044] Furthermore, when performing flat-field correction processing on multiple second processed images, first obtain the background image. The background image refers to the image of the silicon-based chip when no sample to be detected is placed during fluorescence microscopy. Perform gray-scale conversion and filtering processing on the background image and multiple second processed images, calculate the average gray value of each pixel in the background image, and use this as the correction factor. For each pixel in each second processed image among the multiple second processed images, divide the gray value of each pixel by this correction factor to correct the pixel. After flat-field correction processing, multiple binary images are obtained. Among them, the binary image simplifies the pixel values in the image to two states (0 and 1), which is convenient for subsequent identification and processing of fluorescence points.

[0045] (4) Perform morphological processing and hole filling on multiple said binary images to obtain multiple first images after preprocessing.

[0046] Specifically, morphological processing includes erosion and dilation operations. The erosion operation can remove tiny noises and isolated pixel points in the image, while the dilation operation can enhance the contours of fluorescence points to make the fluorescence points more continuous and complete. By performing morphological processing on multiple binary images and filling the holes in the binary images after morphological processing, the ring-like fluorescence points in multiple binary images become round dots.

[0047] Further, traverse the fluorescence spot contours of each fluorescence spot obtained in step (2), and screen the fluorescence spots with ring-like fluorescence spot contours in the binary image according to the geometric features of each contour. Perform an erosion operation on the located ring region to make the inner and outer edges of the ring contract inward simultaneously. Then, perform a dilation operation on the eroded ring-like fluorescence spots to make the circular edges smoother and more continuous.

[0048] Further, the hole filling of the binary image after morphological processing includes: creating a mask image of all zeros, which has the same size as the binary image after morphological processing. Use the floodFill function to start filling from the boundary of the mask image, mark the outer region of the mask image, invert the filled mask image to obtain a mask representing the holes, and perform a bitwise OR operation on the binary image after morphological processing and the hole mask to achieve hole filling. Determine the multiple binary images after morphological processing and hole filling as multiple first images. It should be noted that during the preprocessing of multiple fluorescence spot images, the processing of multiple fluorescence spot images is multi-threaded. By processing multiple fluorescence spot images simultaneously, the detection speed can be accelerated.

[0049] Further, the specific implementation steps for screening the fluorescence spot images according to the fluorescence spot area include:

[0050] (1) Find the contours of all fluorescence spots in multiple said first images, and calculate the fluorescence spot area of each fluorescence spot;

[0051] Specifically, referring to the above description, calculate the fluorescence spot area of each fluorescence spot in multiple first images, and details will not be repeated here.

[0052] (2) Convert the area of each fluorescence spot contour in multiple said first images into the number of fluorescence spots, and determine the first image with the largest number of fluorescence spots as the optimal focus image.

[0053] Specifically, it should be noted that the unit of the fluorescence spot contour area is pixel. In specific implementation, the relationship between the area range value and the fluorescence spots can be determined according to empirical values. For example, in one embodiment, 20 pixel points are regarded as one fluorescence spot. If the area of a certain fluorescence spot contour is 100 pixel points, then the area of this fluorescence spot contour is 5 fluorescence spots. Thus, convert each fluorescence spot contour in multiple first images and calculate the number of fluorescence spots in each first image respectively. Determine the first image with the largest number of fluorescence spots as the optimal focus image. Because the image with the largest number of fluorescence spots means that more fluorescence spots can be captured under this focusing condition, and its focusing position is more conducive to subsequent fluorescence spot counting and analysis.

[0054] Further, when calculating the number of fluorescent spots in each first image, a unique index number is assigned to each first image in the order of taking pictures for subsequent search.

[0055] S103. 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 N images before and after it to obtain a composite image, where N is a positive integer.

[0056] Specifically, the specific implementation steps of performing image fusion processing on the optimal focus image and N images before and after it include:

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

[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 the dataset or list storing the images. Using the index number of the optimal focus image as an index, search for 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 the fluorescent spot information 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. Obtaining these images prepares for subsequent image fusion.

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

[0060] Specifically, after reading the optimal focus image and N first images before and after it one by one, perform image preprocessing to improve the image quality, make the subsequent image superposition effect better, and be more conducive to accurately extracting fluorescent spot information. Common preprocessing operations include grayscale conversion, Gaussian filtering, and binarization processing, etc. After these preprocessing operations, 2N + 1 preprocessed images are obtained, and these images are more suitable for superposition processing in terms of brightness, clarity, and noise control.

[0061] (3) Using the sample to be detected as the superposition reference, superimpose the 2N + 1 preprocessed images onto the same blank image to obtain the composite image.

[0062] Specifically, create a blank image, select the sample to be detected as the overlay reference, and sequentially read the 2N + 1 preprocessed images into this blank image one by one. By using the sample to be detected as the overlay reference, it can ensure that the fluorescent dots in different images can be accurately aligned during overlay, reflecting the true distribution of the fluorescent dots in the sample. In specific implementation, the corresponding pixel values of each preprocessed image can be added to the corresponding positions of the blank image, and then the image overlay is achieved through the accumulation of image pixel values to obtain a composite image. The composite image combines the fluorescent dot information at different focusing positions, contains more effective fluorescent dots, avoids the loss of some fluorescent dots during the counting stage due to different focal lengths, and provides a more comprehensive data basis for subsequent accurate fluorescent dot counting and analysis.

[0063] Furthermore, the vertices of each of the 2N + 1 preprocessed images can be used as fixed reference points, and the vertex positions of all 2N + 1 preprocessed images are unified. For the fluorescent dots in each preprocessed image, calculate the distance between the central pixel point of the fluorescent dot and the central pixel point of the fluorescent dot that has been overlaid into the blank image, and compare the distance with the distance threshold. For the fluorescent dots with a distance less than the threshold, process them based on the largest contour; for the fluorescent dots with a distance greater than or equal to the threshold but a contour distance less than the threshold, fuse them; for the fluorescent dots with both the distance and the contour distance greater than the threshold, do not fuse them, and use the corresponding fluorescent dot in the optimal focusing image as the reference. Taking the largest contour as the standard can retain the most complete information of the fluorescent dots; while fusing when the contour distance is less than the threshold can integrate the subtle differences of the same fluorescent dot in different images to obtain a more accurate shape and range of the fluorescent dots.

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

[0065] Specifically, the secondary image processing of the composite image specifically includes:

[0066] (1) Perform binarization processing on each pixel point in the composite image by using a dynamic threshold function to distinguish the fluorescent dots 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 regions of the image. Compared with the fixed threshold, it can distinguish fluorescent spots from the background more flexibly and accurately. 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 present different grayscale values. Generally, the grayscale value of the fluorescent spots is higher, and the grayscale value of the background is lower, which lays a foundation for subsequent separation of fluorescent spots and the background. For example, in practical applications, if the grayscale values of the fluorescent spots are concentrated in a certain higher interval and the grayscale values of the background are in a lower interval, the dynamic threshold function will determine a suitable threshold according to the grayscale distribution of the image, set the pixel points with grayscale values higher than the threshold to white (or higher grayscale values) representing the fluorescent spots, and the pixel points lower than the threshold to black (or lower grayscale values) representing the background, so as to clearly distinguish the fluorescent spots from the background, and then eliminate the background pixel points to highlight the characteristics of the fluorescent spots.

[0068] (2) Perform a dilation operation on the grayscale image, and process the grayscale image after the dilation operation using a convex hull function. Screen out the convex hulls in the grayscale image processed by the convex hull function that are larger than the area threshold according to the convex hull area threshold to obtain an elimination image.

[0069] Specifically, in the dilation operation, taking the fluorescent spots in the grayscale image as the center, incorporating the surrounding pixel points into the range of the fluorescent spots makes the contour of the fluorescent spots thicker. This can connect some parts that were originally separated but belong to the same fluorescent spot, avoiding misjudgment of the fluorescent spots in subsequent processing.

[0070] Furthermore, process the grayscale image after the dilation operation using a convex hull function. The convex hull function will generate a convex polygon that encloses all the fluorescent spots according to the contour of the fluorescent spots. By calculating the convex hull area, the fluorescent spots can be further screened. By setting the area threshold, the convex hulls in the grayscale image processed by the convex hull function that are larger than the convex hull area threshold can be deleted because the convex hulls with too large an area are likely to contain interference factors such as impurities or air bubbles in the background and are not real fluorescent spots. After screening out these large convex hulls, an elimination image is obtained. The interference information in the image is effectively reduced in the elimination image, making the subsequent analysis of the fluorescent spots more accurate.

[0071] (3) Perform an erosion operation on the elimination image, and perform an AND calculation on the grayscale image and the elimination image after the erosion operation to obtain a result image.

[0072] Specifically, the erosion operation is the opposite of the dilation operation. It will shrink the boundary of the object, remove some isolated and tiny noise points in the elimination image or the redundant pixel points introduced by the dilation operation, making the contour of the fluorescent spots in the elimination image more precise.

[0073] Further, perform an AND operation on the grayscale image and the elimination image after the erosion operation. The AND operation is a logical operation. Only when the pixel values at the corresponding positions of the grayscale image and the elimination image after the erosion operation are both non-zero, the pixel value at the corresponding position in the result image is non-zero. By performing the AND operation, a result image is obtained, which can further highlight the true contour of the fluorescent spots in the elimination image, remove the interference information that may remain during the dilation and convex hull processing, and make the features of the fluorescent spots in the result image more obvious. It should be noted that when performing the AND operation, the grayscale image used is the grayscale image that has not been processed by the dilation operation and the convex hull function, that is, the original grayscale image.

[0074] S105. Screen 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] Specifically, it may include:

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

[0077] Specifically, compare the contour area of each fluorescent spot in the result image with the threshold. When there are fluorescent spots with a contour area larger than the threshold in the result image, determine the result image as the large bubble image. The large bubble image means that there are large bubble areas in the result image that will affect the accurate counting of fluorescent spots. According to the contour area of the fluorescent spots, screen out the fluorescent spots with an area larger than the threshold in the large bubble image. It should be noted that the threshold is preset according to actual needs and is not limited in this embodiment.

[0078] Combined with the previous operations, the contour areas of each fluorescent spot in the result image have been obtained. In this step, compare the contour area of each fluorescent spot with the threshold. For those fluorescent spots with an area larger than the threshold, remove them from the large bubble image because these are probably incorrect fluorescent spot information caused by large bubbles. Through this screening operation, the interference caused by large bubbles is removed, making the fluorescent spot information in the image more accurate. Further, determine the large bubble image after removing the fluorescent spots with an area larger than the threshold as the enhanced image.

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

[0080] Specifically, in combination with the above description, when the contour area of all the fluorescent dots in the result image is less than or equal to the threshold, the result image is determined as a normal image. When the classification result is a normal image, it indicates that there is no interference area of large air bubbles in the image. In this case, no additional fluorescent dot screening operation is required, and the normal image is directly determined as the enhanced image. Because the fluorescent dot information of the normal image itself is relatively accurate and there is no obvious interference from large air bubbles, taking it directly as the enhanced image can retain the complete fluorescent dot information in the image and provide a reliable data basis for subsequent fluorescent dot counting and analysis.

[0081] S106. Traverse all the fluorescent dots in the enhanced image, and screen the fluorescent dots in the enhanced image according to the color of each fluorescent dot to obtain an optimized image; wherein, retain the fluorescent dots with a predetermined color in the enhanced image.

[0082] Specifically, in experiments such as biological detection, in order to simultaneously observe and distinguish different biomolecules, cell structures or targets, various different fluorescent dyes or fluorescent proteins and other markers are used. In a fluorescence microscope, by adjusting the excitation wavelength and selecting different filter plates, the same fluorescent substance can show different colors under different observation conditions. And different fluorescent substances have their specific excitation wavelength and emission wavelength ranges. In a complex experimental system, multiple fluorescent substances emit different colors of light after being excited by different excitation lights, thus forming fluorescent dots of various colors. Common fluorescent dot colors include colors such as red, green, blue, yellow and other composite colors.

[0083] Furthermore, traverse all the fluorescent dots in the enhanced image, and only retain the fluorescent dots with a predetermined color in the enhanced image, and remove the fluorescent dots of other colors to obtain an optimized image. This further improves the accuracy of fluorescent dot counting and reduces the interference of other miscellaneous color fluorescent dots on the counting. It should be noted that the predetermined color is set according to actual needs and is not limited in this embodiment.

[0084] S107. Perform fluorescent dot counting on the optimized image; wherein, during the counting process, only count the fluorescent dots with a fluorescent dot area within a predetermined pixel range in the optimized image.

[0085] Specifically, the specific implementation steps for performing fluorescent dot counting on the optimized image include:

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

[0087] Specifically, the boundary of the fluorescent dots in the optimized image can be recognized through an image processing algorithm to separate each fluorescent dot from the background. These boundary information constitute the contour of the fluorescent dot, and the pixel area of the region enclosed by the contour of each fluorescent dot is further calculated using the contourarea function.

[0088] (2) Retain the fluorescent dots whose area of the fluorescent dots is within a predetermined pixel range, and count the fluorescent dots according to every M pixel areas, and obtain a counting result.

[0089] Specifically, during the counting process, a predetermined pixel range is set, and only the fluorescent dots with the area of the fluorescent dots in the optimized image within this range are counted. It should be noted that the predetermined pixel range is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, the fluorescent dots with an area less than 5 pixels and greater than 1000 pixels are not involved in the counting, and the fluorescent dots with an area between 5 pixels and 1000 pixels are involved in the counting. And for the fluorescent dots involved in the counting, every M pixel areas are used to count the fluorescent dots. That is, during the counting, the area of a certain fluorescent dot is divided by M, and the obtained value is the count value of the fluorescent dot. It should be noted that the value of M is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, M is 150. Finally, the value of the counting point is marked in red numbers in the upper right corner of the corresponding fluorescent dot in the original image to complete the counting work of the fluorescent dots, so as to accurately count the number of fluorescent dots in the sample to be detected.

[0090] Further, after performing fluorescent dot counting on the optimized image, it further includes:

[0091] (1) Mark the count value of the fluorescent dot in the upper right corner of the corresponding fluorescent dot in the optimized image;

[0092] (2) Name the optimized image with the code - the number of fluorescent dots, and store the named optimized image in the folder where the optimal focus image is located.

[0093] The fluorescence spot counting method provided in this embodiment uses a silicon-based chip with a surface roughness less than 5 microns to capture fluorescence spot images at different focal lengths. At different focal lengths, the clarity and quantity of fluorescence spots in the images will vary. Capturing images at multiple focal lengths can more comprehensively capture the fluorescence spots in the sample, avoiding missing some fluorescence spots due to single-focal-length imaging, and providing a rich data basis for subsequent accurate analysis. Process these images, count the number of valid fluorescence spots to select the optimal focused image, and then obtain N images before and after it according to its index for image fusion to obtain a composite image; perform secondary image processing on the composite image to enhance the fluorescence spot features, classify the result image into large bubble images and normal images based on the fluorescence spot contour area and separately screen to obtain enhanced images, traverse the enhanced images to retain the green fluorescence spots to obtain an optimized image, and finally count the fluorescence spots with an area within a predetermined pixel range in the optimized image. Through this series of operations, comprehensively capture fluorescence spot information, remove various interference factors, thereby achieving more accurate fluorescence spot counting and greatly improving the accuracy and reliability of detection.

[0094] Corresponding to the foregoing embodiment of a fluorescence spot counting method, the present application also provides an embodiment of a fluorescence spot counting device.

[0095] Figure 2 It is a schematic structural diagram of the first embodiment of the fluorescence spot counting device provided by the present application. Please refer to Figure 2 , the device provided in this embodiment includes a shooting module 210, a selection module 220, and a counting module 230;

[0096] Among them, the shooting module 210 is used to place the sample to be detected on a silicon-based chip and capture multiple fluorescence spot images of the sample to be detected; among them, the surface roughness of the silicon-based chip is less than 5 microns, and the shooting focal length of each fluorescence spot image is different;

[0097] The selection module 220 is used to perform image processing on multiple fluorescence spot images to obtain an optimal focused image;

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

[0099] The selection module 220 is further used to perform secondary image processing on the composite image to enhance the fluorescence 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 fluorescence spot contour area in the result image to obtain a classification result; where the classification result includes large bubble images and normal images;

[0100] The selection module 220 is further configured to screen the fluorescent dots in the result image according to the classification result and the area of the fluorescent dot contour, so as to obtain an enhanced image.

[0101] The selection module 220 is further configured to traverse all the fluorescent dots in the enhanced image, and screen the fluorescent dots in the enhanced image according to the color of each fluorescent dot, so as to obtain an optimized image; wherein, the fluorescent dots with green color in the enhanced image are retained.

[0102] The counting module 230 is configured to count the fluorescent dots in the optimized image; wherein, during the counting process, only the fluorescent dots with the area of the fluorescent dots in the optimized image within a predetermined pixel range are counted.

[0103] Further, the device further includes a reaction cup feeding module, a sample feeding module, a reagent bin module, a sample adding, incubating and eluting module, and a detection waiting module. The reaction cup feeding module includes a material bin, a lifting mechanism, a crawler and a reaction cup transfer mechanism, and can automatically transport reaction cups; the sample feeding module can add samples; the reagent bin module has a housing, a gear disk, a reagent turntable and a transmission mechanism, and can rotate the reagent tube and regulate the temperature; the sample adding, incubating and eluting 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 dot counting device is used to detect the samples in the detection waiting module.

[0104] The device of this embodiment can be used to execute Figure 1 the steps of the method embodiment shown. The specific implementation principle and process are similar, and will not be elaborated here.

[0105] For the implementation process of the functions and roles of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0106] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present application. Those of ordinary skill in the art can understand and implement it without creative work.

[0107] The above are only the preferred embodiments of the present application, and are 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 spot counting method, characterized in that: The method comprises: Putting the sample to be tested into a silicon-based chip and taking multiple fluorescent spot 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 spot image is different; Filter the fluorescent spot image according to the fluorescent spot area 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 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 classifying 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; 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; 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, and obtaining an optimized image; wherein the fluorescent points in the enhanced image whose color is a predetermined color are retained; The fluorescent points of the optimized image are counted; wherein, during the counting process, only the fluorescent points whose areas in the optimized image are within a predetermined pixel point range are counted.

2. The method according to claim 1, characterized in that Before screening, at least the following steps are included: performing image processing on a plurality of the fluorescent spot images; including: Performing grayscale conversion and filtering processing on the plurality of fluorescent spot images in sequence to obtain a plurality of first processed images; Screening a processing object according to the area of ​​the fluorescent spot contour in each first processed image, performing convex hull completion on the processing object, and obtaining a plurality of second processed images; Performing flat field correction processing 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 1, characterized in that The step of screening the fluorescent spot image according to the fluorescent spot area comprises: Finding the contours 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 spot in the plurality of first images is converted into the number of fluorescent spots, and the first image with the largest number of fluorescent spots is determined as the optimal focus image.

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

5. The method according to claim 1, characterized in that The performing secondary image processing on the comprehensive image comprises: Using a dynamic threshold function to perform binarization processing on each pixel in the comprehensive image, distinguish the fluorescent point from the background, eliminate the background pixels, and obtain a grayscale image; 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 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 between 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 The step of counting the fluorescent spots 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 point range are retained, and each fluorescent spot is counted according to the area of ​​every M pixel points 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, the fluorescent spots with an area greater 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; If the classification result is a common image, the common image is determined as the enhanced image.

8. The method according to claim 1, characterized in that After counting the fluorescent spots 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 comprises 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 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; The selection module is used to filter the fluorescent spot images according to the fluorescent spot areas to obtain the optimal focus image; The selection module is further used 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 comprehensive image, where N is a positive integer; The selection module is further used to perform secondary image processing on the comprehensive image, enhance the fluorescent spot features corresponding to the sample to be detected in the comprehensive image, 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 used to select 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 used to traverse all the 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 whose color is 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 whose areas in the optimized image 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 addition, incubation and elution module, and a detection waiting module; wherein, The reaction cup feeding module is responsible for providing an empty reaction cup and transferring it to the sample addition, incubation and elution module to prepare the reaction container 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 absorb the sample and add it to the reaction cup; The reagent compartment module is used to store reagents, and uses a special transmission structure to continuously rotate the reagent tube to prevent the magnetic beads from settling, while providing the sample addition, incubation and elution modules with the reagents required for detection; The sample addition, incubation and elution module is used to complete the sample and reagent addition, incubation and mixing operations, and utilizes a cleaning mechanism and a resuspension liquid to perform elution and clean impurities; The detection waiting module is used to temporarily store the processed reaction cups, and to transport the reaction cups to a designated position through the rotation of the turntable to wait for optical detection.

Images