CTC image optimization detection method based on fluorescent probe detection

Abstract

The invention relates to the field of cell detection, in particular to a CTC image optimization detection method based on fluorescent probe detection, and the method comprises the following steps: acquiring a CTC fluorescent microscopic image detected by a fluorescent probe method, converting the CTC fluorescent microscopic image into a grayscale image, and carrying out edge detection; evaluating the boundary degree of edge pixel points through gradient information of the edge pixel points in the microscopic grayscale image; optimizing the boundary fitting process through the boundary degree of the edge pixel points; performing fuzzy factor evaluation range optimization according to a boundary fitting result; optimizing an objective function of fuzzy clustering according to the optimized fuzzy factor evaluation, and completing CTC fluorescent microscopic grayscale image segmentation based on fuzzy clustering to obtain a preliminary segmentation result; and performing cell image processing according to the preliminary segmentation result of the CTC fluorescence microscopic image to obtain a CTC cell image detection result, thereby solving the problem of inaccurate CTC image detection caused by non-uniform fluorescence intensity.

Description

Technical Field

[0001] The present invention relates to the technical field of cell detection, and in particular to a CTC image optimization detection method based on fluorescent probe detection. Background Art

[0002] CTC (circulating tumor cells) are cells that fall off from solid tumors and enter the peripheral blood circulation, which has important clinical significance. The detection and analysis of CTC can provide key evidence for early diagnosis of cancer, efficacy evaluation, recurrence monitoring and personalized treatment. In the process of processing and detecting CTC fluorescence microscopy images, the commonly used method for segmenting CTC images is usually to perform preliminary segmentation of CTC fluorescence microscopy images based on fuzzy clustering, and perform cell area filling and morphological processing based on the preliminary segmentation results to complete the detection of tumor cells in CTC fluorescence microscopy images. However, because the fluorescence signal intensity is easily affected by factors such as fluorescent probe concentration, excitation light intensity, and cell morphology, the boundary of the target cell is blurred or some pixels are lost, which affects the accuracy of segmentation and detection. Therefore, how to solve the inaccurate segmentation of CTC fluorescence microscopy images caused by the blurred cell boundary and the loss of some pixels due to the uneven fluorescence signal intensity in the fuzzy clustering segmentation of CTC images based on fluorescent probe detection has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a CTC image optimization detection method based on fluorescent probe detection to solve the problem of inaccurate CTC detection caused by blurred cell boundaries and partial pixel loss due to uneven fluorescence signal intensity.

[0004] An embodiment of the present invention provides a CTC image optimization detection method based on fluorescent probe detection, and the CTC image optimization detection method based on fluorescent probe detection includes the following steps:

[0005] The method comprises the following steps: obtaining a CTC fluorescence microscopic image detected by a fluorescent probe method and converting it into a CTC fluorescence microscopic grayscale image; performing edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image; evaluating the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image by using the gradient information of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image; optimizing the cell boundary fitting process of edge pixel points in the CTC fluorescence microscopic grayscale image by using the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the CTC The boundary fitting result of the fluorescence microscopic grayscale image; according to the boundary fitting result of the CTC fluorescence microscopic grayscale image, the fuzzy factor evaluation range of the pixel points in the CTC fluorescence microscopic grayscale image is optimized to obtain the optimized fuzzy factor evaluation; according to the optimized fuzzy factor evaluation of the pixel points in the CTC fluorescence microscopic grayscale image, the objective function of the pixel points in the fuzzy clustering process is evaluated, and according to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image; according to the preliminary segmentation result of the CTC fluorescence microscopic image, cell image processing is performed to obtain the CTC cell image detection result.

[0006] Preferably, performing edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixels in the CTC fluorescence microscopic grayscale image includes:

[0007] A double threshold of the CTC fluorescence microscopic grayscale image and the edge detection Canny algorithm is obtained, and edge detection is performed on the CTC fluorescence microscopic grayscale image using the Canny algorithm to obtain all edge pixels in the CTC fluorescence microscopic grayscale image.

[0008] Preferably, the step of evaluating the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image by using the gradient information of edge pixels in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image includes:

[0009] Obtain the gradient direction and gradient amplitude of all edge pixels in the CTC fluorescence microscopic grayscale image, obtain a preset number of neighboring pixels of the edge pixel, and obtain a neighbor set of any edge pixel among all edge pixels in the CTC fluorescence microscopic grayscale image according to the number of neighboring pixels of the edge pixel; for any target edge pixel in the CTC fluorescence microscopic grayscale image, use the cosine function mapping result of the gradient direction difference between the target edge pixel and any edge pixel in the neighbor set of the target edge pixel as the first gradient direction difference of the target edge pixel; use the calculation result of subtracting the gradient amplitude of the target edge pixel from the mean of all gradient amplitudes in the neighbor set of the target edge pixel as the first gradient amplitude difference of the target edge pixel; use the mean of the spatial distance difference between the target edge pixel and all edge pixels in the neighbor set of the target edge pixel as the first average distance of the target edge pixel;

[0010] The calculation result of multiplying the first gradient direction difference, the first gradient amplitude difference and the first average distance of the target edge pixel point is used as the first boundary evaluation, the normalized calculation result of the first boundary evaluation mean of the target edge pixel point and all edge pixels in the neighboring set corresponding to the target edge pixel point is used as the second boundary evaluation, and the calculation result of subtracting the constant 1 from the second boundary evaluation is used as the boundary degree of the target edge pixel point.

[0011] Preferably, the cell boundary fitting process of edge pixels in the CTC fluorescence microscopic grayscale image is optimized by the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image to obtain the boundary fitting result of the CTC fluorescence microscopic grayscale image, including:

[0012] All edge pixels in the CTC fluorescence microscopic grayscale image are obtained, the boundary degrees corresponding to all edge pixels are obtained, and preset cell boundary ellipse fitting parameters are obtained, wherein the cell boundary ellipse fitting parameters include the ellipse fitting center, the ellipse fitting major axis length, and the ellipse fitting minor axis length; the boundary fitting objective function is:

[0013] ;

[0014] in, Indicates The boundary fitting objective function corresponding to the edge pixels; Indicates the number of neighboring pixels of the preset edge pixel; Indicates The first The boundary degree of edge pixels; Indicates The first The horizontal coordinate of the edge pixel in the image; Indicates the horizontal coordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates The first The vertical coordinate of the edge pixel in the image; Indicates the ordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates the length of the major axis of the ellipse preset in the boundary fitting process; Indicates the length of the minor axis of the ellipse preset in the boundary fitting process;

[0015] For the boundary fitting objective function, the coordinates of the center of the ellipse and the major and minor axis length parameters are jointly optimized by the gradient descent method to complete the fitting process, and all boundary fitting results in the CTC fluorescence microscopy grayscale image are obtained.

[0016] Preferably, the step of optimizing the fuzzy factor evaluation range of pixels in the CTC fluorescence microscopic grayscale image according to the boundary fitting result of the CTC fluorescence microscopic grayscale image to obtain the optimized fuzzy factor evaluation comprises:

[0017] For the boundary fitting results of the CTC fluorescence microscopic grayscale image, the elliptical range formed by each boundary fitting result is taken as a cell region, and the pixel points in the cell region are taken as a whole local range. For any pixel point in the CTC fluorescence microscopic grayscale image, it is judged whether it is in any cell region. If the pixel point is in any cell region, the cell region where the pixel point is located is used as the optimized evaluation range of the fuzzy factor corresponding to the pixel point in the fuzzy clustering process; if the pixel point is not in any cell region, the eight-neighborhood range of the pixel point itself is used as the optimized evaluation range of the fuzzy factor corresponding to the pixel point in the fuzzy clustering process.

[0018] For any pixel point in the CTC fluorescence microscopic grayscale image, the optimized fuzzy factor evaluation is obtained by the overall difference between the pixel point in the optimized evaluation range of the fuzzy factor corresponding to the pixel point of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process.

[0019] Preferably, the objective function of the pixels in the fuzzy clustering process is evaluated according to the fuzzy factor evaluation after the pixels in the CTC fluorescence microscopic grayscale image are optimized, and the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed according to the optimized objective function to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image, including:

[0020] The calculation formula of the objective function of the pixel point in the fuzzy clustering process is:

[0021] ;

[0022] in, represents the objective function of the clustering process of the CTC fluorescence microscopic grayscale image; Represents the number of pixels in the CTC fluorescence microscopy grayscale image; Indicates the preset number of clusters for the clustering process of the CTC fluorescence microscopic grayscale image; Indicates the CTC fluorescence microscopic grayscale image Pixel pair The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The pixel points are related to the The Euclidean distance of the gray values ​​between the cluster center points of the clusters; Indicates the CTC fluorescence microscopic grayscale image The pixel points in the fuzzy clustering process are Evaluation of optimized fuzzy factors for clusters;

[0023] According to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image.

[0024] Preferably, performing cell image processing according to the preliminary segmentation result of the CTC fluorescence microscopy image to obtain the CTC cell image detection result includes:

[0025] Obtain a preliminary segmentation result of the CTC fluorescence microscopic grayscale image, and obtain the clusters corresponding to the CTC cells according to the segmentation results corresponding to each cluster in the preliminary segmentation result; perform flood filling on the clusters corresponding to the CTC cells, perform morphological operation processing to obtain the final segmentation result, and obtain the detection result of the CTC cell image.

[0026] Preferably, the step of obtaining the optimized fuzzy factor evaluation by the overall difference between the pixel points in the optimized evaluation range of the fuzzy factor corresponding to the pixel points of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process comprises:

[0027] When any pixel in the CTC fluorescence microscopic grayscale image is in any cell region, the calculation formula for the fuzzy factor of any pixel in the CTC fluorescence microscopic grayscale image is:

[0028] ;

[0029] in, Indicates the CTC fluorescence microscopic grayscale image Pixels for the The fuzzy factor of each cluster; Indicates the CTC fluorescence microscopic grayscale image The optimized evaluation range of pixels; Indicates the CTC fluorescence microscopic grayscale image The pixel point and the optimal evaluation range of the pixel point The spatial Euclidean distance between pixels; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range Pixels for the The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range The pixel points are related to the The Euclidean distance between the grayscale values ​​of the cluster center points of the clusters.

[0030] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0031] The present invention obtains a CTC fluorescence microscopic image detected by a fluorescent probe method and converts it into a CTC fluorescence microscopic grayscale image; performs edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image; evaluates the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image according to the gradient information of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image; optimizes the cell boundary fitting process of edge pixel points in the CTC fluorescence microscopic grayscale image according to the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the CTC fluorescence microscopic grayscale image. The method comprises the following steps: obtaining a boundary fitting result of a CTC fluorescence microscopic grayscale image; optimizing a fuzzy factor evaluation range of pixels in the CTC fluorescence microscopic grayscale image according to the boundary fitting result of the CTC fluorescence microscopic grayscale image, and obtaining an optimized fuzzy factor evaluation; evaluating an objective function of pixels in a fuzzy clustering process according to the optimized fuzzy factor evaluation of pixels in the CTC fluorescence microscopic grayscale image, and completing CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering according to the optimized objective function, and obtaining a preliminary segmentation result of the CTC fluorescence microscopic grayscale image; performing cell image processing according to the preliminary segmentation result of the CTC fluorescence microscopic image, and obtaining a CTC cell image detection result. The cell boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image is evaluated, and then the cell boundary fitting is optimized according to the cell boundary degree of the edge pixels to improve the accuracy of cell boundary fitting, and the local range used to evaluate the pixel fuzzy factor in the fuzzy clustering process is obtained according to the optimized fitting result. When fuzzy clustering is performed on the CTC fluorescence microscopic grayscale image, the clustering of pixels in the cell boundary range is optimized to obtain a more accurate preliminary segmentation result of the CTC fluorescence microscopic image, thereby avoiding inaccurate segmentation of the CTC fluorescence microscopic image due to blurred cell boundaries and partial pixel loss caused by uneven fluorescence intensity, thereby improving the detection accuracy of the CTC fluorescence microscopic image. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 Flow chart of the CTC image optimization detection method based on fluorescent probe detection according to an embodiment of the present invention; DETAILED DESCRIPTION

[0034] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0035] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this toothpaste can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an unexpected order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0036] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0037] The specific scenario targeted by the present invention is: performing image optimization processing during tumor cell detection and identification of CTC fluorescence microscopy images.

[0038] In addition, the CTC mentioned in the embodiments of the present invention refers to circulating tumor cells.

[0039] See also Figure 1 , is a method flow chart of the CTC image optimization detection method based on fluorescent probe detection provided in Example 1 of the present invention, such as Figure 1 As shown, the CTC image optimization detection method based on fluorescent probe detection may include:

[0040] Step S101, obtaining a CTC fluorescence microscopic image detected by a fluorescent probe method and converting it into a CTC fluorescence microscopic grayscale image; performing edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image.

[0041] The CTC fluorescence microscopic image detected by the fluorescent probe method is collected, and the image resolution collected is a fixed resolution. In this embodiment, Pixels. The CTC fluorescence microscopy image is then converted into a color space. Specifically, a grayscale processing method is used to convert the collected CTC fluorescence microscopy image into a grayscale image for subsequent CTC cell detection. Among them, the grayscale processing technology is a prior art, so it is not repeated here.

[0042] After obtaining the CTC fluorescence microscopic grayscale image, edge detection is performed on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image, including:

[0043] A double threshold of the CTC fluorescence microscopic grayscale image and the edge detection Canny algorithm is obtained, and edge detection is performed on the CTC fluorescence microscopic grayscale image using the Canny algorithm to obtain all edge pixels in the CTC fluorescence microscopic grayscale image.

[0044] It should be noted that, in one embodiment, the dual threshold of the Canny edge detection algorithm is selected as The double threshold can be adjusted according to the real-time scene and is not required. Thus, edge detection is performed on the CTC fluorescence microscopic grayscale image to obtain all edge pixels in the CTC fluorescence microscopic grayscale image.

[0045] Step S102, evaluating the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image based on the gradient information of edge pixels in the CTC fluorescence microscopic grayscale image, and obtaining the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image.

[0046] After obtaining all edge pixels in the CTC fluorescence microscopic grayscale image, it is necessary to analyze all edge pixels in the image to obtain the boundary degree corresponding to each edge pixel. Obtain the gradient direction and gradient amplitude of all edge pixels in the CTC fluorescence microscopic grayscale image, obtain the preset number of neighboring pixels of the edge pixel, and obtain the neighbor set of any edge pixel in all edge pixels in the CTC fluorescence microscopic grayscale image according to the number of neighboring pixels of the edge pixel; for any target edge pixel in the CTC fluorescence microscopic grayscale image, the cosine function mapping result of the gradient direction difference between the target edge pixel and any edge pixel in the neighbor set of the target edge pixel is used as the first gradient direction difference of the target edge pixel; the calculation result of subtracting the gradient amplitude of the target edge pixel from the mean of all gradient amplitudes in the neighbor set of the target edge pixel is used as the first gradient amplitude difference of the target edge pixel; the mean of the spatial distance difference between the target edge pixel and all edge pixels in the neighbor set of the target edge pixel is used as the first average distance of the target edge pixel;

[0047] The calculation result of multiplying the first gradient direction difference, the first gradient amplitude difference and the first average distance of the target edge pixel point is used as the first boundary evaluation, the normalized calculation result of the first boundary evaluation mean of the target edge pixel point and all edge pixels in the neighboring set corresponding to the target edge pixel point is used as the second boundary evaluation, and the calculation result of subtracting the constant 1 from the second boundary evaluation is used as the boundary degree of the target edge pixel point.

[0048] In one embodiment, assuming that The gradient value of the edge pixel is , No. The gradient direction of the edge pixel is , then the first The calculation expression corresponding to the boundary degree of edge pixels is:

[0049]

[0050] in, Indicates the first The boundary degree of edge pixels; Indicates the number of neighboring pixels of a preset edge pixel; Indicates the first A set of neighboring pixels of an edge pixel; Respectively represent The gradient direction of the edge pixel is The first edge pixel in the neighbor set The gradient direction of edge pixels; represents the cosine function; Indicates the first The first edge pixel in the neighbor set The gradient value of edge pixels; Indicates the first The gradient mean of the set of neighboring edge pixels of an edge pixel; Indicates the first The edge pixels and The first edge pixel in the neighbor set The spatial Euclidean distance between edge pixels; represents the normalization function; Represents a constant ; Indicates absolute value calculation.

[0051] It should be noted that in the existing fuzzy clustering process based on local information of pixels, when evaluating the membership of the target pixel during the membership iteration process of the clustering, the membership of the target data point is optimized by the distance between the neighborhood pixel of the target pixel and the center point of each cluster, so as to ensure that the pixels in the local range of the target pixel are divided into the same cluster with it during the CTC image segmentation process based on clustering. However, because the local information of the target pixel is only considered in the neighborhood range of the target pixel in the image in the fuzzy clustering process, when the internal information of a single cell is missing due to uneven fluorescence intensity in the current scene, the pixels in the cell cannot be accurately segmented. In response to this problem, in the fuzzy clustering process based on local information, it is necessary to obtain local information of the pixels within the boundary according to the boundary pixels of the cell, increase the range represented by the local in the cluster, and thus divide the pixels in a single cell into the same cluster. Therefore, it is necessary to first evaluate the boundary degree of the pixels in the CTC fluorescence microscopy image, and then optimize the boundary fitting process of the edge pixels in the image based on the boundary degree of the pixels, so as to avoid the erroneous evaluation caused by the influence of non-edge cell edge pixels in the process of obtaining the cell boundary through edge pixel fitting alone. That is, based on the consistency of the gradient direction between the edge pixels in the CTC fluorescence microscopy grayscale image and other edge pixels in its neighboring set. , the gradient consistency between the edge pixel and other edge pixels in its neighboring set The spatial distance between the edge pixel and other edge pixels in its neighboring set The boundary degree is evaluated. When the target edge pixel and the pixels in its neighboring edge pixel set show similar gradient directions, the boundary degree of the target edge pixel is higher. When the gradient strength of the neighboring set of the target edge pixel is more uniform, the boundary degree of the target edge pixel is higher. The higher the value, the lower the boundary degree of the edge pixel point. When the overall spatial distance between the target edge pixel point and other edge pixels in its neighboring set is higher, The higher the value, the lower the boundary degree of the edge pixel point. In one embodiment, the preset number of neighboring pixels of the edge pixel point is set to The number of neighboring pixels can be adjusted according to the actual scene and is not required.

[0052] Step S103, optimizing the cell boundary fitting process of the edge pixels in the CTC fluorescence microscopic grayscale image according to the boundary degree of the edge pixels in the CTC fluorescence microscopic grayscale image, and obtaining the boundary fitting result of the CTC fluorescence microscopic grayscale image.

[0053] After obtaining the boundary degree of the edge pixel point, the cell boundary fitting process of the edge pixel point in the CTC fluorescence microscopic grayscale image is optimized according to the boundary degree of each edge pixel point, and the boundary fitting result of the CTC fluorescence microscopic grayscale image is obtained. Specifically, all edge pixel points in the CTC fluorescence microscopic grayscale image are obtained, the boundary degrees corresponding to all edge pixel points are obtained, and the preset cell boundary ellipse fitting parameters are obtained, and the cell boundary ellipse fitting parameters include the ellipse fitting center, the ellipse fitting major axis length, and the ellipse fitting minor axis length; the boundary fitting objective function is:

[0054] ;

[0055] in, Indicates The boundary fitting objective function corresponding to the edge pixels; Indicates the number of neighboring pixels of the preset edge pixel; Indicates The first The boundary degree of edge pixels; Indicates The first The horizontal coordinate of the edge pixel in the image; Indicates the horizontal coordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates The first The vertical coordinate of the edge pixel in the image; Indicates the ordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates the length of the major axis of the ellipse preset in the boundary fitting process; Indicates the length of the minor axis of the ellipse preset in the boundary fitting process;

[0056] It should be noted that in the process of cell boundary fitting, each edge pixel is fitted through the edge pixels in its neighbor set. The first The boundary degree of an edge pixel point is the boundary degree of the pixel point obtained in the above-mentioned boundary degree obtaining process.

[0057] For the boundary fitting objective function, the coordinates of the center of the ellipse and the major and minor axis length parameters are jointly optimized by the gradient descent method to complete the fitting process, and all boundary fitting results in the CTC fluorescence microscopy grayscale image are obtained.

[0058] Step S104, optimizing the fuzzy factor evaluation range of the pixel points in the CTC fluorescence microscopic grayscale image according to the boundary fitting result of the CTC fluorescence microscopic grayscale image, and obtaining the optimized fuzzy factor evaluation range.

[0059] After obtaining all the boundary fitting results in the CTC fluorescence microscopic grayscale image, it is necessary to optimize the fuzzy factor evaluation range of the pixels in the CTC fluorescence microscopic grayscale image according to the boundary fitting results of the CTC fluorescence microscopic grayscale image to obtain the optimized fuzzy factor evaluation. Specifically, for the boundary fitting results of the CTC fluorescence microscopic grayscale image, the elliptical range formed by each boundary fitting result is taken as a cell area, and the pixels in the cell area are taken as a whole local range. For any pixel in the CTC fluorescence microscopic grayscale image, it is determined whether it is in any cell area. If the pixel is in any cell area, the cell area where the pixel is located is taken as the optimized evaluation range of the fuzzy factor corresponding to the pixel in the fuzzy clustering process. If the pixel is not in any cell area, the eight-neighborhood range of the pixel itself is taken as the optimized evaluation range of the fuzzy factor corresponding to the pixel in the fuzzy clustering process.

[0060] For any pixel point in the CTC fluorescence microscopic grayscale image, the optimized fuzzy factor evaluation is obtained by the overall difference between the pixel point in the optimized evaluation range of the fuzzy factor corresponding to the pixel point of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process.

[0061] The optimized fuzzy factor evaluation is obtained by the overall difference between the pixel points in the optimized evaluation range of the fuzzy factor corresponding to the pixel points of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process. Specifically, in one embodiment, the CTC fluorescence microscopic grayscale image has the pixel points in the optimized evaluation range of the fuzzy factor corresponding to the pixel points in the CTC fluorescence microscopic grayscale image. Pixels for the The calculation formula of the fuzzy factor of a cluster is:

[0062] ;

[0063] in, Indicates the CTC fluorescence microscopic grayscale image Pixels for the The fuzzy factor of each cluster; Indicates the CTC fluorescence microscopic grayscale image The optimized evaluation range of pixels; Indicates the CTC fluorescence microscopic grayscale image The pixel point and the optimal evaluation range of the pixel point The spatial Euclidean distance between pixels; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range Pixels for the The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range The pixel points are related to the The Euclidean distance between the grayscale values ​​of the cluster center points of the clusters.

[0064] It should be noted that when the CTC fluorescence microscopy grayscale image When the position of the pixel point in the image is within any boundary fitting result, then the pixel point in the CTC fluorescence microscopy grayscale image The optimal evaluation range of pixels is is the boundary range corresponding to the boundary fitting result corresponding to the pixel point. If the position of the pixel point in the image is not in any boundary fitting result, then the pixel point in the CTC fluorescence microscopy grayscale image The optimal evaluation range of pixels is is the eight-neighborhood range of the pixel.

[0065] Step S105, evaluating the objective function of the pixels in the fuzzy clustering process according to the fuzzy factor evaluation of the pixels in the CTC fluorescence microscopic grayscale image after optimization, and completing the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering according to the optimized objective function, and obtaining the preliminary segmentation result of the CTC fluorescence microscopic grayscale image; performing cell image processing according to the preliminary segmentation result of the CTC fluorescence microscopic image, and obtaining the CTC cell image detection result.

[0066] After obtaining the optimized fuzzy factor evaluation, it is necessary to evaluate the objective function of the pixel points in the fuzzy clustering process according to the optimized fuzzy factor evaluation of the pixel points in the CTC fluorescence microscopy grayscale image.

[0067] In one embodiment, the calculation formula of the objective function of the pixel point in the fuzzy clustering process is:

[0068]

[0069] in, represents the objective function of the clustering process of the CTC fluorescence microscopic grayscale image; Represents the number of pixels in the CTC fluorescence microscopy grayscale image; Indicates the preset number of clusters for the clustering process of the CTC fluorescence microscopic grayscale image; Indicates the CTC fluorescence microscopic grayscale image Pixel pair The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The pixel points are related to the The Euclidean distance of the gray values ​​between the cluster center points of the clusters; Indicates the CTC fluorescence microscopic grayscale image The pixel points in the fuzzy clustering process are Evaluation of optimized fuzzy factors for clusters;

[0070] It should be noted that in the objective function of the pixel points in the fuzzy clustering process, the fuzzy weighted index is a preset parameter. In this embodiment, the fuzzy weighted index is set to , this parameter setting can be adjusted according to the actual scenario and is not required.

[0071] According to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image.

[0072] After obtaining the preliminary segmentation result of the CTC fluorescence microscopic grayscale image, cell image processing can be performed according to the preliminary segmentation result of the CTC fluorescence microscopic image to obtain the CTC cell image detection result. Specifically, the preliminary segmentation result of the CTC fluorescence microscopic grayscale image is obtained, and the clusters corresponding to the CTC cells are obtained according to the segmentation results corresponding to each cluster in the preliminary segmentation result; the clusters corresponding to the CTC cells are flood-filled, and the final segmentation result is obtained by morphological operation processing to obtain the detection result of the CTC cell image.

[0073] It should be noted that flood filling and morphological operation processing are existing technologies, so they will not be described in detail.

[0074] In summary, the embodiment of the present invention obtains a CTC fluorescence microscopic image detected by a fluorescent probe method and converts it into a CTC fluorescence microscopic grayscale image; performs edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image; evaluates the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image through the gradient information of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image; optimizes the cell boundary fitting process of edge pixel points in the CTC fluorescence microscopic grayscale image through the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain The boundary fitting result of the CTC fluorescence microscopic grayscale image; according to the boundary fitting result of the CTC fluorescence microscopic grayscale image, the fuzzy factor evaluation range of the pixel points in the CTC fluorescence microscopic grayscale image is optimized to obtain the optimized fuzzy factor evaluation; according to the optimized fuzzy factor evaluation of the pixel points in the CTC fluorescence microscopic grayscale image, the objective function of the pixel points in the fuzzy clustering process is evaluated, and according to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image; according to the preliminary segmentation result of the CTC fluorescence microscopic image, cell image processing is performed to obtain the CTC cell image detection result. The cell boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image is evaluated, and then the cell boundary fitting is optimized according to the cell boundary degree of edge pixels to improve the accuracy of cell boundary fitting, and the local range used to evaluate the pixel fuzzy factor in the fuzzy clustering process is obtained according to the optimized fitting result, which is used to optimize the clustering of pixels in the cell boundary range when fuzzy clustering is performed on the CTC fluorescence microscopic grayscale image, so as to obtain a more accurate preliminary segmentation result of the CTC fluorescence microscopic image, thereby avoiding inaccurate segmentation of the CTC fluorescence microscopic image due to blurred cell boundaries and partial pixel loss caused by uneven fluorescence intensity, and improving the detection accuracy of the CTC fluorescence microscopic image.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A CTC image optimization detection method based on fluorescent probe detection, characterized in that: The method comprises: The method comprises the following steps: obtaining a CTC fluorescence microscopic image detected by a fluorescent probe method and converting it into a CTC fluorescence microscopic grayscale image; performing edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image; evaluating the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image by using the gradient information of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image; optimizing the cell boundary fitting process of edge pixel points in the CTC fluorescence microscopic grayscale image by using the boundary degree of edge pixel points in the CTC fluorescence microscopic grayscale image to obtain the CTC The boundary fitting result of the fluorescence microscopic grayscale image; according to the boundary fitting result of the CTC fluorescence microscopic grayscale image, the fuzzy factor evaluation range of the pixel points in the CTC fluorescence microscopic grayscale image is optimized to obtain the optimized fuzzy factor evaluation; according to the optimized fuzzy factor evaluation of the pixel points in the CTC fluorescence microscopic grayscale image, the objective function of the pixel points in the fuzzy clustering process is evaluated, and according to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image; according to the preliminary segmentation result of the CTC fluorescence microscopic image, cell image processing is performed to obtain the CTC cell image detection result.

2. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that: The performing edge detection on the CTC fluorescence microscopic grayscale image to obtain all edge pixel points in the CTC fluorescence microscopic grayscale image includes: A double threshold of the CTC fluorescence microscopic grayscale image and the edge detection Canny algorithm is obtained, and edge detection is performed on the CTC fluorescence microscopic grayscale image using the Canny algorithm to obtain all edge pixels in the CTC fluorescence microscopic grayscale image.

3. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that, The step of evaluating the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image by using the gradient information of edge pixels in the CTC fluorescence microscopic grayscale image to obtain the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image includes: Obtain the gradient direction and gradient amplitude of all edge pixels in the CTC fluorescence microscopic grayscale image, obtain a preset number of neighboring pixels of the edge pixel, and obtain a neighbor set of any edge pixel among all edge pixels in the CTC fluorescence microscopic grayscale image according to the number of neighboring pixels of the edge pixel; for any target edge pixel in the CTC fluorescence microscopic grayscale image, use the cosine function mapping result of the gradient direction difference between the target edge pixel and any edge pixel in the neighbor set of the target edge pixel as the first gradient direction difference of the target edge pixel; use the calculation result of subtracting the gradient amplitude of the target edge pixel from the mean of all gradient amplitudes in the neighbor set of the target edge pixel as the first gradient amplitude difference of the target edge pixel; use the mean of the spatial distance difference between the target edge pixel and all edge pixels in the neighbor set of the target edge pixel as the first average distance of the target edge pixel; The calculation result of multiplying the first gradient direction difference, the first gradient amplitude difference and the first average distance of the target edge pixel point is used as the first boundary evaluation, the normalized calculation result of the first boundary evaluation mean of the target edge pixel point and all edge pixels in the neighboring set corresponding to the target edge pixel point is used as the second boundary evaluation, and the calculation result of subtracting the constant 1 from the second boundary evaluation is used as the boundary degree of the target edge pixel point.

4. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that: The step of optimizing the cell boundary fitting process of edge pixels in the CTC fluorescence microscopic grayscale image by the boundary degree of edge pixels in the CTC fluorescence microscopic grayscale image to obtain the boundary fitting result of the CTC fluorescence microscopic grayscale image includes: All edge pixels in the CTC fluorescence microscopic grayscale image are obtained, the boundary degrees corresponding to all edge pixels are obtained, and preset cell boundary ellipse fitting parameters are obtained, wherein the cell boundary ellipse fitting parameters include the ellipse fitting center, the ellipse fitting major axis length, and the ellipse fitting minor axis length; the boundary fitting objective function is: ； in, Indicates The boundary fitting objective function corresponding to the edge pixels; Indicates the number of neighboring pixels of the preset edge pixel; Indicates The first The boundary degree of edge pixels; Indicates The first The horizontal coordinate of the edge pixel in the image; Indicates the horizontal coordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates The first The vertical coordinate of the edge pixel in the image; Indicates the ordinate of the center of the ellipse fitting circle preset in the boundary fitting process; Indicates the length of the major axis of the ellipse preset in the boundary fitting process; Indicates the length of the minor axis of the ellipse preset in the boundary fitting process; For the boundary fitting objective function, the coordinates of the center of the ellipse and the major and minor axis length parameters are jointly optimized by the gradient descent method to complete the fitting process, and all boundary fitting results in the CTC fluorescence microscopy grayscale image are obtained.

5. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that: The step of optimizing the fuzzy factor evaluation range of the pixel points in the CTC fluorescence microscopic grayscale image according to the boundary fitting result of the CTC fluorescence microscopic grayscale image to obtain the optimized fuzzy factor evaluation includes: For the boundary fitting results of the CTC fluorescence microscopic grayscale image, the elliptical range formed by each boundary fitting result is taken as a cell region, and the pixel points in the cell region are taken as a whole local range. For any pixel point in the CTC fluorescence microscopic grayscale image, it is judged whether it is in any cell region. If the pixel point is in any cell region, the cell region where the pixel point is located is used as the optimized evaluation range of the fuzzy factor corresponding to the pixel point in the fuzzy clustering process. If the pixel point is not in any cell region, the eight-neighborhood range of the pixel point itself is used as the optimized evaluation range of the fuzzy factor corresponding to the pixel point in the fuzzy clustering process. For any pixel point in the CTC fluorescence microscopic grayscale image, the optimized fuzzy factor evaluation is obtained by the overall difference between the pixel point in the optimized evaluation range of the fuzzy factor corresponding to the pixel point of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process.

6. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that: The objective function of the pixels in the fuzzy clustering process is evaluated according to the fuzzy factor evaluation after the pixel in the CTC fluorescence microscopic grayscale image is optimized, and the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed according to the optimized objective function to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image, including: The calculation formula of the objective function of the pixel point in the fuzzy clustering process is: ； in, represents the objective function of the clustering process of the CTC fluorescence microscopic grayscale image; Represents the number of pixels in the CTC fluorescence microscopy grayscale image; Indicates the preset number of clusters for the clustering process of the CTC fluorescence microscopic grayscale image; Indicates the CTC fluorescence microscopic grayscale image Pixel pair The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The pixel points are related to the The Euclidean distance of the gray values ​​between the cluster center points of the clusters; Indicates the CTC fluorescence microscopic grayscale image The pixel points in the fuzzy clustering process are Evaluation of optimized fuzzy factors for clusters; According to the optimized objective function, the CTC fluorescence microscopic grayscale image segmentation based on fuzzy clustering is completed to obtain the preliminary segmentation result of the CTC fluorescence microscopic grayscale image.

7. The CTC image optimization detection method based on fluorescent probe detection according to claim 1, characterized in that: The performing cell image processing according to the preliminary segmentation result of the CTC fluorescence microscopy image to obtain the CTC cell image detection result includes: Obtain a preliminary segmentation result of the CTC fluorescence microscopic grayscale image, and obtain the clusters corresponding to the CTC cells according to the segmentation results corresponding to each cluster in the preliminary segmentation result; perform flood filling on the clusters corresponding to the CTC cells, perform morphological operation processing to obtain the final segmentation result, and obtain the detection result of the CTC cell image.

8. The CTC image optimization detection method based on fluorescent probe detection according to claim 5, characterized in that: The optimized fuzzy factor evaluation is obtained by the overall difference between the pixel points in the optimized evaluation range of the fuzzy factor corresponding to the pixel points of the CTC fluorescence microscopic grayscale image and the target cluster center point in the clustering process, including: When any pixel in the CTC fluorescence microscopic grayscale image is in any cell region, the calculation formula for the fuzzy factor of any pixel in the CTC fluorescence microscopic grayscale image is: ； in, Indicates the CTC fluorescence microscopic grayscale image Pixels for the The fuzzy factor of each cluster; Indicates the CTC fluorescence microscopic grayscale image The optimized evaluation range of pixels; Indicates the CTC fluorescence microscopic grayscale image The pixel point and the optimal evaluation range of the pixel point The spatial Euclidean distance between pixels; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range Pixels for the The degree of membership of each cluster; represents the fuzzy weighted index in the fuzzy clustering process; Indicates the CTC fluorescence microscopic grayscale image The first pixel in the optimization evaluation range The pixel points are related to the The Euclidean distance between the grayscale values ​​of the cluster center points of the clusters.

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