WO2021217851A1 - Abnormal cell automatic labeling method and apparatus, electronic device, and storage medium - Google Patents

Abstract

The present application relates to an image processing technique, being applied to the field of smart medical care. Disclosed is an abnormal cell automatic labeling method, comprising: performing Gaussian convolution smoothing processing on a cell pathology picture to obtain an image pyramid; performing detection and fitting on the image pyramid by means of a Hough transform circle detection method so as to obtain a low-power fitting region of interest; mapping the low-power fitting region of interest to an obtained cell pathology picture so as to generate a fitting high-power image; segmenting a region of interest in the fitting high-power image to generate a segmented high-power image; labeling and cutting the segmented high-power image by means of an adaptive threshold segmentation algorithm to obtain an abnormal cell labeling set; and mapping the abnormal cell labeling set to the cell pathology picture to obtain an abnormal cell labeling picture. The present application improves the accuracy of abnormal cell labeling, and reduces the calculation and storage pressure. In addition, also involved is a blockchain technique, and the abnormal cell labeling set may be stored in a blockchain.

Description

Method, device, electronic equipment and storage medium for automatic labeling of abnormal cells

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on April 27, 2020, the application number is 202010348583.2, and the invention title is "Anomalous Cell Automatic Labeling Method, Device, Electronic Equipment, and Storage Medium", and the entire content is approved The reference is incorporated in the application.

Technical field

This application relates to image processing technology, which is applied in the field of smart medical treatment, and particularly relates to methods, devices, electronic equipment, and readable storage media for automatic labeling of abnormal cells.

Background technique

At present, due to the lack of pathologists and cytological testing equipment, various artificial intelligence-assisted screening equipment systems are gradually appearing. There are also a large number of methods for feature extraction and training to detect abnormal cells using deep learning neural networks on the market, but the inventor Realize that the existing methods still require a large number of pathologists to label the pathological data, which increases the labor intensity of the pathologists. At the same time, the detection method of abnormal cells in the neural network requires a large amount of pathological data, so the computational pressure and storage performance of the computer are Both are challenging, so an automatic labeling method for abnormal cells is needed to improve the accuracy of abnormal cell labeling and reduce the pressure of calculation and storage.

Summary of the invention

The present application provides a method, device, electronic equipment, and computer-readable storage medium for automatic labeling of abnormal cells, the main purpose of which is to improve the accuracy of labeling abnormal cells and reduce the pressure of calculation and storage.

In order to achieve the above-mentioned purpose, an automatic labeling method for abnormal cells provided by this application includes:

Acquiring a cytopathological picture, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture;

Detecting and fitting the image pyramid to obtain a low-power fitting region of interest;

Mapping the low-power fitting region of interest to the acquired cytopathological picture and performing an image magnification operation to generate a fitting high-power image, and acquiring all image coordinates of the region of interest in the fitted high-power image;

Segmenting the region of interest in the fitted high-magnification image by using the image coordinates to generate a segmented high-magnification image;

Annotate and cut the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells;

The abnormal cell annotation set is mapped to the cytopathological picture to obtain an abnormal cell annotation map.

In order to solve the above-mentioned problems, the present application also provides an electronic device, wherein the electronic device includes:

At least one processor; and,

A memory communicatively connected with the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for automatically labeling abnormal cells as described below:

Acquiring a cytopathological picture, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture;

Detecting and fitting the image pyramid to obtain a low-power fitting region of interest;

Mapping the low-power fitting region of interest to the acquired cytopathological picture and performing an image magnification operation to generate a fitting high-power image, and acquiring all image coordinates of the region of interest in the fitted high-power image;

Segmenting the region of interest in the fitted high-magnification image by using the image coordinates to generate a segmented high-magnification image;

Annotate and cut the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells;

The abnormal cell annotation set is mapped to the cytopathological picture to obtain an abnormal cell annotation map.

In order to solve the above problems, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an abnormal cell automatic labeling program, and the abnormal cell automatic labeling program can be used by one or more processors. Execute to realize the steps of the method for automatic labeling of abnormal cells as described below:

The image pyramid generating step is used to obtain a cytopathological picture, perform Gaussian convolution smoothing for a preset number of times on the cytopathological picture, generate multiple cytopathological pictures, and obtain an image pyramid according to the cytopathological picture;

The detection and fitting step is used to detect and fit the image pyramid to obtain a low-power fitting region of interest;

The mapping and segmentation step is used to map the low-power fitted region of interest to the acquired cytopathological picture and perform image magnification operations to generate a fitted high-power image, and obtain the region of interest in the fitted high-power image And then use the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image;

An annotation cutting step is used for annotating and cutting the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set;

The abnormal generation step is used to map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

In order to solve the above problems, the present application also provides an automatic labeling device for abnormal cells, wherein the device includes:

The image pyramid generating module is used to obtain a cytopathological picture, perform Gaussian convolution smoothing for a preset number of times on the cytopathological picture, generate multiple cytopathological pictures, and obtain an image pyramid according to the cytopathological picture;

The detection and fitting module is used to detect and fit the image pyramid to obtain a low-power fitting region of interest;

The mapping and segmentation module is used to map the low-power fitted region of interest to the acquired cytopathological picture and perform image magnification operations to generate a fitted high-power image, and obtain the region of interest in the fitted high-power image And then use the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image;

An annotation cutting module, which is used to annotate and cut the segmented high-magnification image through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set;

The abnormal generation module is used to map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

In the embodiment of the application, the original cell pathological pictures are smoothed by Gaussian convolution to obtain an image pyramid composed of multiple cell case pictures with higher and higher resolution from top to bottom. The pathological data features in these cell case pictures are more obvious , Improve the accuracy of abnormal cell annotation; further, because the resolution of the image pixels in the image pyramid is very large, the general image processing method cannot directly use the computer for image analysis processing, so the embodiment of the application fits the pixels to The low-magnification area reduces the pixel and resolution of the image, and further releases the storage pressure of the computer while not affecting the accuracy of subsequent abnormal cell detection; in addition, when the low-magnification fitting area of interest is obtained, the embodiments of the present application further By zooming in, it becomes a fitting high-magnification image. The high-magnification image has a high resolution, which can effectively improve the accuracy of the algorithm for labeling and cutting. Therefore, the method, device, electronic device, and computer-readable storage medium for automatic labeling of abnormal cells proposed in this application can solve the problems of low accuracy of abnormal cell labeling and high pressure of calculation and storage.

Description of the drawings

FIG. 1 is a schematic flowchart of an automatic labeling method for abnormal cells provided by an embodiment of the application;

2 is a schematic diagram of modules of an automatic labeling device for abnormal cells provided by an embodiment of the application;

3 is a schematic diagram of the internal structure of an electronic device for executing the method for automatically labeling abnormal cells according to an embodiment of the application;

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Detailed ways

In order to make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the various embodiments of the present application will be described in detail below with reference to the accompanying drawings. However, a person of ordinary skill in the art can understand that in each embodiment of the present application, many technical details are proposed for the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solution claimed in this application can be realized.

This solution can be applied in the field of smart medical care to promote the construction of smart cities. The purpose of the embodiments of this application is to label abnormal cells through adaptive threshold segmentation, which is used to improve the accuracy of abnormal cell labeling and reduce the pressure of calculation and storage. For example, in the process of cancer cell screening, the cytopathological pictures are stratified to make the data characteristics after stratification more obvious. The abnormal cells in the stratified cytopathological pictures are marked, and the abnormal cells are mapped through coordinate mapping. The method maps the abnormal cells in the low-pixel image in the cervical cytopathological picture to the high-pixel image for subsequent abnormal cell labeling and segmentation.

The implementation details of the automatic labeling method for abnormal cells of this embodiment will be specifically described below. The following content is only provided for ease of understanding and is not necessary for implementing this solution.

Referring to FIG. 1, it is a schematic flowchart of a method for automatically labeling abnormal cells provided by an embodiment of this application. The method can be executed by a device, and the device can be implemented by software and/or hardware.

In this embodiment, the method for automatically labeling abnormal cells includes:

S1. Acquire a cytopathological picture, perform Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtain an image pyramid based on the cytopathological picture.

For example, in the field of cervical cancer screening, the cytopathological pictures described in the embodiments of the present application include cervical cytopathological pictures.

In detail, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture, includes:

Step A: Perform an enlargement operation on the acquired cytopathological pictures to obtain a group a and b layer images, where the initial values of a and b are both 1, and the a group and b layer images are Gaussian rolls The product function performs the smoothing operation of Gaussian convolution to obtain the b+1th layer image of the ath group, where the Gaussian convolution function is:

Where σ represents the smoothing factor, G _{(x, y, σ)} represents the convolution of x and y, and x and y represent the image coordinates;

Step B: Multiply the smoothing factor σ by the scale factor k to obtain a new smoothing factor kσ, and use the Gaussian convolution function to Gaussian the a-th group and the b+1th layer image through the new smoothing factor kσ Convolutional smoothing operation to obtain the b+2th layer image of the ath group, and repeat this step until the Lth layer image of the ath group is obtained, where L is a predefined value;

Step C: Perform a down-sampling operation on the a-th group of L-th layer images to obtain the a+1-th group of b-th layer images, and perform Gaussian convolution on the a+1-th group and b-th layer images using the Gaussian convolution function described above Product smoothing operation to obtain the a+1th group of b+1th layer images, and repeat the step B until the a+1th group of Lth layer images are obtained;

Step D: Perform the loop operation of Step C and Step B on the result obtained in Step C, until the O-th group of L-th layer images is obtained, where O is a predefined value;

Step E: Combine images from the a-th group and b-th layer images to the O-th group and L-th layer images to generate the image pyramid. Wherein, each image from the a-th group of b-th layer images to the O-th group of L-th layer images is a picture layer of the image pyramid.

After smoothing by Gaussian convolution, the cytopathological picture can be split into multiple cytopathological pictures with higher and higher resolution from top to bottom, which makes the characteristics of the pathological data more obvious and improves the accuracy of abnormal cell labeling.

S2. Perform detection and fitting of the image pyramid through the Hough transform circle detection method to obtain a low-power fitting region of interest.

In detail, the S2 includes: performing gray-scale conversion of the image pyramid to generate a gray-scale image; performing binarization processing on the gray-scale image to obtain a contour image; and adopting a random selection method from the contour image , Obtain the candidate region; use the Hough circle transform detection method to identify the region of interest in the candidate region, and fit the region of interest to obtain the low-power fitting region of interest.

Further, the using the Hough circle transform detection method to identify the region of interest in the candidate region, and fitting the region of interest to obtain the low-power fitting region of interest includes:

Step a: Detect the edge of the candidate area in the image space of the candidate area by using an edge detection algorithm to obtain n edge pixel point sets;

Step b. Map the n edge pixel point sets to the parameter space with a predefined value r as a radius:

Where r is a predefined value, θ∈[0,2π), x and y represent the corresponding coordinates (x, y) of the n edge pixel point sets, and (a, b) represent the reference point in the parameter space coordinate;

Step c. Count all the coordinate points in the parameter space, and traverse θ, so that when the edge pixels in the image space are mapped to a circle in the parameter space, the circle is regarded as the Hough circle, and the Hough circle is passed through the Hough circle. The circle constitutes the region of interest;

Step d: Fitting the region of interest to generate a low-power fitting region of interest.

Through the embodiments of the present application, the region of interest in the cytopathological picture can be arbitrarily marked by the doctor, and then identified and fitted by the Hough circle transformation detection method.

S3. Map the low-power fitted region of interest to the acquired cytopathological picture and perform an image magnification operation to generate a fitted high-power image, and obtain all image coordinates of the region of interest in the fitted high-power image .

In the embodiment of this application, the high-magnification image size of the cytopathological picture is very large, and the general image processing method is difficult to load for direct processing, and it is impossible to directly use a computer for image analysis and processing. Therefore, the embodiment of this application adopts the low-magnification The fitted region of interest is mapped to the acquired high-magnification image, a fitted high-magnification image is generated, and the image coordinate information of the region of interest in the fitted high-magnification image is obtained. The image is segmented.

In the embodiment of the present application, the mapping is to divide the low-resolution image into multiple sub-regions, and add each sub-region to the high-resolution image by multiplying the coordinates of the sub-regions and a multiple, and the multiple is the high-resolution image. The magnification of a low-resolution image relative to a low-resolution image.

S4. Using the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image.

This application implements all the image coordinates of the region of interest acquired by fitting, and the cell region of interest can be segmented to generate a segmented high-magnification image with a size of, for example, 3000x3000.

S5. Perform labeling and cutting on the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an abnormal cell label set.

Specifically, the S5 includes:

Step I: Pass each pixel i of the segmented high-magnification image through the formula:

Convert it to a real number between 0-1 and get the normalized value h;

Step II: Perform a pre-compensation calculation on the normalized value h by using a pre-defined gamma correction compensation value to obtain a pre-compensation constant value f;

Step III: Perform denormalization calculation on the pre-compensation constant value f through the formula f*256-0.5 to obtain the corrected image of the segmented high-magnification image;

Step IV. The image gray level of the corrected image and the pixel point gray level of the coordinate in the corrected image are formed into a two-tuple, the mean value and variance of all the two-tuples are calculated, and the two-dimensionality is established by the mean value and the variance A maximum between-cluster variance model, calculating the two-dimensional maximum between-cluster variance model through an adaptive particle clustering algorithm to generate the optimal threshold of the corrected image;

Step V, segment the corrected image by using the optimal threshold to generate a background and foreground segmented image;

Step VI: Perform opening and closing operations on the background and foreground segmented images to generate an abnormal cell annotation set.

In the embodiment of the present application, the background and foreground segmented images are sequentially opened and calculated through a morphological algorithm, and isolated small points in the segmented images are removed to generate a first image set, and the first image set is The morphological algorithm is used to sequentially perform closed calculations to fill in the small cracks between the image cells in the first image set, and clear the holes in the images in the first image set, so that the first image sets the foreground cells and all the cells in the first image set. The background area in the first image set is separated to generate an abnormal cell annotation set.

Traditional cell image segmentation methods are roughly divided into two categories: region-based segmentation methods and edge-based segmentation methods. The basic principle of region-based segmentation methods is to achieve segmentation by classifying adjacent regions with similar characteristics into one category. The edge-based segmentation method generally uses the gray level or the structure with a sudden change as the edge to perform segmentation. This application adopts the edge-based segmentation method to perform image segmentation through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set.

S6. Map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

In the embodiment of the present application, the abnormal cell annotation set is used to locate and identify target cells through a deep learning method to generate an abnormal cell annotation map. If the edge of the abnormal cell annotation map is jagged, the edge smoothing method is used to perform Edge adjustment.

It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned abnormal cell annotation set, the above-mentioned abnormal cell annotation set may also be stored in a node of a blockchain.

As shown in Figure 2, it is a functional block diagram of the device for automatic labeling of abnormal cells of the present application.

The apparatus 100 for automatically labeling abnormal cells described in the embodiment of the present application may be installed in an electronic device. According to the implemented functions, the device for automatically labeling abnormal cells may include a detection and fitting module 101, a mapping segmentation module 102, a labeling and cutting module 103, and an abnormality generation module 104. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The image pyramid generating module 101 is configured to obtain a cytopathological picture, perform Gaussian convolution smoothing processing for a preset number of times on the cytopathological picture, generate multiple cytopathological pictures, and obtain an image pyramid according to the cytopathological picture;

The detection and fitting module 102 is configured to detect and fit the image pyramid by the Hough transform circle detection method to obtain a low-power fitting region of interest;

The mapping and segmentation module 103 is configured to map the low-power fitting region of interest to the acquired cytopathological picture and perform an image magnification operation to generate a fitting high-power image, and acquire the interest in the fitted high-power image All the image coordinates of the region, and then use the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image;

The annotation cutting module 104 is configured to perform annotation cutting on the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set;

The abnormal generation module 105 is configured to map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

In detail, the specific implementation steps of each module of the abnormal cell automatic labeling device are as follows:

The image pyramid generating module 101 obtains a cytopathological picture, performs a preset number of Gaussian convolution smoothing processing on the cytopathological picture, generates a plurality of cytopathological pictures, and obtains an image pyramid according to the cytopathological picture.

For example, in the field of cervical cancer screening, the cytopathological pictures described in the embodiments of the present application include cervical cytopathological pictures.

In detail, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture, includes:

Step A: Perform an enlargement operation on the acquired cytopathological pictures to obtain a group a and b layer images, wherein the initial values of a and b are both 1, and the a group and b layer images are Gaussian rolls The product function performs the smoothing operation of Gaussian convolution to obtain the b+1th layer image of the ath group, where the Gaussian convolution function is:

Where σ represents the smoothing factor, G _{(x, y, σ)} represents the convolution of x and y, and x and y represent the image coordinates;

Step B: Multiply the smoothing factor σ by the scale factor k to obtain a new smoothing factor kσ, and use the Gaussian convolution function to Gaussian the a-th group and the b+1th layer image through the new smoothing factor kσ Convolutional smoothing operation to obtain the b+2th layer image of the ath group, and repeat this step until the Lth layer image of the ath group is obtained, where L is a predefined value;

Step C: Perform a down-sampling operation on the a-th group of L-th layer images to obtain the a+1-th group of b-th layer images, and perform Gaussian convolution on the a+1-th group and b-th layer images using the Gaussian convolution function described above Product smoothing operation to obtain the a+1th group of b+1th layer images, and repeat the step B until the a+1th group of Lth layer images are obtained;

Step D: Perform the loop operation of Step C and Step B on the result obtained in Step C, until the O-th group of L-th layer images is obtained, where O is a predefined value;

Step E: Combine images from the a-th group and b-th layer images to the O-th group and L-th layer images to generate the image pyramid. Wherein, each image from the a-th group of b-th layer images to the O-th group of L-th layer images is a picture layer of the image pyramid.

After smoothing by Gaussian convolution, the cytopathological picture can be split into multiple cytopathological pictures with higher and higher resolution from top to bottom, which makes the characteristics of the pathological data more obvious and improves the accuracy of abnormal cell labeling.

The detection and fitting module 102 detects and fits the image pyramid by a Hough transform circle detection method to obtain a low-power fitting region of interest.

In detail, the detection and fitting module 102: converts the image pyramid to grayscale to generate a grayscale image; performs binarization processing on the grayscale image to obtain a contour image; adopts random from the contour image The selected method is used to obtain the candidate region; the Hough circle transform detection method is used to identify the region of interest in the candidate region, and the region of interest is fitted to obtain the low-power fitting region of interest.

Further, the using the Hough circle transform detection method to identify the region of interest in the candidate region, and fitting the region of interest to obtain the low-power fitting region of interest includes:

Step a: Detect the edge of the candidate area in the image space of the candidate area by using an edge detection algorithm to obtain n edge pixel point sets;

Step b. Map the n edge pixel point sets to the parameter space with a predefined value r as a radius:

Where r is a predefined value, θ∈[0,2π), x and y represent the corresponding coordinates (x, y) of the n edge pixel point sets, and (a, b) represent the reference point in the parameter space coordinate;

Step c. Count all the coordinate points in the parameter space, and traverse θ, so that when the edge pixels in the image space are mapped to the parameter space as a circle, the circle is regarded as the Hough circle, and the Hough circle is passed through the Hough circle. The circle constitutes the region of interest;

Step d: Fitting the region of interest to generate a low-power fitting region of interest.

Through the embodiments of the present application, the region of interest in the cytopathological picture can be arbitrarily marked by the doctor, and then identified and fitted by the Hough circle transformation detection method.

The mapping and segmentation module 103 maps the low-power fitting region of interest to the acquired cytopathological picture and performs an image magnification operation to generate a fitting high-power image, and acquire the interest in the fitted high-power image All image coordinates of the area.

In the embodiment of the application, the high-magnification image size of the cytopathological picture is very large, and the general image processing method is difficult to load for direct processing, and it is impossible to directly use the computer for image analysis and processing. Therefore, the embodiment of the application adopts the low-magnification The fitted region of interest is mapped to the acquired high-magnification image, a fitted high-magnification image is generated, and the image coordinate information of the region of interest in the fitted high-magnification image is obtained. The image is segmented.

In the embodiment of the present application, the mapping is to divide the low-resolution image into multiple sub-regions, and add each sub-region to the high-resolution image by multiplying the coordinates of the sub-regions and a multiple, and the multiple is the high-resolution image. The magnification of a low-resolution image relative to a low-resolution image. Further, the mapping segmentation module 103 uses the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image.

This application implements all the image coordinates of the region of interest acquired by fitting, and the cell region of interest can be segmented to generate a segmented high-magnification image with a size of, for example, 3000x3000.

The labeling and cutting module 104 performs labeling and cutting on the segmented high-magnification image through an adaptive threshold segmentation algorithm to obtain an abnormal cell labeling set.

In detail, the labeling and cutting of the segmented high-magnification image to obtain an abnormal cell labeling set includes:

Step I: Pass each pixel i of the segmented high-magnification image through the formula:

Convert it to a real number between 0-1 and get the normalized value h;

Step II: Perform a pre-compensation calculation on the normalized value h by using a pre-defined gamma correction compensation value to obtain a pre-compensation constant value f;

Step III: Perform denormalization calculation on the pre-compensation constant value f through the formula f*256-0.5 to obtain the corrected image of the segmented high-magnification image;

Step IV. The image gray level of the corrected image and the pixel point gray level of the coordinate in the corrected image are formed into a two-tuple, the mean value and variance of all the two-tuples are calculated, and the two-dimensionality is established by the mean value and the variance A maximum between-cluster variance model, calculating the two-dimensional maximum between-cluster variance model through an adaptive particle clustering algorithm to generate the optimal threshold of the corrected image;

Step V, segment the corrected image by using the optimal threshold to generate a background and foreground segmented image;

Step VI: Perform opening and closing operations on the background and foreground segmented images to generate an abnormal cell annotation set.

In the embodiment of the present application, the background and foreground segmented images are sequentially opened and calculated through a morphological algorithm, and isolated small points in the segmented images are removed to generate a first image set, and the first image set is The morphological algorithm is used to perform closed calculations in sequence to fill in the small cracks between the image cells in the first image set, and clear the holes in the images in the first image set, so that the first image sets the foreground cells and all the cells in the first image set. The background area in the first image set is separated to generate an abnormal cell annotation set.

Traditional cell image segmentation methods are roughly divided into two categories: region-based segmentation methods and edge-based segmentation methods. The basic principle of region-based segmentation methods is to achieve segmentation by classifying adjacent regions with similar characteristics into one category. The edge-based segmentation method generally uses the gray level or the structure with a sudden change as the edge to perform segmentation. This application adopts the edge-based segmentation method to perform image segmentation through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set.

The abnormal generation module 105 maps the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

In the embodiment of the present application, the abnormal cell annotation set is used to locate and identify target cells through a deep learning method to generate an abnormal cell annotation map. If the edge of the abnormal cell annotation map is jagged, the edge smoothing method is used to perform Edge adjustment.

It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned abnormal cell annotation set, the above-mentioned abnormal cell annotation set may also be stored in a node of a blockchain.

As shown in FIG. 3, it is a schematic structural diagram of an electronic device that implements the method for automatically labeling abnormal cells of the present application.

The electronic device 1 may include a processor 10, a memory 11, and a bus, and may also include a computer program stored in the memory 11 and running on the processor 10, such as an abnormal cell automatic labeling program 12.

Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, CD etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, for example, a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), and a secure digital (Secure Digital) equipped on the electronic device 1. , SD) card, flash card (Flash Card), etc. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software and various types of data installed in the electronic device 1, such as codes for an automatic labeling program for abnormal cells, etc., but also to temporarily store data that has been output or will be output.

The processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or more Combinations of central processing unit (CPU), microprocessor, digital processing chip, graphics processor, and various control chips, etc. The processor 10 is the control unit of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules stored in the memory 11 (such as executing Automatic labeling program for abnormal cells, etc.), and call the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

The bus may be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to implement connection and communication between the memory 11 and at least one processor 10 and the like.

FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure. Components, or a combination of certain components, or different component arrangements.

For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, thereby controlling power The device implements functions such as charge management, discharge management, and power consumption management. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

Further, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.

Optionally, the electronic device 1 may also include a user interface. The user interface may be a display (Display) and an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visualized user interface.

It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

The abnormal cell automatic labeling program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:

Acquiring a cytopathological picture, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture;

Detecting and fitting the image pyramid through the Hough transform circle detection method to obtain a low-power fitting region of interest;

Mapping the low-power fitting region of interest to the acquired cytopathological picture and performing an image magnification operation to generate a fitting high-power image, and acquiring all image coordinates of the region of interest in the fitting high-power image;

Segmenting the region of interest in the fitted high-magnification image by using the image coordinates to generate a segmented high-magnification image;

Annotate and cut the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells;

The abnormal cell annotation set is mapped to the cytopathological picture to obtain an abnormal cell annotation map.

Specifically, for the specific implementation method of the above-mentioned instructions by the processor 10, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1, which will not be repeated here.

Further, if the integrated module/unit of the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium may be non-volatile or volatile. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) ).

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, an application program required by at least one function, etc.; the storage data area may store Data created by the use of nodes, etc.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

In the several embodiments provided in this application, it should be understood that the disclosed equipment, device, and method may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application.

Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any associated diagram marks in the claims should not be regarded as limiting the claims involved.

In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The second class words are used to indicate names, and do not indicate any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims (20)

1. An automatic labeling method for abnormal cells, wherein the method includes:

   Acquiring a cytopathological picture, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture;

   Detecting and fitting the image pyramid to obtain a low-power fitting region of interest;

   Mapping the low-power fitting region of interest to the acquired cytopathological picture and performing an image magnification operation to generate a fitting high-power image, and acquiring all image coordinates of the region of interest in the fitted high-power image;

   Segmenting the region of interest in the fitted high-magnification image by using the image coordinates to generate a segmented high-magnification image;

   Annotate and cut the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells;

   The abnormal cell annotation set is mapped to the cytopathological picture to obtain an abnormal cell annotation map.
2. The method for automatically labeling abnormal cells according to claim 1, wherein the cytopathological picture is subjected to Gaussian convolution smoothing for a preset number of times to generate a plurality of cytopathological pictures, and an image pyramid is obtained from the cytopathological pictures ,include:

   Step A: Perform an enlargement operation on the acquired cytopathological pictures to obtain a group a and b layer images, wherein the initial values of a and b are both 1, and the a group and b layer images are Gaussian rolls The product function performs the smoothing operation of Gaussian convolution to obtain the b+1th layer image of the ath group, where the Gaussian convolution function is:

   

   Where σ represents the smoothing factor, G _{(x, y, σ)} represents the convolution of x and y, and x and y represent the image coordinates;

   Step B: Multiply the smoothing factor σ by the scale factor k to obtain a new smoothing factor kσ, and use the Gaussian convolution function to Gaussian the a-th group and the b+1th layer image through the new smoothing factor kσ Convolutional smoothing operation to obtain the b+2th layer image of the ath group, and repeat this step until the Lth layer image of the ath group is obtained, where L is a predefined value;

   Step C: Perform a down-sampling operation on the a-th group of L-th layer images to obtain the a+1-th group of b-th layer images, and perform Gaussian convolution on the a+1-th group and b-th layer images using the Gaussian convolution function described above Product smoothing operation to obtain the a+1th group of b+1th layer images, and repeat the step B until the a+1th group of Lth layer images are obtained;

   Step D: Perform the loop operation of Step C and Step B on the result obtained in Step C, until the O-th group of L-th layer images is obtained, where O is a predefined value;

   Step E: Combine images from the a-th group and b-th layer images to the O-th group and L-th layer images to generate the image pyramid.
3. The method for automatically labeling abnormal cells according to claim 1, wherein the detecting and fitting of the image pyramid by the Hough transform circle detection method to obtain the low-power fitting region of interest comprises:

   Converting the image pyramid to grayscale to generate a grayscale image;

   Performing binarization processing on the grayscale image to obtain a contour image;

   Using a method of random selection from the contour image to obtain a candidate area;

   The Hough circle transform detection method is used to identify the region of interest in the candidate region, and fit the region of interest to obtain the low-power fitting region of interest.
4. The method for automatically labeling abnormal cells according to claim 3, wherein said identifying and fitting the region of interest using the Hough transform circle detection method to obtain the low-power fitting region of interest comprises:

   Detecting the edge of the candidate area in the image space of the candidate area by using an edge detection algorithm to obtain n edge pixel point sets;

   Map the n edge pixel point sets to the parameter space with the predefined value r as the radius:

   

   Where r is a predefined value, θ∈[0,2π), x and y represent the corresponding coordinates (x, y) of the n edge pixel point sets, and (a, b) represent the reference point in the parameter space coordinate;

   All coordinate points in the parameter space are counted, and θ is traversed, so that when the edge pixel points in the image space are mapped to the parameter space as a circle, the circle is regarded as the Hough circle, and the Hough circle is used to form a circle. The area of interest;

   Fitting the region of interest to generate a low-power fitting region of interest.
5. The method for automatically labeling abnormal cells according to claim 1, wherein the mapping is to divide the low-resolution image into a plurality of sub-regions, and add each of the sub-regions to the high-resolution image by multiplying the coordinates of the sub-regions and the multiples The magnification is the magnification of the high-resolution image relative to the low-resolution image.
6. 8. The method for automatically labeling abnormal cells according to claim 1, wherein the step of labeling and cutting the segmented high-magnification image through an adaptive threshold segmentation algorithm to obtain an abnormal cell labeling set comprises:

   Pass each pixel i of the segmented high-magnification image through the formula:

   

   Convert it to a real number between 0-1 and get the normalized value h;

   Perform pre-compensation calculation on the normalized value h by using a pre-defined gamma correction compensation value to obtain a pre-compensation constant value f;

   Perform denormalization calculation on the pre-compensated constant value f through the formula f*256-0.5 to obtain the corrected image of the segmented high-magnification image;

   The image gray level of the corrected image and the pixel point gray level of the coordinate in the corrected image are formed into a binary group, the mean value and variance of all binary groups are calculated, and the two-dimensional maximum class inter-class is established through the mean value and variance A variance model, calculating the two-dimensional maximum between-cluster variance model through an adaptive particle clustering algorithm to generate the optimal threshold of the corrected image;

   Segmenting the corrected image by using the optimal threshold to generate a background and foreground segmented image;

   The background and foreground segmented images are subjected to open operation and closed operation processing to generate an abnormal cell annotation set.
7. 8. The method for automatically labeling abnormal cells according to claim 6, wherein said performing open operation and close operation processing on the background and foreground segmented images to generate an abnormal cell label set comprises:

   Perform opening calculations on the background and foreground segmented images sequentially through a morphological algorithm, and remove isolated small points in the segmented images to generate a first image set;

   The first image set is sequentially closed by a morphological algorithm, the small cracks between the image cells in the first image set are filled, and the voids inside the images in the first image set are cleared, so that the The foreground cells in the first image set are separated from the background areas in the first image set to generate an abnormal cell annotation set.
8. An electronic device, wherein the electronic device includes:

   At least one processor; and,

   A memory communicatively connected with the at least one processor; wherein,

   The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for automatically labeling abnormal cells as described below:

   Acquiring a cytopathological picture, performing Gaussian convolution smoothing processing on the cytopathological picture for a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture;

   Detecting and fitting the image pyramid to obtain a low-power fitting region of interest;

   Mapping the low-power fitting region of interest to the acquired cytopathological picture and performing an image magnification operation to generate a fitting high-power image, and acquiring all image coordinates of the region of interest in the fitted high-power image;

   Segmenting the region of interest in the fitted high-magnification image by using the image coordinates to generate a segmented high-magnification image;

   Annotate and cut the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells;

   The abnormal cell annotation set is mapped to the cytopathological picture to obtain an abnormal cell annotation map.
9. 8. The electronic device according to claim 8, wherein said performing Gaussian convolution smoothing processing on the cytopathological picture a preset number of times to generate a plurality of cytopathological pictures, and obtaining an image pyramid according to the cytopathological picture, comprises:

   Step A: Perform an enlargement operation on the acquired cytopathological pictures to obtain a group a and b layer images, wherein the initial values of a and b are both 1, and the a group and b layer images are Gaussian rolls The product function performs the smoothing operation of Gaussian convolution to obtain the b+1th layer image of the ath group, where the Gaussian convolution function is:

   

   Where σ represents the smoothing factor, G _{(x, y, σ)} represents the convolution of x and y, and x and y represent the image coordinates;

   Step B: Multiply the smoothing factor σ by the scale factor k to obtain a new smoothing factor kσ, and use the Gaussian convolution function to Gaussian the a-th group and the b+1th layer image through the new smoothing factor kσ Convolutional smoothing operation to obtain the b+2th layer image of the ath group, and repeat this step until the Lth layer image of the ath group is obtained, where L is a predefined value;

   Step C: Perform a down-sampling operation on the a-th group of L-th layer images to obtain the a+1-th group of b-th layer images, and perform Gaussian convolution on the a+1-th group and b-th layer images using the Gaussian convolution function described above Product smoothing operation to obtain the a+1th group of b+1th layer images, and repeat the step B until the a+1th group of Lth layer images are obtained;

   Step D: Perform the loop operation of Step C and Step B on the result obtained in Step C, until the O-th group of L-th layer images is obtained, where O is a predefined value;

   Step E: Combine images from the a-th group and b-th layer images to the O-th group and L-th layer images to generate the image pyramid.
10. 8. The electronic device of claim 8, wherein the detecting and fitting of the image pyramid by the Hough transform circle detection method to obtain the low-power fitting region of interest comprises:

    Converting the image pyramid to grayscale to generate a grayscale image;

    Performing binarization processing on the grayscale image to obtain a contour image;

    Using a random selection method from the contour image to obtain a candidate area;

    The Hough circle transform detection method is used to identify the region of interest in the candidate region, and fit the region of interest to obtain the low-power fitting region of interest.
11. 10. The electronic device according to claim 10, wherein said identifying and fitting said region of interest using a Hough transform circle detection method to obtain said low-power fitting region of interest comprises:

    Detecting the edge of the candidate area in the image space of the candidate area by using an edge detection algorithm to obtain n edge pixel point sets;

    Map the n edge pixel point sets to the parameter space with the predefined value r as the radius:

    

    Where r is a predefined value, θ∈[0,2π), x and y represent the corresponding coordinates (x, y) of the n edge pixel point sets, and (a, b) represent the reference point in the parameter space coordinate;

    All coordinate points in the parameter space are counted, and θ is traversed, so that when the edge pixel points in the image space are mapped to the parameter space as a circle, the circle is regarded as the Hough circle, and the Hough circle is used to form a circle. The area of interest;

    Fitting the region of interest to generate a low-power fitting region of interest.
12. The electronic device according to claim 8, wherein the mapping is to divide the low-resolution image into a plurality of sub-regions, and add each of the sub-regions to the high-resolution image by multiplying the coordinates of the sub-regions and the multiples, and The magnification is the magnification of the high-resolution image relative to the low-resolution image.
13. 8. The electronic device according to claim 8, wherein said performing annotating and cutting of said segmented high-power image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells comprises:

    Pass each pixel i of the segmented high-magnification image through the formula:

    

    Convert it to a real number between 0-1 and get the normalized value h;

    Perform pre-compensation calculation on the normalized value h by using a pre-defined gamma correction compensation value to obtain a pre-compensation constant value f;

    Perform denormalization calculation on the pre-compensated constant value f through the formula f*256-0.5 to obtain the corrected image of the segmented high-magnification image;

    The image gray level of the corrected image and the pixel point gray level of the coordinate in the corrected image are formed into a binary group, the mean value and variance of all binary groups are calculated, and the two-dimensional maximum class inter-class is established through the mean value and variance A variance model, calculating the two-dimensional maximum between-cluster variance model through an adaptive particle clustering algorithm to generate the optimal threshold of the corrected image;

    Segmenting the corrected image by using the optimal threshold to generate a background and foreground segmented image;

    The background and foreground segmented images are subjected to open operation and closed operation processing to generate an abnormal cell annotation set.
14. A computer-readable storage medium, wherein an abnormal cell automatic labeling program is stored on the computer-readable storage medium, and the abnormal cell automatic labeling program can be executed by one or more processors to realize the abnormalities described below Steps of automatic cell labeling method:

    The image pyramid generating step is used to obtain a cytopathological picture, perform Gaussian convolution smoothing for a preset number of times on the cytopathological picture, generate multiple cytopathological pictures, and obtain an image pyramid according to the cytopathological picture;

    The detection and fitting step is used to detect and fit the image pyramid to obtain a low-power fitting region of interest;

    The mapping and segmentation step is used to map the low-power fitted region of interest to the acquired cytopathological picture and perform image magnification operations to generate a fitted high-power image, and obtain the region of interest in the fitted high-power image And then use the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image;

    An annotation cutting step is used for annotating and cutting the segmented high-power image through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set;

    The abnormal generation step is used to map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.
15. The computer-readable storage medium of claim 14, wherein the cytopathological picture is subjected to Gaussian convolution smoothing for a preset number of times to generate a plurality of cytopathological pictures, and an image pyramid is obtained according to the cytopathological picture ,include:

    Step A: Perform an enlargement operation on the acquired cytopathological pictures to obtain a group a and b layer images, wherein the initial values of a and b are both 1, and the a group and b layer images are Gaussian rolls The product function performs the smoothing operation of Gaussian convolution to obtain the b+1th layer image of the ath group, where the Gaussian convolution function is:

    

    Where σ represents the smoothing factor, G _{(x, y, σ)} represents the convolution of x and y, and x and y represent the image coordinates;

    Step B: Multiply the smoothing factor σ by the scale factor k to obtain a new smoothing factor kσ, and use the Gaussian convolution function to Gaussian the a-th group and the b+1th layer image through the new smoothing factor kσ Convolutional smoothing operation to obtain the b+2th layer image of the ath group, and repeat this step until the Lth layer image of the ath group is obtained, where L is a predefined value;

    Step C: Perform a down-sampling operation on the a-th group of L-th layer images to obtain the a+1-th group of b-th layer images, and perform Gaussian convolution on the a+1-th group and b-th layer images using the Gaussian convolution function described above Product smoothing operation to obtain the a+1th group of b+1th layer images, and repeat the step B until the a+1th group of Lth layer images are obtained;

    Step D: Perform the loop operation of Step C and Step B on the result obtained in Step C, until the O-th group of L-th layer images is obtained, where O is a predefined value;

    Step E: Combine images from the a-th group and b-th layer images to the O-th group and L-th layer images to generate the image pyramid.
16. 15. The computer-readable storage medium according to claim 14, wherein the detecting and fitting the image pyramid by the Hough transform circle detection method to obtain the low-power fitting region of interest comprises:

    Converting the image pyramid to grayscale to generate a grayscale image;

    Performing binarization processing on the grayscale image to obtain a contour image;

    Using a random selection method from the contour image to obtain a candidate area;

    The Hough circle transform detection method is used to identify the region of interest in the candidate region, and fit the region of interest to obtain the low-power fitting region of interest.
17. 15. The computer-readable storage medium according to claim 16, wherein the identifying and fitting the region of interest using the Hough transform circle detection method to obtain the low-power fitting region of interest comprises:

    Detecting the edge of the candidate area in the image space of the candidate area by using an edge detection algorithm to obtain n edge pixel point sets;

    Map the n edge pixel point sets to the parameter space with the predefined value r as the radius:

    

    Where r is a predefined value, θ∈[0,2π), x and y represent the corresponding coordinates (x, y) of the n edge pixel point sets, and (a, b) represent the reference point in the parameter space coordinate;

    All coordinate points in the parameter space are counted, and θ is traversed, so that when the edge pixel points in the image space are mapped to the parameter space as a circle, the circle is regarded as the Hough circle, and the Hough circle is used to form a circle. The area of interest;

    Fitting the region of interest to generate a low-power fitting region of interest.
18. The computer-readable storage medium according to claim 14, wherein the mapping is to divide the low-resolution image into a plurality of sub-regions, and add each of the sub-regions to the high-resolution image by multiplying the coordinates of the sub-regions and the multiples The magnification is the magnification of the high-resolution image relative to the low-resolution image.
19. 15. The computer-readable storage medium according to claim 14, wherein said performing annotating and cutting of said segmented high-magnification image through an adaptive threshold segmentation algorithm to obtain an annotated set of abnormal cells comprises:

    Pass each pixel i of the segmented high-magnification image through the formula:

    

    Convert it to a real number between 0-1 and get the normalized value h;

    Perform pre-compensation calculation on the normalized value h by using a pre-defined gamma correction compensation value to obtain a pre-compensation constant value f;

    Perform denormalization calculation on the pre-compensated constant value f through the formula f*256-0.5 to obtain the corrected image of the segmented high-magnification image;

    The image gray level of the corrected image and the pixel point gray level of the coordinate in the corrected image are formed into a binary group, the mean value and variance of all binary groups are calculated, and the two-dimensional maximum class inter-class is established through the mean value and variance A variance model, calculating the two-dimensional maximum between-cluster variance model through an adaptive particle clustering algorithm to generate the optimal threshold of the corrected image;

    Segmenting the corrected image by using the optimal threshold to generate a background and foreground segmented image;

    The background and foreground segmented images are subjected to open operation and closed operation processing to generate an abnormal cell annotation set.
20. An automatic labeling device for abnormal cells, wherein the device comprises:

    The image pyramid generating module is used to obtain a cytopathological picture, perform Gaussian convolution smoothing for a preset number of times on the cytopathological picture, generate multiple cytopathological pictures, and obtain an image pyramid according to the cytopathological picture;

    The detection and fitting module is used to detect and fit the image pyramid to obtain a low-power fitting region of interest;

    The mapping and segmentation module is used to map the low-power fitted region of interest to the acquired cytopathological picture and perform image magnification operations to generate a fitted high-power image, and obtain the region of interest in the fitted high-power image And then use the image coordinates to segment the region of interest in the fitted high-magnification image to generate a segmented high-magnification image;

    An annotation cutting module, which is used to annotate and cut the segmented high-magnification image through an adaptive threshold segmentation algorithm to obtain an abnormal cell annotation set;

    The abnormal generation module is used to map the abnormal cell annotation set to the cytopathological picture to obtain an abnormal cell annotation map.

Images