Definition analysis method and system based on digital microscopic panoramic imaging, and storage medium

Abstract

The invention discloses a definition analysis method and system based on digital microscopic panoramic imaging and a storage medium. The method comprises the following steps: acquiring an acquired slice sample image; calculating the definition values of the left edge image and the right edge image of each small image; comparing the slice sample image with the definition value table, if the whole slice sample image is fuzzy and the definition values of the splicing positions of two adjacent small images are similar, judging whether the object is in the field depth range or not according to the object distance; the image is not blurred until the whole image does not exist in the slice sample image; if the definition value ratio of the right edge of the left image to the left edge of the right image is smaller than a preset threshold value, marking a fuzzy position in the slice sample image, and judging whether a light path needs to be corrected or not according to the mark of the slice sample image until the image definition meets the expectation; and generating a slice sample image with a collimated light path. According to the invention, the recognition efficiency is greatly improved, and the recognition result is more accurate.

Description

Technical Field

[0001] This application relates to the field of digital microscopic optical technology, and particularly to a clarity analysis method, system, and storage medium based on digital microscopic panoramic imaging. Background Art

[0002] With the upgrade of information technology, the digitalization of pathology has been gradually popularized, and the traditional manual mode of microscopes has also been gradually replaced. The digitalization of pathology converts physical sections into digital images through a pathology slide scanner and a microscopic imaging system, enabling pathologists to perform tasks such as viewing slides, remote consultation, electronic archiving, AI-assisted diagnosis, and teaching through a display, bringing great convenience to the field of pathology. Image clarity is one of the important indicators for evaluating image quality. During the digitalization process, the quality of the digital images generated by the pathology slide scanner is crucial for subsequent research and applications. How to intelligently evaluate the imaging quality of the scanner, especially image clarity, has become a major technical challenge.

[0003] The image clarity of the scanner's microscopic imaging system depends on many factors. For example, during the production of sample sections, the surface of the sample may be uneven, the glass slide may be uneven, the flatness of the optical path, the accuracy of the focusing algorithm, etc. can all lead to a decline in image quality. To ensure that the generated digital images are clear and available for viewing, it may be necessary to perform a large number of analyses and evaluations of the clarity of the scanned images during various scenarios such as before the scanner leaves the factory, before equipment use, during equipment maintenance, and real-time scanning quality control, and use the results to guide users in making hardware adjustments, parameter adjustments, or slide preparation adjustments, etc. The digital image of a complete sample section is composed of thousands or tens of thousands of small images stitched together. The focusing algorithm and the optical path will cause differences in the clarity of the small images horizontally or vertically. Traditional clarity analysis and evaluation calculate the clarity of a single image. However, the tissue quantity at different positions of a single image is different, and the clarity values vary greatly, making it impossible to accurately judge the clarity of the image. If the clarity of all images is identified, the computational workload is extremely large, the overall efficiency is low, and the stability of the optical path cannot be judged by the clarity of a single image. Summary of the Invention

[0004] The purpose of this application is to provide a clarity analysis method, system, and storage medium based on digital microscopic panoramic imaging to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0005] This application adopts the following technical solutions to achieve the above-mentioned invention purpose: This application provides a clarity analysis method based on digital microscopic panoramic imaging, including: Obtain the collected section sample image, and the section sample image is collected by building an optical path and a scanning platform; Traverse and splice the small images of the same slice sample image, and calculate the clarity values of the left and right edge images of each small image; Create a clarity value table, which includes the left and right clarity values of each small image; Compare the slice sample image with the clarity value table. If the entire slice sample image is blurred and the clarity values at the splicing positions of two adjacent small images are similar, judge whether the object is within the depth of field according to the object distance; When the object is not within the depth of field, refocusing is required. After refocusing, obtain the slice sample image again until there is no situation where the entire slice sample image is blurred; When the object is within the depth of field, the optical path and the scanning platform need to be adjusted. After calibrating the platform and the optical path, obtain the slice sample image again until there is no situation where the entire slice sample image is blurred; When there is no situation where the entire slice sample image is blurred, traverse all adjacent small images. If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than the preset threshold, mark the blurred position in the slice sample image, and judge whether the optical path needs to be corrected according to the marking of the slice sample image until the image clarity meets the expectation; Finally, generate a slice sample image with collimated optical path.

[0006] Furthermore, the method for building the optical path and the scanning platform to collect the slice sample image includes: Place the slice sample on the scanning platform, and perform magnified imaging through the magnifying and correcting optical path composed of the objective lens and the tube lens; The scanning platform uses a line array camera or a area array camera to sequentially collect the local images required for splicing the slice sample image from left to right and from top to bottom. Each slice sample image is composed of several small images spliced together; When collecting several small images, use the autofocus algorithm to adjust the objective lens at the optimal focal length. At this time, the slice image collected by the line array camera or the area array camera is the clearest. In this imaging plane, points within a certain range in front of and behind the lens axis form an acceptable front depth of field and rear depth of field for the eye. The distance range in front of and behind the plane is the depth of field. The formula is as follows: ; Among them, is the front depth of field, is the rear depth of field, is the depth of field; The focal length, object distance, and image distance satisfy the following Gaussian imaging formula: ; Among them, is the focal length, is the object distance, is the image distance.

[0007] Furthermore, traverse and splice the small images of the same slice sample image, and calculate the sharpness values of the left and right edge images of each small image, including: Select the right-edge image data and left-edge image data of the small image; Select different sharpness value algorithms, and calculate the sharpness values of the left-edge image and the right-edge image under different algorithms respectively; Set the sharpness value weights and calculate the total sharpness value.

[0008] Furthermore, select different sharpness value algorithms, and calculate the sharpness values of the left-edge image and the right-edge image under different algorithms respectively, including: Use the Brenner gradient function to calculate the sharpness value , and the formula is as follows: ; Wherein, is the gray value corresponding to the position of the image , and are the width and height of the image respectively. When calculating, skip 1 adjacent pixel to enhance the sensitivity to high-frequency information; Use the Tenengrad gradient function to calculate the sharpness value , and the formula is as follows: ; ; Wherein, and are the width and height of the image respectively, is the preset edge detection threshold; and are the convolutions of the Sobel operator horizontal and vertical direction edge detection operators corresponding to the pixel point respectively. The Sobel operator template is: ; ; Use the Laplacian gradient function to calculate the sharpness value , and the formula is as follows: ; ; Wherein, and are the width and height of the image respectively, is the preset edge detection threshold, and are the convolutions of the Sobel operator horizontal and vertical direction edge detection operators corresponding to the pixel point Convolution of the Laplacian horizontal and vertical edge detection operators, and the Laplacian operator template is: ; Calculate the sharpness value using the sum of absolute values of gray - level differences function , and the formula is as follows: ; Among them, and are the width and height of the image respectively, is the image gray - level value corresponding to the position, represents the sum of absolute values of gray - level differences of adjacent pixels in the vertical direction, represents the sum of absolute values of gray - level differences of adjacent pixels in the horizontal direction.

[0009] Furthermore, set the sharpness value weights and calculate the total sharpness value. The formula is as follows: Set the sharpness value weight to be , the sharpness value weight to be , the sharpness value weight to be , the sharpness value weight to be , calculate the total sharpness value , and the formula is as follows: ; Among them, .

[0010] Furthermore, judge whether the object is not within the depth - of - field range according to the object distance. The formula is as follows: or .

[0011] Furthermore, if the ratio of the sharpness value of the right edge of the left image to the sharpness value of the left edge of the right image is less than the preset threshold, mark the blurred position in the sliced sample image, and judge whether the optical path needs to be corrected according to the annotation of the sliced sample image until the image sharpness meets the expectation, including: When the sharpness value of the right edge of the left image and the sharpness value of the left edge of the right image satisfy the following formula: ; Among them, is the preset threshold; Mark the position where the blur is located with a virtual box in the sliced sample image; Judge whether the optical path needs to be corrected according to the virtual frame. When the marked range of the virtual frame is large and densely distributed, it indicates that correction is needed, and adjust the optical path; Re-acquire the slice sample image until the image clarity meets the expectation.

[0012] This application provides a clarity analysis system based on digital microscopic panoramic imaging, including: An acquisition unit for acquiring the collected slice sample image, and the slice sample image is acquired by building an optical path and a scanning platform; A calculation unit for traversing and stitching the small images of the same slice sample image, and calculating the clarity values of the left and right edge images of each small image; A creation unit for creating a clarity value table, and the clarity value table includes the left and right clarity values of each small image; A first judgment unit for comparing the slice sample image with the clarity value table. If the entire slice sample image is blurred and the clarity values at the stitching positions of two adjacent small images are similar, judge whether the object is within the depth of field according to the object distance; A second judgment unit for re-focusing when the object is not within the depth of field, and re-acquiring the slice sample image after focusing until there is no situation where the entire slice sample image is blurred; A third judgment unit for adjusting the optical path and the scanning platform when the object is within the depth of field, and re-acquiring the slice sample image after calibrating the platform and the optical path until there is no situation where the entire slice sample image is blurred; A fourth judgment unit for traversing all adjacent small images when there is no situation where the entire slice sample image is blurred. If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than a preset threshold, mark the blurred position in the slice sample image, and judge whether the optical path needs to be corrected according to the annotation of the slice sample image until the image clarity meets the expectation; A generation unit for finally generating a slice sample image with collimated optical path.

[0013] This application provides a clarity analysis system based on digital microscopic panoramic imaging, including a memory and a processor; The memory is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the above method.

[0014] This application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0015] The beneficial effects of this application are as follows: Different from calculating the clarity of a single image, this application calculates by comparing the left and right adjacent small images spliced from the same slice sample image. The tissue amounts of the right edge image of the left small image and the left edge image of the right small image are basically the same. The collimation and horizontality of the optical system can be judged by comparing the clarity of the left and right sides. When the clarity of the two sides is similar and both are low, the correctness of the focus algorithm selection needs to be considered. Moreover, when calculating the clarity of a large number of single small images, this application only selects partial image data on both sides of the image for clarity calculation. Compared with the traditional calculation of the clarity of the entire image, the recognition efficiency is greatly improved, and the recognition result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is an overall flowchart of a clarity analysis method based on digital microscopic panoramic imaging according to an embodiment of the present application; Figure 2 FIG. is a panoramic image generated by splicing in an ideal state in a clarity analysis method based on digital microscopic panoramic imaging according to an embodiment of the present application; Figure 3 FIG. is a panoramic image generated by splicing in an actual environment in a clarity analysis method based on digital microscopic panoramic imaging according to an embodiment of the present application; Figure 4 FIG. is a regional map for calculating clarity in a clarity analysis method based on digital microscopic panoramic imaging according to an embodiment of the present application; Figure 5 FIG. is a panoramic image with a marked blurred red frame in a clarity analysis method based on digital microscopic panoramic imaging according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Different from calculating the clarity of a single image, this application calculates by comparing the left and right adjacent small images spliced from the same slice sample image. The tissue amounts of the right edge image of the left small image and the left edge image of the right small image are basically the same. The collimation and horizontality of the optical system can be judged by comparing the clarity of the left and right sides. When the clarity of the two sides is similar and both are low, the correctness of the focus algorithm selection needs to be considered. This application uses multiple image clarity algorithms when calculating the clarity of an image and assigns them certain weights, which can effectively solve the problem of inaccurate calculation using only a single clarity algorithm. Moreover, when calculating the clarity of a large number of single small images, this application only selects partial image data on both sides of the image for clarity calculation. Compared with the traditional calculation of the clarity of the entire image, the recognition efficiency is greatly improved, and the recognition result is more accurate.

[0018] Such as Figure 1As shown in the figure, a clarity analysis method based on digital microscopic panoramic imaging provided by this application specifically includes the following steps: S100: Obtain the collected section sample images, which are collected by building an optical path and a scanning platform; The method of building an optical path and a scanning platform to collect section sample images in S100 is specifically as follows: S101, Place the section on the scanning platform, and perform magnified imaging through the magnifying and correcting optical path composed of an objective lens and a tube lens. The imaging is captured by the camera. After starting, the scanning platform uses a line array camera or a area array camera to sequentially collect the local images required for stitching the section sample image from left to right and from top to bottom. The section sample image of each section sample is composed of thousands of small images. Ideally, as Figure 2 shown, but in actual situations, there will be overlaps between the images captured by the camera, as Figure 3 shown;

[0019] S102, Calculate the focal point and the depth of field . Specifically, the focal length, object distance, and image distance satisfy the Gaussian imaging formula: ; Among them, is the focal length, is the object distance, is the image distance.

[0020] S103, When collecting a number of small images, use the autofocus algorithm to adjust the objective lens at the optimal focal length. At this time, the section images collected by the line array camera or the area array camera are the clearest. In a certain range in front of and behind the imaging plane along the lens axis, points form an acceptable foreground depth and background depth , and the distance range in front of and behind this plane is the depth of field ; .

[0021] S104, Start the platform to collect the section sample images.

[0022] S200: Traverse and stitch the small images of the same section sample image, and calculate the clarity values of the left and right edge images of each small image; The specific content of S200 is as follows: S201, Traverse and stitch the small images of the same section sample image, calculate the clarity values of the left and right edge images of each small image, select the right edge image data and left edge image data of the small image, such as Figure 4 . Select different clarity algorithms, and calculate the clarity values of the left and right edge images under different algorithms respectively Specifically: Calculate the sharpness value using the Brenner gradient function , and the formula is as follows: ; where is the gray value corresponding to the position of the image , and are the width and height of the image (in pixels), respectively. When calculating, skip 1 adjacent pixel (i.e., compare the difference between the current pixel and the pixel separated by one pixel) to enhance the sensitivity to high-frequency information; Calculate the sharpness value using the Tenengrad gradient function , and the formula is as follows: ; ; where and are the width and height of the image, respectively is a preset edge detection threshold; and are the convolutions of the Sobel operator horizontal and vertical direction edge detection operators corresponding to the pixel point , and the Sobel operator template is: ; ; Calculate the sharpness value using the Laplacian gradient function , and the formula is as follows: ; ; where and are the width and height of the image, respectively is a preset edge detection threshold and are the convolutions of the Laplacian horizontal and vertical direction edge detection operators corresponding to the pixel point , and the Laplacian operator template is: ; Calculate the sharpness value using the Sum of Modulus of gray Difference function , and the formula is as follows: ; where and are the width and height of the image respectively, is the image gray value corresponding to the position, represents the sum of the absolute values of the gray differences of adjacent pixels in the vertical direction, represents the sum of the absolute values of the gray differences of adjacent pixels in the horizontal direction.

[0023] S202, set the sharpness value The weight is , the sharpness value The weight is , the sharpness value The weight is , the sharpness value The weight is , calculate the total sharpness value , the formula is as follows: ; Among them, .

[0024] S300: Create a sharpness value table, and the sharpness value table includes the sharpness values of the left and right edge images of each small image.

[0025] S400: Compare the sliced sample image with the sharpness value table. If the whole sliced sample image is blurred and the sharpness values of the splicing positions of two adjacent small images are similar, judge whether the object is within the depth of field range according to the object distance. The formula is as follows: or ; When the object is not within the depth of field range, refocusing is required. After refocusing, obtain the sliced sample image again until there is no situation where the whole sliced sample image is blurred; When the object is within the depth of field range, adjust the optical path and the scanning platform. After calibrating the platform and the optical path, obtain the sliced sample image again until there is no situation where the whole sliced sample image is blurred.

[0026] S500: In the case where there is no situation where the whole sliced sample image is blurred, traverse all adjacent small images. If the ratio of the sharpness value of the right edge of the left image to the left edge of the right image is less than the preset threshold, mark the blurred position in the sliced sample image, and judge whether the optical path needs to be corrected according to the marking of the sliced sample image until the image sharpness meets the expectation; The specific content of S500 is: The preset threshold is , according to the formula:

[0027] Traverse all adjacent small images. When the clarity between the right edge of the left image and the left edge of the right image satisfies the above formula, mark the location of the blur with a dashed box in the sliced sample image, as Figure 5 . Determine whether the optical path needs to be corrected based on the blurred red box. When the marked range of the blurred red box is large and densely distributed, it indicates that correction is needed, and adjust the optical path. After correcting the optical path, return to step S203 again until the overall image is clear and the marked range of the dashed box is small and sparsely distributed.

[0028] S600: Finally, generate a sliced sample image with collimated optical path.

[0029] This application also provides an image processing system based on the deconvolution algorithm, including: An acquisition unit for acquiring the acquired sliced sample image, and the sliced sample image is acquired by setting up an optical path and a scanning platform; A calculation unit for traversing and stitching small images of the same sliced sample image, and calculating the clarity values of the left and right edge images of each small image; A creation unit for creating a clarity value table, and the clarity value table includes the left and right clarity values of each small image; A first judgment unit for comparing the sliced sample image and the clarity value table. If the entire sliced sample image is blurred and the clarity values at the stitching positions of two adjacent small images are similar, determine whether the object is within the depth of field according to the object distance; A second judgment unit for refocusing when the object is not within the depth of field, and re-acquiring the sliced sample image after focusing until there is no situation where the entire sliced sample image is blurred; A third judgment unit for adjusting the optical path and the scanning platform when the object is within the depth of field, and re-acquiring the sliced sample image after calibrating the platform and the optical path until there is no situation where the entire sliced sample image is blurred; A fourth judgment unit for traversing all adjacent small images when there is no situation where the entire sliced sample image is blurred. If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than a preset threshold, mark the blur position in the sliced sample image, and judge whether the optical path needs to be corrected according to the marking of the sliced sample image until the image clarity meets the expectation; A generation unit for finally generating a sliced sample image with collimated optical path.

[0030] Another image processing system based on the deconvolution algorithm provided by this application may also be: including a memory and a processor; the memory is used for storing instructions; The processor is used to operate according to the instructions to execute the steps of the foregoing clarity analysis method based on digital microscopic panoramic imaging.

[0031] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the aforementioned clarity analysis method based on digital microscopic panoramic imaging are implemented.

[0032] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0033] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0034] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0035] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0036] The above is only the preferred embodiment of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present application.

Claims

1. A clarity analysis method based on digital microscopic panoramic imaging, characterized in that, Including: Obtain the collected sliced sample image, which is collected by building an optical path and a scanning platform; Traverse and splice the small images of the same sliced sample image, and calculate the clarity values of the left and right edge images of each small image; Create a clarity value table, which includes the left and right clarity values of each small image; Compare the sliced sample image with the clarity value table. If the whole sliced sample image is blurred and the clarity values at the splicing positions of two adjacent small images are similar, judge whether the object is within the depth of field according to the object distance: When the object is not within the depth of field, refocusing is required. After refocusing, obtain the sliced sample image again until there is no situation where the whole sliced sample image is blurred; When the object is within the depth of field, adjust the optical path and the scanning platform. After calibrating the platform and the optical path, obtain the sliced sample image again until there is no situation where the whole sliced sample image is blurred; In the case where there is no situation where the whole sliced sample image is blurred, traverse all adjacent small images. If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than the preset threshold, mark the blurred position in the sliced sample image, and judge whether the optical path needs to be corrected according to the marking of the sliced sample image until the image clarity meets the expectation; Finally, generate a sliced sample image with collimated optical path.

2. The clarity analysis method based on digital microscopic panoramic imaging according to claim 1, wherein The method for building an optical path and a scanning platform to collect a sliced sample image includes: Place the sliced sample on the scanning platform, and perform magnified imaging by an amplified correction optical path composed of an objective lens and a tube lens; The scanning platform uses a line array camera or a area array camera to sequentially collect the local images required for splicing the sliced sample image from left to right and from top to bottom. Each sliced sample image is composed of several small images spliced together; When collecting several small images, use an autofocus algorithm to adjust the objective lens to the best focal length. At this time, the sliced image collected by the line array camera or the area array camera is the clearest. At a certain range of points in front of and behind the imaging plane along the lens axis, the acceptable front depth of field and rear depth of field are formed. The distance range in front of and behind the plane is the depth of field. The formula is as follows: ； Among them, is the front depth of field, is the rear depth of field, is the depth of field; The focal length, object distance, and image distance satisfy the following Gaussian imaging formula: ； Among them, is the focal length, is the object distance, is the image distance.

3. A clarity analysis method based on digital microscopic panoramic imaging according to claim 1, characterized in that, Traverse and splice the small images of the same sliced sample image, and calculate the clarity values of the left and right edge images of each small image, including: Select the right edge image data and left edge image data of the small image; Select different clarity value algorithms, and calculate the clarity values of the left edge image and the right edge image under different algorithms respectively; Set the clarity value weight and calculate the total clarity value.

4. A clarity analysis method based on digital microscopic panoramic imaging according to claim 3, characterized in that Select different clarity value algorithms, and calculate the clarity values of the left edge image and the right edge image under different algorithms respectively, including: Calculate the sharpness value using the Brenner gradient function , the formula is as follows: ； Among them, is the grayscale value corresponding to the image position, and are the width and height of the image respectively. When calculating, skip 1 adjacent pixel to enhance the sensitivity to high-frequency information; Calculating the sharpness value using the Tenengrad gradient function , the formula is as follows: ； ； Among them, and are the width and height of the image respectively, is a preset edge detection threshold; and are respectively the convolutions of the corresponding pixel point with the edge detection operators in the horizontal and vertical directions of the Sobel operator. The Sobel operator template is: ； ； Calculate the sharpness value using the Laplacian gradient function , the formula is as follows: ； ； Among them, and are the width and height of the image respectively, is a preset edge detection threshold, and are the convolutions of the corresponding pixel point with the Laplacian horizontal and vertical edge detection operators respectively. The Laplacian operator template is: ； Calculate the sharpness value using the sum of absolute values of gray - level differences function , and the formula is as follows: ； wherein, and are the width and height of the image respectively, is the image gray value corresponding to the position, represents the sum of the absolute values of the gray differences between adjacent pixels in the vertical direction, represents the sum of the absolute values of the gray differences between adjacent pixels in the horizontal direction.

5. The clarity analysis method based on digital microscopic panoramic imaging according to claim 4, characterized in that Set the clarity value weight and calculate the total clarity value. The formula is as follows: Set the clarity value The weight is , the clarity value The weight is , the clarity value The weight is , the clarity value The weight is , calculate the total clarity value , the formula is as follows: ； Among them, 。 6. The clarity analysis method based on digital microscopic panoramic imaging according to claim 5, wherein, Judge whether the object is not within the depth of field according to the object distance. The formula is as follows: or 。 7. A clarity analysis method based on digital microscopic panoramic imaging according to claim 6, characterized in that, If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than the preset threshold, mark the blurred position in the sliced sample image, and judge whether the optical path needs to be corrected according to the marking of the sliced sample image until the image clarity meets the expectation, including: When the sharpness value of the right edge of the left image and the sharpness value of the left edge of the right image satisfy the following formula: ； wherein, is a preset threshold value; Mark the position where the blur is located with a virtual frame in the sliced sample image; Judge whether the optical path needs to be corrected according to the virtual frame. When the marked range of the virtual frame is large and densely distributed, it indicates that correction is needed, and adjust the optical path; Re-obtain the slice sample image until the image clarity meets the expectation.

8. A clarity analysis system based on digital microscopic panoramic imaging, characterized in that, Including: An acquisition unit for acquiring the acquired slice sample image, and the slice sample image is acquired by building an optical path and a scanning platform; A calculation unit for traversing and stitching the small images of the same slice sample image, and calculating the clarity values of the left and right edge images of each small image; A creation unit for creating a clarity value table, and the clarity value table includes the left and right clarity values of each small image; A first judgment unit for comparing the slice sample image with the clarity value table. If the whole slice sample image is blurred and the clarity values at the stitching positions of two adjacent small images are similar, judge whether the object is within the depth of field according to the object distance; A second judgment unit for re-focusing when the object is not within the depth of field, and re-acquiring the slice sample image after focusing until there is no situation where the whole slice sample image is blurred; A third judgment unit for adjusting the optical path and the scanning platform when the object is within the depth of field, re-acquiring the slice sample image after calibrating the platform and the optical path until there is no situation where the whole slice sample image is blurred; A fourth judgment unit for traversing all adjacent small images when there is no situation where the whole slice sample image is blurred. If the ratio of the clarity value of the right edge of the left image to the left edge of the right image is less than a preset threshold, mark the blurred position in the slice sample image, and judge whether the optical path needs to be corrected according to the annotation of the slice sample image until the image clarity meets the expectation; A generation unit for finally generating a slice sample image with collimated optical path.

9. A clarity analysis system based on digital microscopic panoramic imaging, characterized in that, Including a memory and a processor; The memory is used for storing instructions; The processor is used for operating according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the steps of the method according to any one of claims 1-7 are implemented.

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