Fiber core bundle center positioning method and device of confocal microendoscope and terminal

Abstract

The invention relates to the field of confocal microendoscopes, in particular to a fiber core bundle center positioning method and device of a confocal microendoscope and a terminal. The method comprises the steps of obtaining a plurality of frames of optical fiber end face images and performing fusion processing to generate a reference image; performing convolution filtering on the reference image based on the fiber core feature template to generate a fiber core response image; and positioning a plurality of candidate fiber core points of the fiber core response image, and clustering the plurality of candidate fiber core points to generate a fiber core beam estimation center. According to the method, noise in the image is filtered through fusion processing, filtering is performed through the fiber core feature template to highlight the fiber cores in the fused image, interference is filtered, and finally the estimation center of the fiber core bundle is obtained based on the distance clustering method, so that a background model is conveniently established for each fiber core and real-time tracking is performed on each fiber core in subsequent steps, and the accuracy of the estimation center is improved. According to the confocal microendoscope, the anti-interference capability is remarkably improved, the positioning precision is high, the response speed is high, and the imaging performance and reliability of the confocal microendoscope are improved.

Description

Technical Field This invention relates to the field of confocal microendoscopy, and more particularly to a method, apparatus, and terminal for positioning the core bundle center of a confocal microendoscopy. Background Technology

[0001] A microendoscopy is a medical device that, through channels such as those found in gastroscopes and colonoscopes, can be inserted into the human body to acquire localized histological images, enabling precise diagnosis of minute lesions, gastrointestinal diseases, and early gastrointestinal cancers. A microendoscopy system consists of a main unit, a probe, and a monitor. During clinical examination, the probe is connected to the main unit, imaging is activated, and the probe is guided along the channel into the patient's body to reach the observation site. The monitor displays a real-time microscopic image of the observed area. However, the accuracy of estimating the fiber bundle center position determines the accuracy of the fiber core positioning result, both during the calibration phase to establish the fiber core background model and during the real-time imaging phase to track the fiber core position. This accuracy affects the subsequent imaging quality. Existing methods for locating the fiber bundle center are complex and prone to large errors, making them unsuitable for the requirements of confocal microendoscopy. Summary of the Invention

[0002] This invention provides a method, apparatus, and terminal for positioning the core bundle center of a confocal microendoscopy, which solves the technical problems mentioned above.

[0003] A first aspect of the present invention provides a method for locating the core bundle center of a confocal microendoscopy, comprising the following steps: S1, acquire several frames of fiber endface images; S2, perform fusion processing on the several frames of fiber endface images to generate a reference image; S3, obtain the fiber core feature template corresponding to the target detection area, and perform convolution filtering on the reference image based on the fiber core feature template to generate a fiber core response image; S4, locate several candidate core points in the core response image, and generate a core bundle estimation center by clustering the several candidate core points.

[0004] A second aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for locating the core bundle center of a confocal microendoscopy microscope.

[0005] A third aspect of the present invention provides a fiber core bundle center positioning terminal, including the aforementioned computer-readable storage medium and a processor, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the above-described method for positioning the fiber core bundle of a confocal microendoscope.

[0006] A fourth aspect of this invention provides a core bundle center positioning device for a confocal microendoscopy system, comprising an image acquisition module, a fusion module, a filtering module, and a positioning module. The image acquisition module is used to acquire several frames of fiber endface images; The fusion module is used to fuse the several frames of fiber endface images to generate a reference image; The filtering module is used to obtain the fiber core feature template corresponding to the target detection area, and to perform convolution filtering on the reference image based on the fiber core feature template to generate a fiber core response image; The positioning module is used to locate several candidate fiber core points in the fiber core response image, and to generate a fiber core bundle estimation center after clustering the several candidate fiber core points.

[0007] The beneficial effects of this invention are as follows: This invention provides a method, device, and terminal for locating the fiber core bundle center of a confocal microendoscopy system. In the calibration or fiber core tracking stage, the acquired multi-frame fiber end-face images are first fused to filter noise in the images. Then, the fiber core feature template corresponding to the target detection area is used for filtering, thereby highlighting the fiber core in the fused image and filtering interference. Finally, the position of each fiber core in the fiber core bundle after image processing is located, and the estimated center of the fiber core bundle is obtained based on the distance clustering method of all fiber core positions. This facilitates subsequent steps in establishing a background model for each fiber core and real-time tracking of each fiber core. This not only significantly improves the anti-interference capability but also provides high positioning accuracy and fast response speed, thereby improving the imaging performance and reliability of the confocal microendoscopy system.

[0008] To make the above-mentioned objects, features and advantages of the invention more apparent and understandable, preferred embodiments of the invention are described below in detail with reference to the accompanying drawings. Attached Figure Description To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the method for locating the core bundle center of a confocal microendoscopy provided in Example 1; Figure 2 This is a schematic diagram of the core bundle center positioning device of the confocal microendoscopy provided in Example 2; Figure 3 This is a schematic diagram of the core bundle center positioning terminal of the confocal microendoscopy provided in Example 3. Detailed Implementation To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described in this specification are merely for explaining the invention and are not intended to limit the invention.

[0010] Figure 1 This is a flowchart illustrating the core bundle center positioning method for a confocal microendoscopy system provided in Example 1, as shown below. Figure 1 As shown, it includes the following steps: S1, acquire several frames of fiber endface images; S2, perform fusion processing on the several frames of fiber endface images to generate a reference image; S3, obtain the fiber core feature template corresponding to the target detection area, and perform convolution filtering on the reference image based on the fiber core feature template to generate a fiber core response image; S4, locate several candidate core points in the core response image, and generate a core bundle estimation center by clustering the several candidate core points.

[0011] The above embodiments provide a method for locating the fiber core bundle center in a confocal microendoscopy system. During the calibration or fiber core tracking stage, multiple frames of fiber end-face images are first fused to filter noise. Then, a fiber core feature template corresponding to the target detection area is used for filtering, highlighting the fiber core in the fused image and filtering interference. Finally, the position of each fiber core in the processed fiber core bundle is located, and a clustering method is used to obtain the estimated center of the fiber core bundle based on all fiber core positions. This facilitates subsequent steps in establishing a background model for each fiber core and real-time tracking of each fiber core. This method significantly improves anti-interference capabilities, provides high positioning accuracy and fast response speed, and enhances the imaging performance and reliability of the confocal microendoscopy system.

[0012] Each step is described below through specific embodiments.

[0013] For example, in different scenarios, step S1 acquires fiber end-face images under different preconditions. If it's during the calibration phase, the probe is cleaned and placed in the air, imaging is activated, and the laser power brightness is adjusted to the maximum. The PMT voltage is set to achieve appropriate imaging brightness. Then, the probe automatically finds the focus position to achieve the clearest image of the fiber end-face. N frames are then continuously acquired at the position with the clearest image, thereby obtaining stable and reliable reference data. The value of N is set according to the detection parameters, detection location, etc., and is typically... Within the range, a background model is established for each fiber core during the calibration phase, and the background brightness value of each fiber core at the maximum laser power is determined.

[0014] In the subsequent fiber core tracking stage, at each detection moment, such as at each collision detection moment to determine whether the probe has collided, N frames of real-time imaging are also performed on the fiber end face. Based on the imaging data, the center position of the fiber core bundle is located, and the position of each fiber core is accurately tracked to improve the imaging quality.

[0015] For example, step S2 can use methods such as median processing, mean processing, nearest neighbor mean processing, and / or temporal filtering to fuse several frames of fiber optic end-face images into a reference image. One embodiment uses median processing. Median filtering is a non-linear process that is particularly effective in maintaining image edges and structural details, thereby reducing or eliminating random noise, accidental errors, or other one-off anomalies. Mean processing is faster. In practical applications, one or more noise reduction methods can be selected or combined depending on the detection requirements.

[0016] For example, in step S3, for the fiber core arrangement characteristics of different confocal microendoscopic probes, probe-specific fiber core feature templates are pre-generated. This template is essentially a convolution kernel, and the weight distribution of the convolution kernel is set according to the ideal spot shape of the fiber core. After noise reduction processing, step S3 uses different fiber core feature templates to perform convolution filtering on the reference image. The high correlation between the feature template and the real fiber core region highlights the response peak, while the low correlation between background noise, cladding reflection, and other structures and the feature template is attenuated, thereby highlighting the real fiber core in the reference image, further filtering interference, and generating a fiber core response image.

[0017] For example, in step S4 of one embodiment, based on the fact that the fiber core appears as a bright spot in the image, the brightest pixel is most likely to represent the fiber core location. Therefore, local maxima are used to locate each real fiber in the fiber bundle. Here, a local maxima is a point on the fiber core response image where the local pixel value significantly exceeds the surrounding pixels, i.e., the point with the largest pixel value in the neighborhood region. This point is used as a candidate fiber core point, and a fiber core bundle estimation center is generated, including the following steps: S401, Locate several local maxima points in the fiber core response image and establish an initial set of local maxima points. For example, firstly, traverse all pixels of the fiber core response image to obtain target pixels located within a preset mask. Then, determine several neighboring points of each target pixel. If the brightness value of the target pixel is greater than the brightness values ​​of all its neighboring points, then the target pixel is a local maximum point, thereby establishing an initial set of local maxima points.

[0018] Specifically, in fiber bundle end-face images, the effective fiber core area typically occupies only a portion of the area, surrounded by useless cladding and background. Directly scanning the entire image to local maxima introduces noise points, such as reflective points at the cladding edges. Therefore, a mask can be used to define the true fiber core area and eliminate interference areas at the fiber bundle edges, reducing computational load and improving detection efficiency and response speed. The mask shape is usually set according to the probe type, and the mask size is set according to the fiber core diameter and fiber bundle diameter. Preferably, the mask shape and size can be dynamically adjusted or the mask boundary attenuated based on the probe's condition (e.g., whether it is bent, whether it is used at high temperatures), and the probe's location to avoid abrupt gradient changes and further improve detection performance.

[0019] S402, obtain the pixel value of each local maximum point in the local maximum point set, and sort all local maximum points according to the pixel value size, for example, sort them in descending order to generate an optimized local maximum point set.

[0020] S403, obtain a first target truncation threshold, and select target local maxima points whose pixel values ​​are greater than the first target truncation threshold as preferred fiber core points. For example, generate a first target truncation number based on a default fiber core number, such as a number slightly larger than the default fiber core number (e.g., 1.1-1.5 times the default fiber core number). Then, truncate the optimized local maxima point set based on the first target truncation number, and use the pixel value corresponding to the truncation point as the first target truncation threshold. This allows the target local maxima points whose pixel values ​​are greater than the first target truncation threshold to be selected as preferred fiber core points, further reducing the region of the fiber core points and improving computational efficiency.

[0021] S404, perform distance clustering on the preferred fiber core points and obtain the category with the most clusters as the most likely fiber core center region. This is because the real fiber core is generally uniform, dense, and has a high fiber core pixel value, making it easier to cluster into a category according to distance, and the number of clusters exceeds that of other interfering preferred fiber core points.

[0022] S405, finally locate the center point of the fiber core central region and use it as the estimated center of the fiber core bundle.

[0023] Distance clustering methods divide a dataset into several clusters based on the distance between data points. Data points within the same cluster are relatively close in distance, while data points in different clusters are relatively far apart. For example, in one embodiment, step S404 of the distance clustering method includes the following steps: S4041, Mark the positions of all preferred core points to form a marked image; S4042, Traverse the preferred fiber core points, obtain unclassified target preferred fiber core points, and add the target preferred fiber core points to the clustering sequence; S4043, retrieve the head of the clustering sequence as the current point, and add the current point to the corresponding temporary category point set according to the distance between the current point and each temporary category point set; S4044, determine a first search radius based on the detection location, search for unclassified neighboring points around the current point in the marked image based on the first search radius, and add the unclassified neighboring points to the temporary category point set to which the current point belongs; S4045, Repeat the above steps until all points in the clustering sequence have been classified. Add each temporary category point set to the corresponding clustering array to obtain the category with the most clusters in the clustering array, which is then used as the most likely fiber core center region. The above embodiment transforms the fiber core bundle center location problem into spatial point set distribution analysis. The densest region of fiber core coordinates is identified through distance clustering algorithm, and its center is the fiber core bundle center. Compared with traditional methods, it can automatically filter outliers at the edges, exhibiting strong noise resistance and high detection efficiency.

[0024] For example, in a preferred embodiment, the first search radius is determined based on the detection location, specifically as follows: Query the first preset mapping table to obtain the search coefficient corresponding to the detection accuracy; Query the second preset mapping table to obtain the fiber core distance corresponding to the detection location; The first search radius is generated based on the search coefficient and the fiber core distance, i.e., the first search distance = search coefficient * fiber core distance. Here, the fiber core distance can be the average fiber core spacing of the fiber core bundle in the probe. For example, it can be set to 4-5 pixels for a gastric probe and 6-7 pixels for a pancreatic and biliary probe.

[0025] In other preferred embodiments, the bending state of the probe can also be considered when determining the search coefficient, such as adjusting the search coefficient when it is at different degrees of bending. After clustering, the clustering rationality can be calculated, and the first search radius can be adjusted according to the clustering rationality to improve accuracy. For example, the maximum number of clusters in all cluster arrays (the number of cluster points in the fiber core center region) can be obtained, and the ratio of the maximum number of clusters to the preferred number of fiber core points can be calculated. When the ratio is less than a preset threshold, it indicates that the number of points clustered to the fiber core center region is too small, and effective points may have been missed. At this time, the first search radius is increased and clustering is performed again until the preset threshold is reached to further improve the clustering effect.

[0026] The above embodiments all use local maxima to locate each real fiber in the fiber bundle. In other embodiments, a centroid positioning method or a positioning method that combines local maxima and centroids can also be used to determine each real fiber in the fiber bundle. Specifically, the fiber core response image is divided into several connected regions, and the candidate fiber core points also include centroids corresponding to several connected regions in the fiber core response image. Target centroids with pixel values ​​greater than the corresponding second target truncation threshold (calculated using a similar method to the first target truncation threshold) are obtained. The target centroids are matched, fused, and deduplicated with the preferred fiber core points, and the fiber core center region is updated based on the processing results. This approach retains the rapid initial screening capability of local maxima and leverages the accuracy advantage of the centroid method, further improving detection efficiency and accuracy. Specifically, the matching and fusion process can set a maximum matching distance threshold based on the fiber spacing. It searches for the centroid closest to the corresponding local maximum point among the target centroids and calculates the matching distance. If the matching distance is less than the maximum matching distance threshold, it is considered to represent the same fiber core and is retained in the preferred fiber core points. Otherwise, the centroid is considered a new fiber core missed by the local maximum method and added to the preferred fiber core points. Simultaneously, if there are unmatched local maxima points among the preferred fiber core points, these points are verified to determine if they are noise or invalid points, and corresponding processing is performed. Finally, deduplication is performed based on coordinate uniqueness, thereby improving the selection accuracy of preferred fiber core points.

[0027] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0028] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for locating the core bundle center of a confocal microendoscopy microscope.

[0029] Figure 3 This is a schematic diagram of the core bundle center positioning device of the confocal microendoscopy provided in Example 2, as shown below. Figure 3 As shown, it includes an image acquisition module 100, a fusion module 200, a filtering module 300, and a positioning module 400. The image acquisition module 100 is used to acquire several frames of optical fiber end face images; The fusion module 200 is used to fuse the several frames of fiber end face images to generate a reference image; The filtering module 300 is used to obtain the fiber core feature template corresponding to the target detection area, and to perform convolution filtering on the reference image based on the fiber core feature template to generate a fiber core response image; The positioning module 400 is used to locate several candidate fiber core points in the fiber core response image, and to generate a fiber core bundle estimation center after clustering the several candidate fiber core points.

[0030] The above embodiments provide a fiber core bundle center positioning device for a confocal microendoscopy. In the calibration or fiber core tracking stage, the acquired multi-frame fiber end-face images are first fused to filter out noise. Then, the fiber core feature template corresponding to the target detection area is used for filtering to highlight the fiber core in the fused image and filter out interference. Finally, the position of each fiber core in the fiber core bundle after image processing is located, and the estimated center of the fiber core bundle is obtained by clustering based on all fiber core positions. This facilitates the subsequent steps of establishing a background model for each fiber core and real-time tracking of each fiber core. This not only significantly improves the anti-interference capability but also has high positioning accuracy and fast response speed, thereby improving the imaging performance and reliability of the confocal microendoscopy.

[0031] In a preferred embodiment, the positioning module 400 specifically includes: The first positioning unit is used to locate several local maxima points of the fiber core response image and establish an initial set of local maxima points. The sorting unit is used to obtain the pixel value of each local maximum point in the local maximum point set, and sort all local maximum points according to the size of the pixel value to generate an optimized local maximum point set. The first screening unit is used to obtain a first target truncation threshold and select the target local maxima points whose pixel values ​​are greater than the first target truncation threshold as preferred fiber core points. Distance clustering unit, used to perform distance clustering on the preferred fiber core points and obtain the category with the most clusters as the fiber core center region; The second positioning unit is used to locate the center point of the fiber core central region and serve as the estimated center of the fiber core bundle.

[0032] In a preferred embodiment, the positioning module 400 further includes a second filtering unit, used to locate centroids corresponding to several connected regions in the fiber core response image, obtain target centroids whose pixel values ​​are greater than the corresponding second target truncation threshold, match, fuse, and deduplicate the target centroids with the preferred fiber core points, and update the fiber core center region according to the processing results.

[0033] In a preferred embodiment, the distance clustering unit includes: The marking unit is used to mark the positions of all preferred core points to form a marking image; The third filtering unit is used to traverse the preferred fiber core points, obtain unclassified target preferred fiber core points, and add the target preferred fiber core points to the clustering sequence. An execution unit is used to retrieve the head of the clustering sequence as the current point, and add the current point to the corresponding temporary category point set according to the distance between the current point and each temporary category point set; The search unit is used to determine a first search radius based on the detection location, and search for unclassified neighboring points around the current point in the marked image based on the first search radius, and add the unclassified neighboring points to the temporary category point set to which the current point belongs; The control unit drives all the above units until all points in the clustering sequence have been classified, and adds each temporary category point set to the corresponding clustering array.

[0034] This invention also provides a core bundle center positioning terminal for a confocal microendoscopy, comprising the aforementioned computer-readable storage medium and a processor. When the processor executes the computer program on the computer-readable storage medium, it implements the steps of the above-described confocal microendoscopy core bundle center positioning method. Figure 3 This is a schematic diagram of the core bundle center positioning terminal of the confocal microendoscopy provided in Embodiment 3 of the present invention, as shown in the figure. Figure 3 As shown, the core bundle center positioning terminal 8 of the confocal microendoscopy system in this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various method embodiments described above, for example... Figure 1 The steps shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module in the above-described device embodiments, for example... Figure 2 The functions of the module shown.

[0035] For example, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 82 in the core bundle center positioning terminal 8 of the confocal microendoscopy.

[0036] The core bundle center positioning terminal 8 of the confocal microendoscopy may include, but is not limited to, a processor 80 and a readable storage medium 81. Those skilled in the art will understand that... Figure 3This is merely an example of the fiber core bundle center positioning terminal 8 of a confocal microendoscopy system and does not constitute a limitation on the fiber core bundle center positioning terminal 8 of a confocal microendoscopy system. It may include more or fewer components than shown, or combine certain components, or different components. For example, the fiber core bundle center positioning terminal of the confocal microendoscopy system may also include a power management module, a computing processing module, input / output devices, network access devices, a bus, etc.

[0037] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0038] The readable storage medium 81 can be an internal storage unit of the core bundle center positioning terminal 8 of the confocal microendoscopy, such as a hard disk or memory of the core bundle center positioning terminal 8. The readable storage medium 81 can also be an external storage device of the core bundle center positioning terminal 8 of the confocal microendoscopy, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard equipped on the core bundle center positioning terminal 8 of the confocal microendoscopy. Furthermore, the readable storage medium 81 can include both the internal storage unit of the core bundle center positioning terminal 8 of the confocal microendoscopy and an external storage device. The readable storage medium 81 is used to store the computer program and other programs and data required by the core bundle center positioning terminal of the confocal microendoscopy. The readable storage medium 81 can also be used to temporarily store data that has been output or will be output.

[0039] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0040] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0041] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0042] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0043] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0044] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0045] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. A method of core bundle center positioning for a confocal endomicroscope, characterized by, The method comprises the following steps: S1, obtaining a plurality of fiber end face images; S2, performing fusion processing on the plurality of fiber end face images to generate a reference image; S3, obtaining a core feature template corresponding to a target detection position, and performing convolution filtering on the reference image based on the core feature template to generate a core response image; S4, positioning a plurality of candidate core points of the core response image, and generating a core bundle estimation center after clustering the plurality of candidate core points.

2. The method of claim 1, wherein The candidate core points are a plurality of local maximum points of the core response image, and the generation of the core bundle estimation center specifically comprises: S401, positioning a plurality of local maximum points of the core response image to establish an initial local maximum point set; S402, obtaining pixel values of each local maximum point in the initial local maximum point set, and sorting all local maximum points according to the pixel value size to generate an optimized local maximum point set; S403, obtaining a first target cutoff threshold, and taking a target local maximum point with a pixel value greater than the first target cutoff threshold as a preferred core point; S404, performing distance clustering on the preferred core point, and taking a class with the largest number of clusters as a core center region; S405, positioning a center point of the core center region as the core bundle estimation center.

3. The core bundle center positioning method according to claim 2, wherein the candidate core points further comprise a plurality of centroid points corresponding to a plurality of connected regions in the core response image, a target centroid point with a pixel value greater than a corresponding second target cutoff threshold is obtained, the target centroid point is matched, fused and de-duplicated with the preferred core point, and the core center region is updated according to the processing result.

4. The method of claim 2 or 3, wherein In S401, the plurality of local maximum points of the core response image are positioned, specifically as follows: All pixel points of the core response image are traversed to obtain a target pixel point located in a preset mask; A plurality of neighborhood points of each target pixel point are determined, and if the brightness value of the target pixel point is greater than the brightness value of all neighborhood points in the neighborhood, the target pixel point is a local maximum point.

5. The method of claim 4, wherein In S403, the target cutoff threshold is obtained, specifically as follows: a first target cutoff number is generated according to a core number default value, the optimized local maximum point set is truncated based on the first target cutoff number, and the pixel value corresponding to the truncation point is taken as the first target cutoff threshold.

6. The method of claim 4, wherein In S404, the preferred core point is subjected to distance clustering, specifically as follows: The positions of all preferred core points are marked to form a marked image; All unclassified target preferred core points are obtained by traversing the preferred core points, and the target preferred core points are added to a cluster number sequence; The head of the cluster number sequence is called as a current point, and the current point is added to a corresponding temporary class point set according to the distance between the current point and each temporary class point set; A first search radius is determined according to the detection position, and unclassified adjacent points around the current point in the marked image are searched according to the first search radius, and the unclassified adjacent points are added to the temporary class point set to which the current point belongs. The above steps are repeated until all points of the cluster number sequence are classified, and each temporary class point set is added to the corresponding cluster array.

7. The method of claim 6, wherein The first search radius is determined according to the detection position, and specifically: A first preset mapping table is queried to obtain a search coefficient corresponding to the detection accuracy; A second preset mapping table is queried to obtain a core distance corresponding to the detection position; The first search radius is generated according to the search coefficient and the core distance.

8. A confocal endomicroscopy fiber bundle core center positioning device, characterized in that, The image acquisition module is configured to acquire a plurality of frames of fiber end face images; The fusion module is configured to perform fusion processing on the plurality of frames of fiber end face images to generate a reference image; The filtering module is configured to acquire a core feature template corresponding to a target detection position, and perform convolution filtering on the reference image based on the core feature template to generate a core response image; The positioning module is configured to locate a plurality of candidate core points of the core response image, and generate a core bundle estimation center after clustering the plurality of candidate core points. 9.A computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the core bundle center positioning method of the confocal microscopic endoscope according to any one of claims 1-7. 10.A core bundle center positioning terminal comprising the computer readable storage medium and the processor, wherein the processor executes the computer program stored in the computer readable storage medium to implement the steps of the core bundle center positioning method of the confocal microscopic endoscope according to any one of claims 1-7. ​

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