Cardiovascular surgery image processing system and method for image enhancement

Abstract

The invention provides an image-enhanced cardiovascular surgery image processing system and method, and relates to the technical field of image processing, and the method comprises the steps: obtaining cardiovascular image data; extracting structural components, texture components and noise components of the cardiovascular image data; enhancing the structural component of the cardiovascular image data to obtain a first structural enhanced component; extracting a local structure feature of the first structure enhancement component, and generating a second structure enhancement component; performing enhancement processing on texture components of the cardiovascular image data to obtain texture enhancement components; and fusing the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component to obtain cardiovascular enhanced image data. The method pays attention to enhancement processing of local details, avoids excessive enhancement of noise, and can improve the image quality.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an image-enhanced cardiovascular surgery image processing system and method. Background Art

[0002] Cardiovascular surgery images include medical images of the heart, arteries, veins and related structures, such as echocardiography, CT angiography (CTA), magnetic resonance imaging (MRI) and angiography, etc. The quality and clarity of these images directly affect the doctor's diagnostic accuracy and surgical results, so image enhancement technology plays a vital role in cardiovascular surgery.

[0003] Currently, the main method for image enhancement of cardiovascular surgery images is histogram equalization (HE). Histogram equalization improves the overall contrast of the image by evenly distributing the grayscale values ​​of pixels, and has a significant effect on improving the visibility of low-contrast areas in cardiovascular images.

[0004] However, histogram equalization equalizes all pixel values ​​of an image across the entire image range. Although it can enhance contrast, it is not ideal when processing local details and is prone to ignoring slight changes in local areas. In particular, in low-contrast areas, histogram equalization may over-enhance noise, causing the noise to be enhanced together with useful information in the image, resulting in a decrease in image quality, especially in noisy medical images. Summary of the invention

[0005] In order to solve the technical problems that the prior art is not ideal when processing local details, is prone to ignoring slight changes in local areas, may over-enhance noise, so that the noise and useful information in the image are enhanced together, resulting in a decrease in image quality, the present invention provides an image enhanced cardiovascular surgery image processing system and method.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] A first aspect of an embodiment of the present invention provides an image enhancement cardiovascular surgery image processing method, comprising:

[0008] S1: Obtain cardiovascular imaging data;

[0009] S2: extracting structural components, texture components and noise components of the cardiovascular image data;

[0010] S3: performing enhancement processing on the structural component of the cardiovascular image data to obtain a first structural enhancement component;

[0011] S4: extracting local structural features of the first structural enhancement component and generating a second structural enhancement component;

[0012] S5: performing enhancement processing on the texture components of the cardiovascular image data to obtain texture enhancement components;

[0013] S6: Fusing the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component to obtain cardiovascular enhanced image data.

[0014] A second aspect of an embodiment of the present invention provides an image-enhanced cardiovascular surgery image processing system, comprising:

[0015] processor;

[0016] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the image-enhanced cardiovascular surgical image processing method as described in the first aspect is implemented.

[0017] The third aspect

[0018] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the image enhancement cardiovascular surgical image processing method as described in the first aspect.

[0019] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0020] In the present invention, the structural components, texture components and noise components of the cardiovascular imaging data are extracted, the structural components and the texture components of the detail parts are enhanced respectively, and then fused with the noise components, focusing on the enhancement of local details to avoid excessive enhancement of noise and improve image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic flow chart of an image enhancement cardiovascular surgery image processing method provided by an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of the structure of an image-enhanced cardiovascular surgery image processing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0025] Reference Manual Attached Figure 1 , showing a flow chart of an image enhancement cardiovascular surgery image processing method provided by an embodiment of the present invention.

[0026] The embodiment of the present invention provides an image-enhanced cardiovascular surgery image processing method, which can be implemented by an image-enhanced cardiovascular surgery image processing device, which can be a terminal or a server. The processing flow of the image-enhanced cardiovascular surgery image processing method can include the following steps:

[0027] S1: Obtain cardiovascular imaging data.

[0028] Optionally, the cardiovascular imaging data may be echocardiography, CT angiography (CTA), magnetic resonance imaging (MRI), angiography, and the like.

[0029] S2: Extract the structural component, texture component and noise component of cardiovascular imaging data.

[0030] Among them, the structural components contain the main anatomical features in cardiovascular images, such as the outlines of blood vessels, the boundaries of heart chambers, etc. After special processing, these structural components can enable doctors to see important anatomical structures more clearly.

[0031] Among them, texture components contain details of cardiovascular tissue (such as subtle changes in myocardial fibers or arterial walls). Enhancing these texture components helps to highlight the tiny features of diseased tissue.

[0032] Among them, the noise component will affect the diagnostic accuracy of medical images. By separating the noise component, it can be processed using a denoising algorithm to reduce unnecessary interference and improve image clarity.

[0033] In a possible implementation, S2 specifically includes sub-steps S201 to S205:

[0034] S201: Filter the cardiovascular image data through Gaussian filtering to obtain a filtered image.

[0035] Among them, Gaussian filtering is a classic smoothing filtering method in image processing, which is mainly used to remove noise in an image while retaining the overall structure of the image. Its basic principle is to perform weighted averaging on the pixels in the image, and the distribution of weights obeys the Gaussian function, so as to achieve the purpose of smoothing the image. Gaussian filtering is a mature technology and will not be described in detail in the present invention.

[0036] S202: Extracting structural components of cardiovascular imaging data from the filtered image using a total variation image reconstruction algorithm.

[0037] Among them, the total variation image reconstruction algorithm (TV) is a technology commonly used for image denoising, reconstruction and enhancement, especially in removing noise while maintaining image edge details. The basic idea of ​​the total variation image reconstruction algorithm is to remove noise in the image by minimizing the total variation (TV) of the image while maintaining the edge details of the image. The total variation image reconstruction algorithm is a mature technology and will not be described in detail in this invention.

[0038] S203: Determine detail components of the cardiovascular image data according to the structural components of the cardiovascular image data.

[0039] Optionally, S203 specifically includes: determining the detail component of the cardiovascular imaging data according to the following formula:

[0040] I D =II S

[0041] Among them, I D represents the detail component, I represents the original cardiovascular imaging data, and I S Represents structural components.

[0042] S204: filtering the detail components of the cardiovascular image data through anisotropic diffusion filtering to obtain texture components of the cardiovascular image data.

[0043] Among them, Anisotropic Diffusion Filtering (ADF) is an advanced filtering technology for image denoising and smoothing, which aims to reduce the noise in the image while retaining the edges and important structural details to the maximum extent. The core idea of ​​anisotropic diffusion filtering is that during the smoothing process of the image, the flat areas (areas with more noise) should be smoothed more strongly, while the edge areas (areas with drastic grayscale changes) should retain details and reduce the smoothing effect. This is different from traditional isotropic filtering (such as Gaussian filtering), which performs the same smoothing operation in all areas (whether smooth areas or edge areas), resulting in blurred edges. Anisotropic diffusion filtering is a mature technology and will not be described in detail in this invention.

[0044] S205: Determine the noise component of the cardiovascular image data according to the texture component of the cardiovascular image data.

[0045] Optionally, S205 specifically includes: determining the noise component of the cardiovascular image data according to the following formula:

[0046] I N =I D -I T

[0047] Among them, I N represents the noise component, I D Indicates the detail component, I T Represents texture components.

[0048] In the present invention, by decomposing cardiovascular image data into structural, texture and noise components, noise interference can be reduced while retaining important image information, thereby significantly improving image contrast and clarity. This decomposition and processing method helps doctors observe cardiovascular structures and identify lesions more clearly, and provides more accurate imaging basis for surgical planning. In addition, it can enhance the performance of automated image analysis algorithms and improve the accuracy of diagnosis.

[0049] S3: Performing enhancement processing on the structural component of the cardiovascular image data to obtain a first structural enhancement component.

[0050] In a possible implementation, S3 specifically includes: performing enhancement processing on the structural component of the cardiovascular image data by using the CLAHE algorithm to obtain a first structural enhancement component.

[0051] Among them, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is an improved image enhancement method that is specifically used to enhance the local contrast of images, especially for those images with low contrast and blurred details. CLAHE is an improved version of the traditional Adaptive Histogram Equalization (AHE). By limiting the contrast of the local histogram, it avoids the over-enhancement phenomenon that may be caused by the AHE method. Especially in medical images with more noise, CLAHE can better balance the enhancement effect and noise control.

[0052] Specifically, S3 includes:

[0053] S301: Divide the input cardiovascular image into multiple small blocks (local areas), and process each small block separately. This allows each part of the image to be enhanced independently, avoiding excessive contrast enhancement or unevenness caused by global histogram equalization.

[0054] S302: Perform histogram equalization on the pixels in each small block, calculate the gray value distribution, and redistribute the gray value so that the contrast of the local area is improved.

[0055] S303: Contrast limitation By setting a threshold, the frequency (i.e., the number of pixels) of a gray level in the histogram cannot exceed the threshold. For the part exceeding the threshold, "clip" is performed to evenly distribute it to other gray levels. This step is to avoid the appearance of enhanced noise and local artifacts.

[0056] S304: After performing histogram equalization on each small block, discontinuous transition areas may be generated between small blocks. To solve this problem, CLAHE smoothes the pixels between adjacent blocks by bilinear interpolation, making the entire image look natural and coherent, avoiding block effects.

[0057] In the present invention, the CLAHE algorithm can significantly enhance the local details and contrast in cardiovascular images through adaptive local processing and contrast limitation, and is particularly suitable for the enhancement processing of structural components.

[0058] S4: extracting local structural features of the first structural enhancement component and generating a second structural enhancement component.

[0059] In a possible implementation, S4 specifically includes: extracting the local structural features of the first structural enhancement component through an STS operator, and generating a second structural enhancement component.

[0060] It should be noted that the first structural enhancement component (for example, the enhancement component obtained by the CLAHE algorithm) has improved the global contrast of the image and highlighted some important structural information. However, contrast enhancement alone may not be enough to capture finer local structural features, such as edges, corners, or texture details. The STS operator can further extract local significant features on this basis, especially strong edges and complex geometric shapes in the image. Through this operation, key structures in the image can be made clearer, such as vascular walls, vascular bifurcation points, heart valves, etc. in cardiovascular images.

[0061] Among them, the STS (Structure Tensor Saliency Detection) operator is an image processing method based on the structure tensor, which is used to extract local geometric structural features in the image, such as edges, corners, textures, etc. The STS operator can identify significant structural information in the image by calculating the local gradient changes of the image.

[0062] Specifically, given an image matrix, the structure tensor is:

[0063]

[0064] Among them, ST represents the structure tensor, w represents the local window, and E i represents the gradient in the i direction, E jrepresents the gradient in the j direction.

[0065] The eigenvalues ​​of the structure tensor provide more information about the local geometric structure of the input image. The specific calculation method of the eigenvalues ​​of the structure tensor is:

[0066]

[0067] Among them, v1 represents the first feature, which usually represents a strong edge or a significant feature of a local area in the image, and v2 represents the second feature, which usually represents a flat area or a direction with a small gradient change.

[0068] Furthermore, the STS operator is specifically:

[0069]

[0070] Here, STS represents the STS operator.

[0071] In the present invention, the STS operator is used to extract local structural features and generate a second structural enhancement component, which can further enhance key features in the image, such as edges, corners, and subtle structures. This enhancement method can better adapt to complex medical image processing requirements while retaining important information, reducing noise amplification, and enhancing local details. In particular, it can provide doctors with clearer and more accurate diagnostic evidence in cardiovascular image analysis.

[0072] S5: Performing enhancement processing on the texture component of the cardiovascular image data to obtain a texture enhancement component.

[0073] In a possible implementation, S5 specifically includes sub-steps S501 and S502:

[0074] S501: Extract edges of texture components of cardiovascular imaging data using the Canny edge detection algorithm.

[0075] Among them, the Canny edge detection algorithm is a classic and efficient edge detection method, which is widely used in the fields of computer vision and image processing. It accurately extracts the edges in the image through a series of steps while minimizing the interference of noise. The goal of Canny edge detection is to identify the significant edges in the image, ensure that the edge positioning is accurate, and the edge lines have good continuity. The Canny edge detection algorithm is a mature technology and will not be described in detail in this invention.

[0076] S502: Enhance the texture components of the cardiovascular image data according to the edge extraction result to obtain texture enhancement components:

[0077] I Ca =max(I T ,ICanny )

[0078] Among them, I Ca Represents the texture enhancement component, max represents a larger value, I T represents the texture component, I Canny Represents edge features extracted by the Canny edge detection algorithm.

[0079] S6: Fusing the first structure enhancement component, the second structure enhancement component, the texture enhancement component, and the noise component to obtain cardiovascular enhanced image data.

[0080] In a possible implementation, S6 specifically includes: fusing the first structure enhancement component, the second structure enhancement component, the texture enhancement component, and the noise component according to the following formula to obtain cardiovascular enhanced image data:

[0081] I E =λ1I CL +λ2I STS +λ3I Ca +λ4I N

[0082] Among them, I E Indicates cardiovascular enhanced imaging, I CL represents the first structural reinforcement component, I STS represents the second structural reinforcement component, I Ca represents the texture enhancement component, I N represents the noise component, λ1 represents the weight coefficient of the first structure enhancement component, λ2 represents the weight coefficient of the second structure enhancement component, λ3 represents the weight coefficient of the texture enhancement component, and λ4 represents the weight coefficient of the noise component.

[0083] In the present invention, by weighted fusion of the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component, cardiovascular enhanced image data is generated, so that the quality of cardiovascular images can be optimized at multiple levels to ensure a good balance between contrast enhancement, detail expression and noise control.

[0084] In a possible implementation manner, the weight coefficients of the first structure enhancement component, the second structure enhancement component, the texture enhancement component, and the noise component are determined by a firefly algorithm.

[0085] Specifically, set the fitness function of the firefly algorithm:

[0086]

[0087] Wherein, f represents the fitness function, θ represents the weight coefficient set, which includes the weight coefficients of the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component, V represents the variance of the original cardiovascular image, M represents the mean of the output cardiovascular enhanced image, and E out represents the entropy of the output cardiovascular enhanced image, E in represents the entropy of the original cardiovascular image, G out Represents the average gradient of the output cardiovascular enhanced image, G in represents the average gradient of the original cardiovascular image, PSNR represents the peak signal-to-noise ratio, and I E represents the output enhanced cardiovascular image, I represents the initial cardiovascular image, PSNR(I E ,I) represents the peak signal-to-noise ratio between the output cardiovascular enhanced image and the original cardiovascular image.

[0088] In the present invention, the fitness function combines entropy, average gradient and peak signal-to-noise ratio. By optimizing entropy and average gradient, it is possible to ensure that important details and edges in the image are retained or enhanced. This is crucial for the identification of important structures such as blood vessels and heart valves in cardiovascular images. By introducing PSNR as a measurement indicator, distortion caused by over-enhancement can be avoided, and the similarity between the enhanced image and the original image can be maintained. This is very important for avoiding false edges or false structures, especially in medical images, to avoid misleading doctors' judgment.

[0089] Initialize the firefly individuals, each firefly individual represents a feasible set of weight coefficients, each firefly individual is composed of multiple dimensional components, and each component represents the weight coefficient of the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component.

[0090] Compare each firefly individual with other individuals in the population, generate a random number u, and move according to the following adaptive movement rules:

[0091]

[0092] in, represents the position of the i-th firefly individual at the t+1th iteration, ω represents the inertia weight factor, represents the position of the i-th firefly individual at the t-th iteration, represents the position of the j-th firefly individual at the t-th iteration, β represents the attraction, α represents the step factor, sign represents the sign function, r represents a random number in the range of 0 to 1, Levy represents the Levy flight random step, exp represents the exponential function with the natural constant e as the base, b represents the logarithmic spiral shape parameter, l represents a uniform random number between -1 and 1, Rt represents the adaptive switching factor at the tth iteration, and u represents a uniform random number between 0 and 1.

[0093] In the present invention, the firefly individual can automatically select the search strategy according to the current global fitness change in different iterations through the adaptive switching factor. When the random number is greater than the adaptive switching factor, the Levy flight with randomness is adopted, which is a random step search method with long jump characteristics, allowing the firefly to explore a wider search space, thereby increasing the possibility of finding the global optimal solution. When the random number is less than the adaptive switching factor, the logarithmic spiral search is used, which is a refined local search method that can perform accurate local search near an already excellent solution to find the local optimal solution.

[0094] Optionally, the attraction is specifically:

[0095]

[0096] Among them, β0 represents the attraction between two fireflies when the distance between them is 0, exp represents the exponential function with the natural constant e as the base, γ represents the light intensity attraction coefficient, and r ij represents the Euclidean distance between the i-th firefly individual and the j-th firefly individual.

[0097] In the present invention, when the distance between two fireflies is small, the attraction is large, and the individuals will quickly approach each other, which helps to enhance the local search capability. Especially in the late stage of optimization, when the distance between individuals is small, enhancing the attraction can converge to the local optimal solution more quickly. Furthermore, when the distance between individuals is close, the solution can be quickly approached through the large attraction, and a fine search can be performed in the local area of ​​the solution space.

[0098] Optionally, the adaptive switching factor is specifically:

[0099]

[0100] in, represents the fitness value of the global best position at the t-1th iteration, Represents the fitness value of the global best position at the t-2th iteration.

[0101] In the present invention, an adaptive switching factor is used, and the search strategy can be dynamically adjusted according to the global best fitness value of the previous iterations to ensure that the most suitable search behavior is adopted at different stages. By comparing the changes in the global best fitness value of two iterations, it is decided whether to conduct more global exploration or focus on local search. If the fitness is significantly improved in the first two iterations, the algorithm may still be in the global exploration stage, and the algorithm tends to conduct more global searches (i.e., using methods such as Levy flight) to find new and better solutions. If the fitness improvement slows down, it indicates that the algorithm may have found some better local solutions, which are small at this time. The algorithm will focus more on local search (i.e., using strategies such as logarithmic spirals), so as to fine-tune the solution and improve the local convergence speed.

[0102] Optionally, the Levy flight random step size is specified as:

[0103]

[0104] Among them, u obeys The normal distribution of v follows The normal distribution of , λ represents the exponential parameter.

[0105]

[0106] Among them, τ is the standard Gamma function.

[0107] In the present invention, the benefit of using Levy flight random step size is that it can enhance the global search capability of the optimization algorithm, especially in complex, multi-peak search spaces, by helping the algorithm avoid falling into local optimal solutions through long jump behavior and increasing the probability of finding the global optimal solution.

[0108] After the firefly individual moves to a new location, update the individual fitness;

[0109] Determine whether the current number of iterations has reached the maximum number of iterations; if so, output the weight coefficient set representing the firefly individual with the highest current fitness; otherwise, return to continue iterating.

[0110] In the present invention, the weight coefficients of the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component are determined by the firefly algorithm, which can significantly improve the effect of cardiovascular image enhancement. The method combines global and local searches to adaptively adjust the weight coefficients, so that the algorithm can dynamically optimize different components of the image, thereby enhancing image details, improving contrast, suppressing noise, and ultimately improving the overall quality of the image, and reducing the complexity of manual parameter adjustment and improving optimization efficiency.

[0111] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0112] In the present invention, the structural components, texture components and noise components of cardiovascular imaging data are extracted, the structural components and the texture components of the detail parts are enhanced respectively, and then fused with the noise components, focusing on the enhancement of local details to avoid excessive enhancement of noise and improve image quality.

[0113] Reference Manual Attached Figure 2 , showing a structural schematic diagram of an image-enhanced cardiovascular surgery image processing system provided by the present invention.

[0114] The present invention further provides an image-enhanced cardiovascular surgery image processing system 20, which is applied to the above-mentioned image-enhanced cardiovascular surgery image processing method, comprising:

[0115] Processor 201.

[0116] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the cardiovascular surgery image processing method for image enhancement as described in the method embodiment is implemented.

[0117] The image-enhanced cardiovascular surgery image processing system 20 provided by the present invention can execute the above-mentioned image-enhanced cardiovascular surgery image processing method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on them.

[0118] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0119] In the present invention, the structural components, texture components and noise components of the cardiovascular imaging data are extracted, the structural components and the texture components of the detail parts are enhanced respectively, and then fused with the noise components, focusing on the enhancement of local details to avoid excessive enhancement of noise and improve image quality.

[0120] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0121] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0122] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the cardiovascular surgical image processing method for image enhancement as described in the method embodiment is implemented.

[0123] A computer-readable storage medium provided by the present invention can implement the steps and effects of the image-enhanced cardiovascular surgical image processing method of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0124] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0125] In the present invention, the structural components, texture components and noise components of the cardiovascular imaging data are extracted, the structural components and the texture components of the detail parts are enhanced respectively, and then fused with the noise components, focusing on the enhancement of local details to avoid excessive enhancement of noise and improve image quality.

[0126] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A cardiovascular surgery image processing method for image enhancement, characterized in that: include: S1: Obtain cardiovascular imaging data; S2: extracting structural components, texture components and noise components of the cardiovascular image data; S3: performing enhancement processing on the structural component of the cardiovascular image data to obtain a first structural enhancement component; S4: extracting local structural features of the first structural enhancement component and generating a second structural enhancement component; S5: performing enhancement processing on the texture components of the cardiovascular image data to obtain texture enhancement components; S6: Fusing the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component to obtain cardiovascular enhanced image data.

2. The image enhancement cardiovascular surgery image processing method according to claim 1, characterized in that: The S2 specifically includes: S201: filtering the cardiovascular image data by Gaussian filtering to obtain a filtered image; S202: extracting structural components of the cardiovascular imaging data from the filtered image using a total variation image reconstruction algorithm; S203: determining detail components of the cardiovascular image data according to the structural components of the cardiovascular image data; S204: filtering the detail components of the cardiovascular image data by anisotropic diffusion filtering to obtain texture components of the cardiovascular image data; S205: Determine the noise component of the cardiovascular image data according to the texture component of the cardiovascular image data.

3. The image enhancement cardiovascular surgery image processing method according to claim 2, characterized in that: The S203 is specifically as follows: A detail component of the cardiovascular image data is determined.

4. The image enhancement cardiovascular surgery image processing method according to claim 2, characterized in that: The S205 is specifically as follows: A noise component of the cardiovascular image data is determined.

5. The image enhancement cardiovascular surgery image processing method according to claim 1, characterized in that: The S3 is specifically: The structural component of the cardiovascular image data is enhanced by using the CLAHE algorithm to obtain a first structural enhancement component.

6. The image enhancement cardiovascular surgery image processing method according to claim 1, characterized in that: The S4 is specifically: The local structural features of the first structural enhancement component are extracted through the STS operator, and a second structural enhancement component is generated.

7. The image enhancement cardiovascular surgery image processing method according to claim 1, characterized in that: The S5 specifically includes: S501: extracting edges of texture components of the cardiovascular image data using a Canny edge detection algorithm; S502: Enhance the texture components of the cardiovascular image data according to the edge extraction result to obtain texture enhancement components.

8. The image enhancement cardiovascular surgery image processing method according to claim 1, characterized in that: The S6 is specifically: According to the following formula, the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component are fused to obtain cardiovascular enhanced image data.

9. The image enhancement cardiovascular surgery image processing method according to claim 8, characterized in that: The weight coefficients of the first structure enhancement component, the second structure enhancement component, the texture enhancement component and the noise component are determined by a firefly algorithm.

10. An image-enhanced cardiovascular surgical image processing system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the image-enhanced cardiovascular surgical image processing method according to any one of claims 1 to 9 is implemented.

Images