Self-adaptive image enhancement method and system based on magnetic resonance image

Abstract

The invention provides an adaptive image enhancement method and system based on a magnetic resonance imaging (MRI) image, and relates to the technical field of image processing, and the method comprises the steps: obtaining a global gradient map of the MRI image, and extracting an initial seed point set from the global gradient map according to a preset condition; gridding is carried out on the MRI image, and gradient information of the global gradient map is mapped to each block formed by gridding; calculating the comprehensive weight of each initial seed point in each block, screening the seed points according to a preset weight threshold value, and determining growth seed points in each block; for all the blocks, based on a set similarity criterion, carrying out region parallel expansion from the growth seed point, and terminating when the expansion meets a stop condition; when the adjacent blocks are overlapped, the adjacent blocks are combined, and the enhanced MRI image is finally output, so that the contrast ratio and the definition of the image are improved while the detail and edge information of the image are reserved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an adaptive image enhancement method based on magnetic resonance imaging and a system thereof. Background Art

[0002] Magnetic resonance imaging (MRI), as a non-invasive medical imaging technology, plays an important role in clinical diagnosis with its high soft tissue contrast and multi-plane imaging capabilities. However, MRI images are often affected by factors such as noise, artifacts, and inhomogeneous fields, resulting in reduced image quality and posing challenges to doctors' diagnosis. In order to improve the visualization and diagnostic accuracy of MRI images, adaptive image enhancement technology has emerged.

[0003] Traditional MRI image enhancement methods mostly rely on fixed filtering algorithms or histogram equalization techniques. These methods often do not work well when processing complex and changeable MRI images, and it is difficult to adaptively retain image details and suppress noise.

[0004] Therefore, it is necessary to provide an adaptive image enhancement method based on magnetic resonance imaging and a system thereof to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an adaptive image enhancement method and system based on magnetic resonance imaging, which overcomes the shortcomings of existing MRI image enhancement methods in processing complex and changeable images.

[0006] The present invention provides an adaptive image enhancement method based on magnetic resonance imaging, the method comprising the following steps: Acquire a global gradient map of the MRI image based on the gradient information, and extract an initial seed point set from the global gradient map according to preset conditions; Gridding the MRI image, and mapping the gradient information of the global gradient map to each block formed by the gridding; For each initial seed point in each block, calculate its comprehensive weight, and screen the seed points according to the preset weight threshold to determine the growth seed points in each block; For all blocks, based on the set similarity criteria, the region is expanded in parallel starting from the growth seed point, and the expansion is terminated when the stop condition is met; When overlapping areas appear in adjacent blocks, the adjacent blocks are merged based on the regional merging rules, and finally an enhanced MRI image is output.

[0007] Preferably, the step of acquiring a global gradient map of an MRI image based on gradient information, and extracting an initial seed point set from the global gradient map according to preset conditions, comprises: Calculate the gradient intensity and gradient direction of each pixel in the MRI image by using an edge detection algorithm; Integrate the calculated gradient strength and gradient direction into a global gradient map; Determine a local extreme point in the global gradient map as a candidate seed point; Candidate seed points are screened according to the preset gradient intensity threshold and grayscale value range, and candidate seed points that meet the conditions are retained as the initial seed point set.

[0008] Preferably, the gridding of the MRI image and mapping the gradient information of the global gradient map to each block formed by the gridding includes: The MRI image is divided into M×N uniform blocks, wherein the value of M is the quotient of the MRI image width and the preset basic grid size after rounding operation, and the preset gradient strength variance adjustment coefficient is superimposed for dynamic adjustment, and the value of N is the quotient of the MRI image height and the preset basic grid size after rounding operation, and the preset gradient strength variance adjustment coefficient is superimposed for dynamic adjustment; The gradient information of each pixel in the global gradient map is mapped to the corresponding block according to the spatial position.

[0009] Preferably, for each initial seed point in each block, calculating its comprehensive weight, and screening the seed points according to a preset weight threshold to determine the growth seed points in each block, includes: For each initial seed point in each block, the comprehensive weight of the initial seed point is calculated based on its gradient strength, grayscale value and gradient consistency characteristics of neighboring pixels, where the calculation formula of the comprehensive weight is: in, is the comprehensive weight, is the normalized value of the gradient intensity of the initial seed point, is the normalized grayscale value of the initial seed point, is the gradient consistency eigenvalue of the neighborhood pixels of the initial seed point, , , are the weight coefficients of gradient intensity, gray value and gradient consistency features, and satisfy ; The comprehensive weight is compared with a dynamic weight threshold, and initial seed points whose comprehensive weight is greater than the weight threshold are screened out as growth seed points in the block.

[0010] Preferably, for all blocks, based on a set similarity criterion, the region is expanded in parallel starting from the growth seed point, and the expansion is terminated when the expansion satisfies a stopping condition, including: For the growth seed point in each block, based on the similarity criterion, the pixels in the neighborhood of the growth seed point are expanded to add the neighborhood pixels that meet the similarity criterion to the current growth area, wherein the similarity criterion includes a gray value difference threshold and a gradient intensity difference threshold; Dynamically adjust the parameter value of the similarity criterion during the expansion process to adapt to the local characteristics of the target area; The expansion terminates when any of the following stop conditions are met: The number of newly added pixels in the current expansion area is lower than the preset minimum growth threshold; None of the boundary pixels of the current expansion area meet the similarity criterion; The area of ​​the current expansion area has reached the preset maximum area limit.

[0011] Preferably, the dynamically adjusting the parameter value of the similarity criterion comprises the following steps: Counting pixel features of the current growth area, wherein the pixel features include grayscale mean and gradient intensity mean; Update the parameter value of the similarity criterion according to the pixel features of the current growth area, including: The gray value difference threshold is updated to a multiple of the standard deviation of the gray value mean of the current growth area; The gradient intensity difference threshold is updated to a multiple of the standard deviation of the mean value of the gradient intensity in the current growth area.

[0012] Preferably, the merging of adjacent blocks based on the region merging rule includes: Detect the overlapping area of ​​adjacent block extension areas, and extract the grayscale mean, gradient direction histogram and texture feature vector of each pixel in the overlapping area; Calculate the grayscale distribution similarity, gradient direction histogram cosine similarity and texture feature Euclidean distance of adjacent blocks in the overlapping area; The block merge is performed when the following merge conditions are met at the same time: The grayscale distribution similarity is greater than the preset grayscale similarity threshold. The cosine similarity of the gradient direction histogram is greater than the preset direction consistency threshold. And the Euclidean distance of texture features is less than a preset texture difference threshold.

[0013] The present invention also provides an adaptive image enhancement system based on magnetic resonance imaging, which is used to perform an adaptive image enhancement method based on magnetic resonance imaging, and the system comprises: An initial seed point determination module is used to obtain a global gradient map of the MRI image based on gradient information, and extract an initial seed point set from the global gradient map according to preset conditions; A gridding module, used for gridding the MRI image and mapping the gradient information of the global gradient map to each block formed by the gridding; A growth seed determination module is used to calculate the comprehensive weight of each initial seed point in each block, and screen the seed points according to a preset weight threshold to determine the growth seed points in each block; A region growing module, for performing parallel region expansion starting from the growth seed point for all blocks based on a set similarity criterion, and terminating when the expansion meets a stop condition; The region adjustment module is used to merge adjacent blocks based on the region merging rules when overlapping regions appear in adjacent blocks, and finally output an enhanced MRI image.

[0014] Compared with the related art, the adaptive image enhancement method based on magnetic resonance imaging and the system thereof provided by the present invention have the following beneficial effects: First, the global gradient map of the MRI image is obtained based on the gradient information, and the initial seed point set is extracted from the global gradient map according to the preset conditions to ensure that the selection of seed points is both accurate and efficient. Then, the MRI image is gridded, and the gradient information of the global gradient map is mapped to each block for subsequent regional expansion. During the regional expansion process, the method determines the growth seed point by calculating the comprehensive weight of each initial seed point and screening the seed points according to the preset weight threshold. At the same time, the region is expanded in parallel from the growth seed point based on the set similarity criterion, and the parameter value of the similarity criterion is dynamically adjusted during the expansion process to adapt to the local characteristics of the target area. Finally, when overlapping areas appear in adjacent blocks, the adjacent blocks are merged based on the regional merging rules to ensure that the enhanced MRI image maintains continuity and consistency globally. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of an adaptive image enhancement method based on magnetic resonance imaging provided by the present invention; Figure 2 A module structure diagram of an adaptive image enhancement system based on magnetic resonance imaging provided by the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention, rather than all structures, are shown in the accompanying drawings. In addition, the embodiments of the present invention and the features in the embodiments may be combined with each other without conflict.

[0017] It should also be noted that, for ease of description, only the parts related to the present invention, but not all of the contents, are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. Embodiment 1

[0018] The present invention provides an adaptive image enhancement method based on magnetic resonance imaging. Figure 1 As shown, the method comprises the following steps: S1: Acquire a global gradient map of the MRI image based on gradient information, and extract an initial seed point set from the global gradient map according to preset conditions.

[0019] Wherein, step S1 specifically includes the following steps: S11: Calculate the gradient intensity and gradient direction of each pixel in the MRI image by using an edge detection algorithm.

[0020] In this embodiment, an edge detection algorithm (including but not limited to the Sobel operator) is used to perform pixel-by-pixel gradient analysis on the MRI image. The spatial derivatives are calculated by the convolution kernels in the horizontal and vertical directions, the gradient strength is calculated, and the gradient direction is determined. This process suppresses noise interference through Gaussian filtering preprocessing, and effectively distinguishes the grayscale transition areas of different tissues while retaining the edge features of the anatomical structure. The technical effect is reflected in the accurate quantification of the edge significance of each position in the image, providing a mathematical representation of the spatial gradient features for subsequent seed point extraction.

[0021] S12: Integrate the calculated gradient strength and gradient direction into a global gradient map.

[0022] In this embodiment, the structured integration of gradient information is achieved by constructing a dual-channel data matrix. The gradient intensity of each pixel is mapped to the amplitude channel of the matrix, and the gradient direction is encoded as an angle channel to form a global gradient map with a spatial correspondence. This process uses normalization to scale the gradient intensity to a range of 0-255, which is convenient for subsequent threshold processing. The technical advantage is to establish a unified spatial reference framework, so that the gradient distribution characteristics of each area can be quickly located during subsequent block processing, while reducing the resource consumption caused by repeated calculations.

[0023] S13: Determine local extreme points in the global gradient map as candidate seed points.

[0024] In this embodiment, a sliding window mechanism is used to search for local extreme values ​​on the gradient intensity map. A 3×3 neighborhood window is set to traverse the entire image, and the pixel with the largest gradient intensity in the center of the window is retained as a candidate seed point through a non-maximum suppression algorithm. This process introduces eight-neighborhood directional interpolation technology to perform sub-pixel extreme value judgment along the gradient direction to improve edge positioning accuracy. This effectively screens out the key feature points of the true anatomical boundary, eliminates the pseudo-edge response caused by gradient diffusion, and makes the candidate seed points concentrated in the strong gradient area at the junction of tissues.

[0025] S14: Screening candidate seed points according to a preset gradient intensity threshold and grayscale value range, and retaining candidate seed points that meet the conditions as an initial seed point set.

[0026] In this embodiment, the seed point set is optimized by dual constraints. First, an adaptive gradient threshold is applied. ( is the global gradient mean, is the standard deviation, The preset adjustment coefficient is obtained experimentally) for initial screening as the global gradient mean to remove noise points in low-contrast areas; then a secondary screening is performed in combination with the prior grayscale range of the target tissue to exclude artifact interference in non-focus areas. This hierarchical screening mechanism dynamically adjusts the threshold parameters to significantly reduce the false positive rate while ensuring sensitivity, so that the spatial distribution density of the final seed point set matches the complexity of the target anatomical structure, providing high-quality initial points for subsequent regional growth.

[0027] S2: gridding the MRI image, and mapping the gradient information of the global gradient map to each block formed by gridding.

[0028] Wherein, step S2 comprises the following steps: S21: Divide the MRI image into M×N uniform blocks, wherein the value of M is the quotient of the MRI image width and the preset basic grid size, which is rounded off and then dynamically adjusted by superimposing the preset gradient strength variance adjustment coefficient; the value of N is the quotient of the MRI image height and the preset basic grid size, which is rounded off and then dynamically adjusted by superimposing the preset gradient strength variance adjustment coefficient.

[0029] In this embodiment, the MRI image is divided into M×N uniform blocks, and the number of blocks is directly controlled by the preset gradient strength variance adjustment coefficient. In specific implementation, the image is first preliminarily divided according to the preset basic grid size (e.g., 16×16 pixels): the original M value is the image width divided by the basic grid size and rounded down, and the original N value is the image height divided by the same basic grid size and rounded down. Subsequently, the preset gradient strength variance adjustment coefficient (e.g., 1.2 or 0.8) is multiplied by the original M and N values ​​to obtain the final number of blocks.

[0030] For example, if the original image block is 20×15 and the adjustment coefficient is 1.2, the number of blocks after adjustment is 24×18. This coefficient is a pre-set fixed value, and its size directly reflects the block density requirement. If the coefficient is greater than 1, the blocks are denser to enhance local processing accuracy; if the coefficient is less than 1, the number of blocks is reduced to reduce computational complexity. After the block division is completed, the size of each block may no longer be completely uniform, but the overall grid structure is maintained.

[0031] S22: Mapping the gradient information of each pixel in the global gradient map to the corresponding block according to the spatial position.

[0032] In this embodiment, the coordinate range of each block is determined by the block division result of step S21. The system traverses each pixel in the global gradient map and fills the gradient strength and direction data into the corresponding block according to its coordinates.

[0033] For example, if the coordinates of the upper left corner of a block are (x1, y1) and the lower right corner is (x2, y2), then all pixel gradient information that satisfies x1≤ x < x2 and y1 ≤ y < y2 is assigned to the block. Each block independently stores the gradient intensity matrix and gradient direction histogram to form a local gradient feature description. This mapping process is achieved by establishing a block index table to ensure efficient data access.

[0034] S3: For each initial seed point in each block, calculate its comprehensive weight, and screen the seed points according to a preset weight threshold to determine the growth seed points in each block.

[0035] Wherein, step S3 specifically includes the following steps: S31: For each initial seed point in each block, the comprehensive weight of the initial seed point is calculated based on its gradient strength, grayscale value and gradient consistency characteristics of neighboring pixels, wherein the calculation formula of the comprehensive weight is: in, is the comprehensive weight, is the normalized value of the gradient intensity of the initial seed point, is the normalized grayscale value of the initial seed point, is the gradient consistency eigenvalue of the neighborhood pixels of the initial seed point, , , are the weight coefficients of gradient intensity, gray value and gradient consistency features, and satisfy .

[0036] In this embodiment, the normalized value of the gradient intensity is obtained by linearly mapping the ratio of the gradient intensity of the seed point to the maximum gradient intensity in the block, ensuring that its range is between 0 and 1. The grayscale value normalization value is obtained by mapping the grayscale value of the seed point to the percentage position of the grayscale dynamic range of the entire MRI image. For example, if the image grayscale range is 0-255, and the grayscale value of a pixel is 128, the normalization result is 0.5. The calculation of the gradient consistency eigenvalue is quantified by analyzing the gradient direction difference of the pixels in the 3×3 neighborhood around the seed point, using the inverse function of the standard deviation of the directional angle: first calculate the standard deviation of the gradient direction of each pixel in the neighborhood, and then convert it into a consistency index by 1 / (1+standard deviation). Finally, the three normalized eigenvalues ​​are substituted into the weighted formula, where the weight coefficients α, β, and γ are dynamically adjusted by the gradient intensity variance of the grid block, giving the gradient feature a higher weight in the complex texture area (high variance), and increasing the weight proportion of the grayscale feature in the uniform area (low variance) to achieve adaptive feature fusion.

[0037] S32: Compare the comprehensive weight with a dynamic weight threshold, and select initial seed points whose comprehensive weight is greater than the weight threshold as growth seed points in the block.

[0038] In this embodiment, the weight threshold includes a basic threshold, which is determined based on the median of the comprehensive weights of all initial seed points in the block, and a compensation item for the mean gradient intensity of the block is superimposed at the same time: for blocks whose gradient intensity mean is 20% higher than the overall image mean, the threshold is automatically reduced by 5% to retain more edge seeds; otherwise, the threshold is increased by 3% to suppress noise interference.

[0039] Spatial continuity constraints are used during screening. When there are seed points that have passed the screening within the 8 neighborhoods around a certain sub-point, the threshold is temporarily relaxed by 10% to ensure the density of seed points in important edge areas. Through this screening mechanism, isolated noise points (such as pseudo-gradient points generated by random noise) can be effectively filtered out, while real tissue boundary seed points with spatial continuity can be retained, providing a high-quality starting point for subsequent regional growth.

[0040] S4: For all blocks, based on the set similarity criterion, the region is expanded in parallel starting from the growth seed point, and the expansion is terminated when the stop condition is met.

[0041] Wherein, step S4 specifically includes the following steps: S41: For each growth seed point in each block, based on the similarity criterion, expand from the growth seed point to its neighboring pixels, and add the neighboring pixels that meet the similarity criterion to the current growth area, wherein the similarity criterion includes a gray value difference threshold and a gradient intensity difference threshold.

[0042] In this embodiment, for the 8 neighborhood pixels around the current growth seed point, the absolute difference in grayscale mean and the absolute difference in gradient intensity between the 8 neighborhood pixels and the region where the seed point is located are calculated. If the grayscale difference of a neighborhood pixel is less than the grayscale value difference threshold and the gradient difference is less than the gradient intensity difference threshold, the pixel is included in the current growth region and marked as a newly added pixel. This process continues to expand outward in an iterative manner, gradually merging pixels that meet the similarity conditions into the same region, which can effectively capture the continuity characteristics of the target region, reduce the misjudgment of noise pixels, and ensure the smoothness and consistency of the region boundary.

[0043] S42: dynamically adjusting the parameter value of the similarity criterion during the expansion process to adapt to the local characteristics of the target area; The expansion terminates when any of the following stop conditions are met: The number of newly added pixels in the current expansion area is lower than the preset minimum growth threshold; None of the boundary pixels of the current expansion area meet the similarity criterion; The area of ​​the current expansion area has reached the preset maximum area limit.

[0044] The dynamically adjusting the parameter value of the similarity criterion includes the following steps: Count the pixel features of the current growth area, where the pixel features include a grayscale mean and a gradient intensity mean.

[0045] Update the parameter value of the similarity criterion according to the pixel features of the current growth area, including: The gray value difference threshold is updated to a multiple of the standard deviation of the gray value mean of the current growth area; The gradient intensity difference threshold is updated to a multiple of the standard deviation of the mean value of the gradient intensity in the current growth area.

[0046] During the expansion process, the parameters of the similarity criterion are dynamically adjusted according to the local characteristics of the current growing area. Specifically, after each expansion, the grayscale mean and gradient intensity mean of the pixels in the area are counted, and their standard deviations are calculated. Subsequently, the grayscale value difference threshold is updated to 1.5 times the standard deviation of the grayscale mean of the current area, and the gradient intensity difference threshold is updated to 1.2 times the standard deviation of the gradient intensity mean. This dynamic adjustment strategy enables the algorithm to adapt to the heterogeneity of different tissues or lesion areas. For example, in the tumor edge area with large grayscale changes, the threshold is automatically relaxed to accommodate larger local differences; while in the cerebrospinal fluid area with uniform grayscale, the threshold is tightened to avoid over-segmentation. By real-time feedback of local statistical features, the adaptability of the algorithm to complex MRI images is enhanced.

[0047] The termination condition of region expansion is achieved through triple judgment. First, if the number of newly added pixels in a single expansion is lower than the preset minimum growth threshold (such as 5 pixels), the region growth is judged to be saturated and the expansion is terminated to avoid invalid calculations. Secondly, if all boundary pixels of the current expansion area do not meet the similarity criterion, it indicates that the area has been completely surrounded and cannot be extended further, and it stops immediately. Finally, when the area of ​​the region exceeds the preset maximum limit (such as 60% of the block area), it is forced to terminate to prevent excessive expansion across blocks from affecting the subsequent merging logic. These three conditions work together to ensure the complete capture of key areas, effectively control the computational complexity, and avoid global structural distortion caused by local overgrowth, thereby achieving a balance between efficiency and accuracy.

[0048] S5: When overlapping areas appear in adjacent blocks, the adjacent blocks are merged based on the regional merging rule, and an enhanced MRI image is finally output.

[0049] Wherein, step S5 comprises the following steps: S51: Detect the overlapping area of ​​the adjacent block extension areas, and extract the grayscale mean, gradient direction histogram and texture feature vector of each pixel in the overlapping area.

[0050] S52: Calculate the grayscale distribution similarity, gradient direction histogram cosine similarity and texture feature Euclidean distance of adjacent blocks in the overlapping area.

[0051] S53: merging blocks when the following merging conditions are met at the same time: The grayscale distribution similarity is greater than the preset grayscale similarity threshold. The cosine similarity of the gradient direction histogram is greater than the preset direction consistency threshold. And the Euclidean distance of texture features is less than a preset texture difference threshold.

[0052] In this embodiment, the set of overlapping pixels at the boundary of adjacent blocks is first determined by comparing the coordinate range. For each overlapping pixel, its grayscale value, gradient direction angle (based on the directional channel data of the global gradient map) and texture feature vector (calculated using the local binary pattern LBP algorithm) are extracted. The grayscale mean is obtained by calculating the grayscale arithmetic mean of all pixels in the overlapping area; the gradient direction histogram is obtained by dividing the direction angle into 36 bins with a frequency interval of 10 degrees to form a 36-dimensional histogram vector; the texture feature is generated by the LBP operator of the 3×3 window to generate an 8-bit binary code, and the LBP mode distribution histogram of all pixels in the statistical area is used as a 128-dimensional feature vector. This process provides a quantitative basis for structure, direction and texture for subsequent similarity measurement through the multi-dimensional characterization of spatial features, effectively avoiding the limitations of a single feature criterion.

[0053] In the similarity calculation stage, the grayscale distribution similarity uses the Bhattacharyya distance to measure the grayscale histogram matching degree of the overlapping areas of two blocks. The smaller the value, the closer the distribution. The cosine similarity of the gradient direction histogram is obtained by calculating the dot product after normalizing the two 36-dimensional histogram vectors, reflecting the spatial consistency of the gradient direction distribution. The Euclidean distance of the texture feature directly calculates the L2 norm distance between the two 128-dimensional LBP feature vectors. In the specific implementation, the grayscale similarity threshold is set to 0.8 (Bhattacharyya distance ≤ 0.2 corresponds to similarity ≥ 0.8), the direction consistency threshold is set to 0.75, and the texture difference threshold is set to 120. These parameters are optimized through training with a large number of medical image samples to ensure robustness in scenarios such as brain tissue segmentation and tumor edge enhancement. This multi-feature fusion measurement method significantly improves the reliability of regional merging decisions, especially in areas with blurred tissue boundaries or noise interference, it can accurately identify the true anatomical continuity.

[0054] When the merging conditions are met, the block region fusion operation is performed. First, the pixels in the overlapping area are marked as common areas and uniformly assigned to the dominant block with a larger area in the adjacent blocks. Subsequently, the bilinear interpolation method is used to smooth the grayscale transition at the merge boundary, and the gradient consistency weight of the boundary pixels is recalculated based on the gradient direction histogram of the merged area. This process synchronously updates the block index table to ensure data consistency in subsequent processing stages. Through this merging mechanism based on multi-dimensional feature similarity, the pseudo-boundaries introduced by artificial blocks can be effectively eliminated, the integrity of continuous anatomical structures can be restored, and the clear boundary between the lesion area and normal tissue can be retained. The final output enhanced image achieves global structural coherence while maintaining local detail sharpness, providing highly reliable imaging support for clinical diagnosis. Embodiment 2

[0055] The present invention also provides an adaptive image enhancement system based on magnetic resonance imaging, which is used to perform an adaptive image enhancement method based on magnetic resonance imaging, referring to Figure 2 As shown, the system comprises: The initial seed point determination module 100 is used to obtain a global gradient map of the MRI image based on gradient information, and extract an initial seed point set from the global gradient map according to preset conditions.

[0056] The gridding module 200 is used to grid the MRI image and map the gradient information of the global gradient map to each block formed by gridding.

[0057] The growth seed determination module 300 is used to calculate the comprehensive weight of each initial seed point in each block, and screen the seed points according to a preset weight threshold to determine the growth seed points in each block.

[0058] The region growing module 400 is used to perform region parallel expansion starting from the growth seed point for all blocks based on a set similarity criterion, and terminate when the expansion meets a stopping condition.

[0059] The region adjustment module 500 is used to merge adjacent blocks based on the region merging rule when overlapping regions appear between adjacent blocks, and finally output an enhanced MRI image.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0062] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. An adaptive image enhancement method based on magnetic resonance imaging, characterized in that: The method comprises the following steps: Acquire a global gradient map of the MRI image based on the gradient information, and extract an initial seed point set from the global gradient map according to preset conditions; Gridding the MRI image, and mapping the gradient information of the global gradient map to each block formed by the gridding; For each initial seed point in each block, calculate its comprehensive weight, and screen the seed points according to the preset weight threshold to determine the growth seed points in each block; For all blocks, based on the set similarity criteria, the region is expanded in parallel starting from the growth seed point, and the expansion is terminated when the stop condition is met; When overlapping areas appear in adjacent blocks, the adjacent blocks are merged based on the regional merging rules, and finally an enhanced MRI image is output.

2. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 1, characterized in that: The step of acquiring a global gradient map of the MRI image based on the gradient information and extracting an initial seed point set from the global gradient map according to a preset condition includes: Calculate the gradient intensity and gradient direction of each pixel in the MRI image by using an edge detection algorithm; Integrate the calculated gradient strength and gradient direction into a global gradient map; Determine a local extreme point in the global gradient map as a candidate seed point; Candidate seed points are screened according to the preset gradient intensity threshold and grayscale value range, and candidate seed points that meet the conditions are retained as the initial seed point set.

3. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 2, characterized in that: The gridding of the MRI image and mapping the gradient information of the global gradient map to each block formed by the gridding includes: The MRI image is divided into M×N uniform blocks, wherein the value of M is the quotient of the MRI image width and the preset basic grid size after rounding operation, and the preset gradient strength variance adjustment coefficient is superimposed for dynamic adjustment, and the value of N is the quotient of the MRI image height and the preset basic grid size after rounding operation, and the preset gradient strength variance adjustment coefficient is superimposed for dynamic adjustment; The gradient information of each pixel in the global gradient map is mapped to the corresponding block according to the spatial position.

4. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 3, characterized in that: The step of calculating the comprehensive weight of each initial seed point in each block, and screening the seed points according to a preset weight threshold to determine the growth seed points in each block includes: For each initial seed point in each block, the comprehensive weight of the initial seed point is calculated based on its gradient strength, grayscale value and gradient consistency characteristics of neighboring pixels, where the calculation formula of the comprehensive weight is: in, is the comprehensive weight, is the normalized value of the gradient intensity of the initial seed point, is the normalized grayscale value of the initial seed point, is the gradient consistency eigenvalue of the neighborhood pixels of the initial seed point, , , are the weight coefficients of gradient intensity, gray value and gradient consistency features, and satisfy ; The comprehensive weight is compared with a dynamic weight threshold, and initial seed points whose comprehensive weight is greater than the weight threshold are screened out as growth seed points in the block.

5. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 4, characterized in that: For all the blocks, based on the set similarity criterion, the regions are expanded in parallel from the growth seed point, and the expansion is terminated when the stop condition is met, including: For the growth seed point in each block, based on the similarity criterion, the pixels in the neighborhood of the growth seed point are expanded to add the neighborhood pixels that meet the similarity criterion to the current growth area, wherein the similarity criterion includes a gray value difference threshold and a gradient intensity difference threshold; Dynamically adjust the parameter value of the similarity criterion during the expansion process to adapt to the local characteristics of the target area; The expansion terminates when any of the following stop conditions are met: The number of newly added pixels in the current expansion area is lower than the preset minimum growth threshold; None of the boundary pixels of the current expansion area meet the similarity criterion; The area of ​​the current expansion area has reached the preset maximum area limit.

6. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 5, characterized in that: The method of dynamically adjusting the parameter value of the similarity criterion comprises the following steps: Counting pixel features of the current growth area, wherein the pixel features include grayscale mean and gradient intensity mean; Update the parameter value of the similarity criterion according to the pixel features of the current growth area, including: The gray value difference threshold is updated to a multiple of the standard deviation of the gray value mean of the current growth area; The gradient intensity difference threshold is updated to a multiple of the standard deviation of the mean value of the gradient intensity in the current growth area.

7. The method for adaptive image enhancement based on magnetic resonance imaging according to claim 6, characterized in that: The merging of adjacent blocks based on the region merging rule includes: Detect the overlapping area of ​​adjacent block extension areas, and extract the grayscale mean, gradient direction histogram and texture feature vector of each pixel in the overlapping area; Calculate the grayscale distribution similarity, gradient direction histogram cosine similarity and texture feature Euclidean distance of adjacent blocks in the overlapping area; The block merge is performed when the following merge conditions are met at the same time: The grayscale distribution similarity is greater than the preset grayscale similarity threshold. The cosine similarity of the gradient direction histogram is greater than the preset direction consistency threshold. And the Euclidean distance of texture features is less than a preset texture difference threshold.

8. An adaptive image enhancement system based on magnetic resonance imaging, used to execute the adaptive image enhancement method based on magnetic resonance imaging according to any one of claims 1 to 7, characterized in that: The system comprises: An initial seed point determination module is used to obtain a global gradient map of the MRI image based on gradient information, and extract an initial seed point set from the global gradient map according to preset conditions; A gridding module, used for gridding the MRI image and mapping the gradient information of the global gradient map to each block formed by the gridding; A growth seed determination module is used to calculate the comprehensive weight of each initial seed point in each block, and screen the seed points according to a preset weight threshold to determine the growth seed points in each block; A region growing module, for performing parallel region expansion starting from the growth seed point for all blocks based on a set similarity criterion, and terminating when the expansion meets a stop condition; The region adjustment module is used to merge adjacent blocks based on the region merging rules when overlapping regions appear in adjacent blocks, and finally output an enhanced MRI image.

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