DSA coronary artery stenosis analysis method based on matching degree measurement

Abstract

The invention provides a DSA (Digital Subtraction Angiography) coronary artery stenosis analysis method based on matching degree measurement. According to the method, image preprocessing, catheter calibration, blood vessel segmentation and center line extraction are carried out on a DSA image, an improved MoM (Measure of Match) model is combined to accurately measure the diameter of a blood vessel based on the matching degree, and the stenosis rate of a stenosis area is calculated. The method has an automatic correction function, can automatically correct the narrow area according to the reference diameter of the normal blood vessel after the narrow detection, and improves the detection precision and stability. The method is suitable for rapid and accurate analysis of coronary artery stenosis, can effectively reduce manual operation errors, and is high in degree of automation.

Description

Technical Field

[0001] The present invention relates to the field of medical image analysis, and in particular to a method for analyzing coronary artery stenosis using digital subtraction angiography (DSA) based on matching degree measurement, which is suitable for automated vascular stenosis detection and correction, and improves detection accuracy and efficiency. Background Art

[0002] Coronary artery disease is one of the leading causes of death and disability worldwide. Coronary artery stenosis, especially severe stenosis, can hinder blood flow and cause myocardial ischemia, leading to serious consequences such as angina pectoris and myocardial infarction. Therefore, accurate detection of the location and degree of coronary artery stenosis is of great clinical significance for the diagnosis and treatment of cardiovascular diseases.

[0003] Digital subtraction angiography (DSA) is the gold standard method for detecting cardiovascular stenosis and can provide high-resolution images of vascular structures. However, traditional DSA analysis relies on the doctor's experience and subjective judgment, and has certain limitations and errors. Especially when processing large-scale imaging data, manual analysis is time-consuming and laborious, and the results are easily affected by the doctor's subjective operation and judgment. In recent years, with the rapid development of medical image processing technology, computer-based automated vascular analysis methods have been widely studied to improve detection accuracy and reduce human errors.

[0004] Although the automated DSA analysis methods in the prior art help to improve efficiency to a certain extent, they generally have some significant deficiencies. Specifically, these deficiencies include the accuracy of vascular segmentation under complex backgrounds, that is, the vascular segmentation algorithm is easily interfered by noise, resulting in inaccurate segmentation of vascular contours, especially in low-contrast images, where it is particularly difficult to accurately segment the edges of blood vessels; stenosis detection errors, especially when the stenosis area is discontinuous or only slightly stenotic, the existing methods often miss detection or misjudge; and lack of effective automatic correction function. Even if the stenosis area is detected, its automatic correction function is limited and still needs to rely on manual operation for further adjustment, and high-precision automatic correction cannot be achieved. In addition, in the detection of coronary artery stenosis, the inaccuracy of the catheter calibration factor may also lead to deviations in the calculation results of the degree of stenosis, thereby affecting subsequent clinical judgments. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the prior art in the detection of coronary artery stenosis and propose a DSA coronary artery stenosis analysis method based on matching degree measurement. By combining automated image processing, precise stenosis measurement and intelligent correction, an efficient, accurate and automated coronary artery stenosis analysis system is provided. The present invention not only improves the accuracy of image analysis and reduces subjective errors, but also greatly improves the detection efficiency, making the stenosis detection and correction process more automated and intelligent.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: a DSA coronary artery stenosis analysis method based on matching degree measurement, the specific steps are as follows:

[0007] Step 1: DSA image reading and preprocessing.

[0008] First, the system reads DSA images from the image database and supports multiple formats, especially the processing of DICOM sequence images. In order to ensure the image processing effect, the system provides the function of adjusting the window and window level to make the image contrast and brightness meet the needs of the application user.

[0009] (1) Calculation of window width and window position: The window width (Width) is defined as the difference between the maximum and minimum CT values ​​in the image, which is used to determine the brightness range of the image. The calculation formula is:

[0010] Width=CT max -CT min

[0011] The window level is defined as the average of the maximum and minimum CT values ​​and is used to determine the median brightness of the image. The calculation formula is:

[0012]

[0013] (2) Image standardization: After calculating the window width and window position, the system will standardize the image. The image brightness will be adjusted according to the calculated window width and window position, and the CT value of the image will be mapped to the range of [0,255] to facilitate subsequent processing and analysis.

[0014] Step 2, catheter calibration. In DSA images, accurate measurement of blood vessel diameters is premised on accurate catheter calibration. The purpose of catheter calibration is to calculate a calibration factor by marking and measuring the catheter area to convert the pixel size in the image to the actual physical size. The steps of catheter calibration are as follows:

[0015] (1) Marking the catheter area: First, determine the catheter's marking points in the DSA image. The catheter is usually an important reference object in the image. The system marks the starting and ending points of the catheter through automation or user interaction. Based on these marking points, the system can extract the catheter's contour and determine the catheter's edge area.

[0016] (2) Calculate the catheter diameter: Next, the system calculates the diameter of the catheter by extracting the contour of the catheter area. This diameter is calculated based on the pixel value of the catheter in the image and may vary in actual applications due to different image resolution and viewing angles.

[0017] (3) Calculation of calibration factor: In order to convert the pixel size in the image to the actual physical size, the calibration factor of the catheter needs to be calculated. The calibration factor κ is calculated by the following formula:

[0018]

[0019] Among them, D actual is the actual diameter of the catheter (usually in millimeters), and D pixel is the diameter of the catheter in the image (in pixels). This calibration factor can be used to convert the vessel diameter value in the image to its actual physical size.

[0020] (4) Application of calibration factor: After the catheter is calibrated, the calibration factor is applied to the subsequent vessel diameter measurement. This factor converts the pixel-level size of the vessel into the actual size, thus ensuring the accuracy of the calculation of the degree of vascular stenosis.

[0021] The catheter calibration step is a key link in the entire vascular analysis process. It can effectively reduce errors caused by factors such as image scale, viewing angle and resolution, thereby providing an accurate basis for subsequent vascular analysis.

[0022] Step 3: Blood vessel segmentation and centerline extraction: The system automatically detects the user's manually marked points on the blood vessel, determines the stenosis area to be detected, and performs segmentation and centerline extraction on the blood vessels in the area.

[0023] The blood vessel segmentation in the present invention is achieved through adaptive thresholding technology, which dynamically adjusts the threshold based on the change of pixel intensity in the local area to ensure that the blood vessel area can be accurately segmented under different contrast and lighting conditions.

[0024] Adaptive Thresholding:

[0025] For each pixel, its neighborhood is defined as a window of size N×N, which contains the pixel information of the pixel and its neighborhood. The average value of the pixel value in the area is μ local Calculated by the following formula:

[0026]

[0027] Among them, I(i,j) represents the grayscale value of the pixel in the window.

[0028] Next, the threshold T(x,y) of the pixel is determined by calculating the average value in the local area and subtracting a constant C:

[0029] T(x,y)=μ local (x,y)-C

[0030] In this way, for each pixel in the image, its local threshold T(x,y) is calculated based on the pixel value in the neighborhood, rather than a global fixed threshold. This mechanism enables the segmentation algorithm to adapt to changing contrast conditions, ensuring that the vascular area can still be accurately identified in the case of complex backgrounds or uneven lighting. After segmentation, the binary image B(x,y) is defined as follows:

[0031]

[0032] Morphological closing operation:

[0033] After obtaining the preliminary binary image, the morphological closing operation is further used to optimize the image. The closing operation is composed of the image expansion operation D(B) and the erosion operation E(D(B)), and its mathematical definition is as follows:

[0034] Closing operation (B) = E (D (B))

[0035] Among them, D(B) represents the dilation operation of the binary image, which expands the edge of the blood vessel outward to fill the small holes in the segmentation image; and E(D(B)) represents the corrosion operation after the dilation, which shrinks the expanded part again and retains the shape and continuity of the blood vessel. This step ensures the integrity of the blood vessel area, especially for broken or small blood vessel segments, which can be restored to a more coherent shape through closing operations to reduce the interference of background noise.

[0036] Centerline extraction:

[0037] After the blood vessel segmentation, the present invention adopts a skeleton extraction technology to ensure that the center line of the blood vessel structure can be extracted from the binary blood vessel image. The technology gradually simplifies the pixels of the blood vessel area and retains its topological structure. The final center line has a single pixel width and can completely retain the connectivity and morphological characteristics of the blood vessel. This process ensures that the path of the main blood vessel in the complex blood vessel network will not be destroyed, laying the foundation for subsequent analysis.

[0038] Centerline pruning:

[0039] After the centerline of the blood vessel is initially extracted, it is necessary to further perform centerline pruning to remove redundant noise fragments and ensure the connectivity and accuracy of the centerline. The pruning process specifically includes the following steps:

[0040] (1) Determination of the starting point and the ending point: In order to ensure the alignment of the center line and the blood vessel segmentation mark point, the present invention adopts a method based on the Euclidean distance (L2 norm) to determine the starting point and the ending point of the center line. The distance between the blood vessel segmentation mark point (i.e., the first and last mark point) and each point in the center line is calculated, and the center line point with the smallest distance is selected as the starting point and the ending point. The calculation formula of the L2 distance d(x, y) is:

[0041]

[0042] Among them, (x1, y1) is the coordinate of the marker point, and (x2, y2) is the coordinate of the center line point. Through this distance calculation, the center line node closest to the marker point can be accurately found.

[0043] (2) Depth-first search (DFS) path tracking: After determining the starting point and the end point, the depth-first search (DFS) algorithm is used to track the path on the center line. DFS starts from the starting point, recursively traverses all connected nodes on the center line, and searches along the blood vessel path until the end point is found. DFS ensures that the selected path is a connected path from the starting point to the end point.

[0044] (3) Path verification and pruning: Once DFS finds a path to the end point, the system will retain the path and prune the invalid centerline segments that are not included in the main path. This step can effectively remove excess noise and unnecessary branches, ensuring that the final centerline is both streamlined and consistent with the actual vascular structure.

[0045] In this way, the present invention can accurately extract a complete and coherent blood vessel centerline, providing a reliable basis for subsequent blood vessel diameter measurement and stenosis analysis.

[0046] Step 4, blood vessel diameter measurement. After the centerline is extracted, the system interpolates the blood vessel contour and fits the blood vessel edge with a B-spline curve to ensure accurate reconstruction of the blood vessel morphology. In order to further improve the accuracy of diameter calculation, especially in areas of blood vessel curvature or stenosis, the present invention proposes and uses an improved MoM (Measure of Match) model.

[0047] (1) Determination of blood vessel direction: In blood vessel contour fitting, the system first determines the direction of the blood vessel through curve fitting and Hessian matrix optimization. Assuming point P is the current center point, the slope θ of the local fitting line segment at point P is weighted and combined. p1The eigenvector θ corresponding to the smaller eigenvalue of the Hessian matrix p2 As the blood vessel direction θ p , defined as follows:

[0048] θ p =αθ p1 +(1-α)θ p2

[0049] (2) Calculation of blood vessel diameter:

[0050] The system calculates the vessel diameter based on the distance d between the left and right boundary points P1 and P2 of the vessel. The improved MoM model introduces the optimization of the matching degree of bilateral boundary points on this basis, making the boundary features on the left and right sides clearer. The diameter calculation formula is:

[0051] Blood vessel diameter = d l +d r

[0052] Among them, d l and d r They represent the distance from the center point P to the left and right boundary points P1 and P2 respectively, and the diameter of the blood vessel is obtained by summing them up.

[0053] (3) Edge matching measurement:

[0054] The improved MoM algorithm maximizes the edge matching by matching the actual edge of the blood vessel with the fitting curve. To improve the accuracy of the calculation, the algorithm calculates the matching degree on the left and right sides separately:

[0055]

[0056] Among them, m is the maximum search distance, f1(t+i) and f2(t+i) are the fitted left and right curves, g1(t+i) and g2(t+i) are the left and right vascular edge points. By calculating the deviation between the fitted curve and the actual edge, the improved MoM algorithm can maximize the matching degree of the left and right vascular edges and further optimize the accuracy of the vascular diameter.

[0057] (4) Determination of blood vessel diameter:

[0058] Finally, the system determines the optimal boundary point positions P1 and P2 on the left and right sides by maximizing the MoM values ​​(matching degree) on the left and right sides:

[0059]

[0060] The accurate blood vessel diameter is calculated accordingly. The method proposed by the present invention can effectively improve the accuracy of diameter measurement, especially when processing curved or narrow blood vessel segments.

[0061] Step 5: Detection and measurement of stenosis area. By comparing the blood vessel diameters of the stenosis area and the normal area, the system automatically identifies the stenosis area and calculates the stenosis rate based on the minimum diameter of the stenosis area and the reference diameter. The specific calculation formula is as follows:

[0062]

[0063] Among them, %D is the diameter stenosis rate, %A is the area stenosis rate, and D Lesion (Unit: mm) is the minimum diameter of the area with vascular stenosis rate ≥5%, D Ref (Unit: mm) is the average diameter of the healthy blood vessel segment adjacent to the stenotic area. When the stenosis rate exceeds the preset threshold of 5%, the system will automatically mark the area for further review or subsequent processing by the user.

[0064] Step 6, automatic stenosis correction. After stenosis detection, the system automatically corrects the stenosis area. By referring to the centerline of the blood vessel and the reference diameter, the system uses the B-spline curve to generate a reference contour of the normal blood vessel, and automatically corrects the abnormal diameter of the stenosis area. The automatic correction function of the present invention not only improves the accuracy of stenosis detection, but also reduces the workload of manual adjustment. The corrected blood vessel contour is smooth and conforms to the natural morphology of the blood vessel.

[0065] Compared with the prior art, the advantages of the present invention are as follows: (1) High precision and automation. Through catheter calibration, improved MoM model and B-spline curve interpolation, the present invention can provide extremely high stenosis detection and diameter measurement accuracy, and realize fully automatic stenosis area correction, greatly reducing the subjective error of manual operation. (2) Strong adaptability. The adaptive threshold segmentation algorithm of the present invention can adapt to DSA images with different contrast and image quality, especially in images with low contrast and complex background, it still maintains a high segmentation accuracy. (3) Automatic correction function. The automatic stenosis correction function provided by the present invention can not only effectively process the detected stenosis area, but also ensure the naturalness and consistency of the vascular morphology through automatic correction based on the reference contour of the normal blood vessel, reducing subsequent manual adjustments. (4) User-friendliness. The system has a friendly user interface, supports manual adjustment and dynamic display of results, can meet the personalized processing needs of clinicians for different cases, and improves the convenience of clinical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The system interface, function introduction and flow chart of the present invention;

[0067] Figure 2 Schematic diagram for reading the original image;

[0068] Figure 3 This is a schematic diagram of catheter calibration;

[0069] Figure 4 To determine the marking points of the narrow area, calculate the segmentation map and the centerline diagram;

[0070] Figure 5 Diameter measurement schematic diagram;

[0071] Figure 6 This is a schematic diagram of stenosis measurement;

[0072] Figure 7 Schematic diagram of automatic stenosis correction. DETAILED DESCRIPTION

[0073] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0074] Example: Figure 1 As shown, the present invention provides a DSA coronary artery stenosis analysis method based on matching measurement, and the final test result is as follows Figure 6 As shown, the specific steps are as follows:

[0075] Step 1: Read the original image. First, read the multi-frame DSA image sequence in DICOM format, and adjust the window width and window position by sliding the right wheel of the system so that the contrast and brightness of the blood vessels meet the analysis requirements. The adjusted image will be used for further blood vessel segmentation and feature extraction. Figure 2 shown.

[0076] Step 2, catheter calibration. Mark the start and end points of the catheter in the image, calculate the ratio of the actual diameter of the catheter to the pixel value in the image, generate a calibration factor for subsequent vascular diameter measurement, and convert pixel units to physical units. The calculation formula for the catheter calibration factor is:

[0077]

[0078] Among them, D actual is the actual diameter of the catheter, and D pixel is the diameter of the catheter in the image. Figure 3 shown.

[0079] Step 3, determine the marker points of the stenotic area, calculate the segmentation map and center line. Through image analysis technology, combined with the morphological characteristics of the blood vessel contour, the position of the marker points in the stenotic area is automatically calculated to accurately cover the stenotic area. The vascular area is binarized using the adaptive threshold segmentation method, and the local threshold is dynamically adjusted to ensure accurate segmentation of blood vessels under complex backgrounds. Then, small holes and noise are removed through morphological closing operations, while retaining the continuity of the blood vessels. Subsequently, the skeleton extraction technology is used to generate the vascular centerline, and the depth-first search (DFS) algorithm is used for pruning to ensure the accuracy and continuity of the centerline. The effect is as follows: Figure 4 shown.

[0080] Step 4: Blood vessel diameter measurement. After completing step 3, the blood vessel contour is calculated using the improved MoM (Measure of Match) model. By combining the B-spline curve interpolation technology, the system can accurately measure the blood vessel diameter at different locations, especially when dealing with curved and narrow areas of the blood vessels. By optimizing the left and right boundary point matching measurement, the blood vessel diameter calculation is more accurate. The effect is as follows Figure 5 shown.

[0081] Step 5: Stenosis area measurement. Based on the blood vessel diameter obtained in step 4, the stenosis rate is automatically calculated by comparing the diameter of the stenosis area with that of the healthy blood vessel area. The stenosis rate calculation formula is:

[0082]

[0083] Among them, %D is the diameter stenosis rate, %A is the area stenosis rate, and D Lesion (Unit: mm) is the minimum diameter of the area with vascular stenosis rate ≥5%, D Ref (Unit: mm) is the average diameter of the healthy blood vessel segment adjacent to the stenotic area. When the stenosis rate exceeds 5%, the system automatically marks the area. Figure 6 shown.

[0084] Step 6: Automatic stenosis correction. Based on the stenosis measurement results of step 5, the system automatically refers to the diameter of the healthy blood vessel, uses the B-spline curve to generate the reference contour of the stenosis area, and corrects the diameter of the abnormal stenosis area. This correction method ensures that the stenosis area returns to the normal vascular morphology, reduces the operation of manual adjustment, and improves the overall automation. The effect is as follows Figure 7 shown.

[0085] Effect evaluation:

[0086] The present invention discloses a DSA coronary artery stenosis analysis method based on matching degree measurement. The test results of the method are as follows Figure 6As shown. As can be seen from the attached figure of the specification, the present invention is integrated into a software system, and the test process is: click the "Function" button in the upper left corner of the system, find the "Read Original Image" button to import the DSA image into the system, determine the catheter marking point and click the "Catheter Calibration" button to obtain the calibration factor, and then click the "Calculate Segmentation Map" button according to the stenosis area marking point to obtain the vascular segmentation map and center line, then click the "Calculate Diameter" button to calculate the vascular segment diameter, then click the "Stenosis Measurement" button to calculate the vascular stenosis rate, and finally click the "Automatic Stenosis Correction" button to restore the healthy vascular segment reference. The test results show that this method can quickly and accurately detect the coronary artery stenosis area, providing doctors with an efficient and reliable analysis tool in the diagnosis and treatment of coronary artery disease.

[0087] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any person familiar with the technology can understand and think of any changes or substitutions within the technical scope disclosed by the present invention, which should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A DSA coronary artery stenosis analysis method based on matching degree measurement, characterized in that: The method comprises the following steps: a) Image reading and preprocessing: read DSA images, adjust image window width and window position to optimize image display quality; b) Catheter calibration: Select the catheter marker points in the image in the software and calculate the catheter calibration factor for subsequent accurate measurement of vessel diameter and stenosis. The catheter calibration factor is the ratio of the actual catheter width (mm) to the measured pixel value (pixel); c) Blood vessel segmentation: Binarization of the blood vessel region is performed based on the adaptive threshold segmentation method to generate a segmentation map of the blood vessel region; d) Centerline extraction: extract the vascular skeleton in the segmentation image, and remove noise and non-connected areas through pruning operations to ensure the continuity of the centerline; e) Vascular diameter measurement: The vascular contour is calculated using the improved MoM model, and the vascular diameters at different locations are accurately measured through B-spline curve interpolation; f) Stenosis area measurement: By comparing the diameter of the stenosis area with the normal blood vessel area, the stenosis rate of the blood vessel is calculated. The calculation method of the stenosis rate includes: Among them, %D is the diameter stenosis rate, %A is the area stenosis rate, and D Lesion (Unit: mm) is the minimum diameter of the area with vascular stenosis rate ≥5%, D Ref (Unit: mm) is D Lesion Normal blood vessel diameter reference value, g) Automatic stenosis correction: Based on the reference diameter and the vessel centerline, the reference contour of the normal vessel is automatically generated, and the diameter of the stenosis area is corrected through B-spline curve interpolation.

2. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 1, characterized in that: ； Step 1: DSA image reading and preprocessing. First, the system reads DSA images from the image database and supports multiple formats, especially the processing of DICOM sequence images. In order to ensure the image processing effect, the system provides the function of adjusting the window and window level to make the image contrast and brightness meet the needs of the application user. (1) Calculation of window width and window position: The window width (Width) is defined as the difference between the maximum and minimum CT values ​​in the image and is used to determine the brightness range of the image. The calculation formula is: Width=CT max -CT min The window level is defined as the average of the maximum and minimum CT values ​​and is used to determine the median brightness of the image. The calculation formula is: (2) Image standardization: After calculating the window width and window position, the system will standardize the image. The image brightness will be adjusted according to the calculated window width and window position, and the CT value of the image will be mapped to the range of [0, 255] to facilitate subsequent processing and analysis.

3. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 1, characterized in that: The catheter calibration steps include: a) selecting the marker points of the catheter area and binarizing the catheter area by adaptive threshold segmentation; b) extracting the catheter centerline and calculating the catheter calibration factor; c) The catheter calibration factor is calculated as the ratio of the actual catheter width (mm) to the measured pixel value (pixels).

4. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 1, characterized in that: The adaptive threshold segmentation method in the blood vessel segmentation step dynamically adjusts the threshold according to the local contrast of the image to ensure the accuracy of blood vessel segmentation, especially in low-contrast areas; Adaptive Thresholding: For each pixel, its neighborhood is defined as a window of size N×N, which contains the pixel information of the pixel and its neighborhood. The average value of the pixel value in the area is μ local Calculated by the following formula: Among them, I(i,j) represents the gray value of the pixel in the window, Next, the threshold T(x,y) of the pixel is determined by calculating the average value in the local area and subtracting a constant C: T(x,y)=μ local (x,y)-C In this way, for each pixel in the image, its local threshold T(x,y) is calculated based on the pixel value in the neighborhood, rather than a global fixed threshold. This mechanism enables the segmentation algorithm to adapt to changing contrast conditions and ensure that the vascular area can still be accurately identified in the case of complex background or uneven lighting. After segmentation, the binary image B(x,y) is defined as follows:

5. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 1, characterized in that: The centerline extraction step comprises: a) Using the breadth-first search algorithm, starting from the marked point, traverse the connectivity of the vascular region; b) Remove noise and disconnected small fragments according to the skeleton pruning algorithm to ensure the continuity of the center line; The pruning process specifically includes the following steps: (1) Determination of the starting and ending points: The starting and ending points of the centerline are determined by using a method based on the Euclidean distance (L2 norm). The distances between the vascular segmentation markers (i.e., the first and last markers) and each point on the centerline are calculated. The centerline points with the smallest distance are selected as the starting and ending points. The calculation formula for the L2 distance is: Among them, (x1, y1) is the coordinate of the marked point, and (x2, y2) is the coordinate of the center line point. Through this distance calculation, the center line node closest to the marked point can be accurately found. (2) Depth-first search (DFS) path tracking: After determining the starting point and the end point, the depth-first search (DFS) algorithm is used to track the path on the center line. DFS starts from the starting point, recursively traverses all connected nodes on the center line, and searches along the blood vessel path until the end point is found. DFS ensures that the selected path is a connected path from the starting point to the end point. (3) Path verification and pruning: Once DFS finds a path to the end point, the system retains the path and prunes the invalid centerline segments that are not included in the main path. This step can effectively remove excess noise and unnecessary branches, ensuring that the final centerline is both concise and consistent with the actual vascular structure.

6. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 1, characterized in that: The details of blood vessel diameter measurement are as follows: After the centerline is extracted, the system interpolates the blood vessel contour and fits the blood vessel edge with the B-spline curve to ensure accurate reconstruction of the blood vessel morphology. In order to further improve the accuracy of diameter calculation, especially in areas with curved or narrow blood vessels, an improved MoM (Measure of Match) model is used. (1) Determination of blood vessel direction: In blood vessel contour fitting, the system first determines the direction of the blood vessel through curve fitting and Hessian matrix optimization. Assuming point P is the current center point, the slope θ of the local fitting line segment at point P is weighted. p1 The eigenvector θ corresponding to the smaller eigenvalue of the Hessian matrix p2 As the blood vessel direction θ p , defined as follows: i p =θ p1 +(1-α)θ p2 (2) Calculation of blood vessel diameter: The system calculates the vessel diameter based on the distance d between the left and right boundary points P1 and P2 of the vessel. On this basis, the improved MoM model introduces the optimization of the matching degree of bilateral boundary points, making the boundary features on the left and right sides clearer. The diameter calculation formula is: Blood vessel diameter = d l +d r Among them, d l and d r They represent the distance from the center point P to the left and right boundary points P1 and P2 respectively. The diameter of the blood vessel is obtained by summing them up. (3) Edge matching measurement: The improved MoM algorithm maximizes the edge matching by matching the actual edge of the blood vessel with the fitting curve. To improve the accuracy of the calculation, the algorithm calculates the matching degree on the left and right sides respectively: Where m is the maximum search distance, f1(t+i) and f2(t+i) are the fitted left and right curves, g1(t+i) and g2(t+i) are the left and right vascular edge points, respectively. By calculating the deviation between the fitted curve and the actual edge, the improved MoM algorithm can maximize the matching degree of the left and right vascular edges and further optimize the accuracy of the vascular diameter. (4) Determination of blood vessel diameter: Finally, the system determines the optimal boundary point positions P1 and P2 on the left and right sides by maximizing the MoM values ​​(matching degree) on the left and right sides: The precise blood vessel diameter can be calculated from this.

7. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 6, characterized in that: In the stenosis rate calculation step, the reference diameter is calculated based on the average value of the diameters of adjacent healthy blood vessel regions before and after the stenosis region.

8. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 7, characterized in that: In the automatic stenosis correction step, B-spline curve interpolation is performed on the normal blood vessel reference contour in the stenosis area to smooth the corrected blood vessel contour.

9. The DSA coronary artery stenosis analysis method based on matching degree measurement according to claim 8, characterized in that: The method also includes a user interface function that supports manual adjustment of the stenosis area and dynamic display of the stenosis degree.

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