Carotid artery automatic positioning and segmentation method based on adaptive feature optimization

Abstract

The invention discloses a carotid artery automatic positioning and segmentation method based on adaptive feature optimization, and the method comprises the steps: firstly, designing a standard matching template according to the shape gray features of a lumen in an ultrasonic carotid artery cross section image, and carrying out the adjustment according to a possible scale, and generating a multi-scale template group; secondly, matching a multi-scale template group with the image to obtain a possible position of the carotid artery, further selecting a most probable region by combining self-adaptive ring gray information and relative position information, and generating an initial contour of a carotid artery lumen according to the most probable region; and finally, the initial contour is evolved through a DRLSE algorithm with an optimized external energy term, so that contour segmentation with high accuracy and high robustness can be realized. According to the method, robust position detection is realized by designing a reasonable multi-scale matching template group, and segmentation precision and robustness are improved by optimizing an energy item for driving evolution. According to the invention, automatic positioning and segmentation of the carotid artery can be accurately and effectively realized, and guidance is provided for processing and analysis of medical images.

Description

Technical Field

[0001] The invention belongs to the field of medical image processing, and relates to an ultrasonic carotid artery cross-section lumen recognition method, and specifically to an adaptive carotid artery automatic positioning and segmentation algorithm based on the improvement of the grayscale characteristics of the carotid artery lumen shape. Background Art

[0002] As one of the main blood vessels in the human body, the health of the carotid artery is closely related to the occurrence of cardiovascular and cerebrovascular diseases. Vascular ultrasound examination can detect early signs of the disease, which is crucial for the prevention and treatment of cardiovascular and cerebrovascular diseases. The structure of the carotid lumen directly affects blood supply and blood pressure, and is a key indicator for assessing the health of the cardiovascular and cerebrovascular system. Therefore, accurate segmentation of the carotid lumen helps to identify and analyze the possibility and cause of the disease, which is of great significance for improving the accuracy and efficiency of disease diagnosis.

[0003] Traditional carotid artery diagnosis relies on ultrasound scanning and subjective analysis by doctors, which is time-consuming and easily affected by personal experience and skills. With the development of computer-aided diagnosis technology, automated segmentation methods have emerged, including threshold-based, region-based, and model-based methods. However, these methods often require users to provide prior information, such as seed points. For this reason, some automatic auxiliary diagnosis systems propose to find the target area first and then further implement segmentation. Template matching is a commonly used target detection algorithm, but its performance is highly dependent on the design and selection of the template. If the template design is unreasonable or the scale is inappropriate, it may lead to poor matching results.

[0004] In recent years, image segmentation technology has made significant progress. The distance regularized level set evolution method (DRLSE) has performed well in target contour extraction. This method introduces a distance regularization term to correct the deviation between the level set function and the signed distance function, avoiding the need for periodic reinitialization. However, in the field of medical image processing, the DRLSE method still faces challenges, such as complex backgrounds and multiple noises. In addition, the existing DRLSE method is still sensitive to the selection of initial contours, and its performance needs to be improved when processing images of weak boundary target objects. Summary of the invention

[0005] In order to overcome the problems of insufficient autonomy and accuracy in existing carotid artery segmentation systems, the present invention provides a method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization. This method achieves robust position detection by designing a reasonable multi-scale matching template group, and improves segmentation accuracy and robustness by optimizing the energy term that drives evolution. This method uses image features to automatically adjust key parameters during the detection process, can process carotid artery images of different sizes and shapes, and provide high-precision segmentation results, effectively overcoming the limitations of existing technologies and providing a new solution for computer-assisted ultrasound diagnosis.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization comprises the following steps:

[0008] Step 1: By analyzing the grayscale features of the lumen shape in the ultrasonic carotid artery cross-section image, a circular ring is designed as a standard matching template for the carotid artery, and the possible size of the carotid artery lumen is calculated by the image features of the statistical data set, and the carotid artery standard template is scaled to obtain a multi-scale template group;

[0009] Step 2: for the carotid artery ultrasound image to be detected, traverse the templates of each scale in the multi-scale template group, record the area with the highest similarity for each scale template as the best matching area at that scale, generate a simulated circle and adjust its size adaptively, further analyze the pixel grayscale difference information and relative position information of the simulated circle to select the most likely lumen area, and generate a simulated carotid artery lumen contour;

[0010] Step 3. The external energy term of the DRLSE algorithm is optimized in combination with the shape and grayscale features of the carotid artery lumen. Two new energy terms, shape constraint term and shape region energy term, are added to constrain the shape and grayscale distribution. An improved level set function evolution equation is established. The level set function evolution equation drives the contour evolution, and a two-step evolution stage is designed to quickly approach the target contour and then perform fine segmentation to better meet the different characteristics of the contour during the evolution process, ultimately achieving contour segmentation with high accuracy and high robustness.

[0011] Compared with the prior art, the present invention has the following advantages:

[0012] (1) Traditional template matching methods have a strong dependence on the template itself, and the effect is poor when the target object is deformed, the size changes, or the image conditions are not ideal. The present invention provides a carotid artery segmentation method based on the grayscale feature design of the carotid artery shape, with an adaptive multi-scale template matching positioning method and an optimized level set algorithm. This method independently designs a template by deeply analyzing the grayscale features of the carotid artery shape, and combines multi-scale transformation with an adaptive adjustment mechanism, so that the algorithm has good robustness and enhances its adaptability in changing scenarios. In addition, by further analyzing the regional grayscale features and relative position information, the complete dependence on the template is effectively reduced, thereby maintaining a stable segmentation effect in a variety of situations.

[0013] (2) Compared with the traditional DRLSE algorithm, the present invention adopts an initial contour that is close to the position and shape of the carotid artery as the starting point, which reduces the number of iterations required for the evolution process, thereby reducing the consumption of computing resources and time. The external energy function is optimized in combination with the shape grayscale features unique to the carotid artery lumen ultrasound image, and two new energy terms are introduced to consider and restrict the shape and regional grayscale features respectively, so that the accuracy and reliability of the lumen contour segmentation can be maintained even under conditions of poor image quality such as contour defects and blurred edges. The analysis and introduction of the unique characteristics of the carotid artery make this algorithm show stronger applicability and anti-interference ability in the carotid artery segmentation task than general algorithms.

[0014] (3) The present invention adopts a two-step structure of first positioning and then segmentation to achieve automatic segmentation of the carotid artery, reduce dependence on doctor's guidance, and thus improve the working efficiency of the system. Based on the first step of positioning, the second step of segmentation uses the positioning result as the initial reference, which enhances the overall scalability and optimizability of the system. This structured method provides an efficient and automated solution for carotid artery segmentation, which has important clinical application value.

[0015] (4) The method proposed in the present invention can accurately and effectively realize the automatic positioning and segmentation of the carotid artery, and provide guidance for the processing and analysis of medical images. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of the method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization;

[0017] Figure 2 It is the standard matching template image of the carotid artery;

[0018] Figure 3 Examples of simulated circles obtained at different scales;

[0019] Figure 4 This is an example of the initial outline of the carotid artery lumen;

[0020] Figure 5 An example of the final contour obtained for carotid artery lumen segmentation. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0022] The present invention discloses a method for automatic positioning and segmentation of the carotid artery based on adaptive feature optimization. First, a standard matching template is designed according to the grayscale characteristics of the lumen shape in the ultrasonic carotid artery cross-section image, and a multi-scale template group is generated according to possible scale adjustments; secondly, the multi-scale template group is matched with the image to obtain the possible position of the carotid artery, and the most likely area is further selected by combining the adaptive grayscale information of the circular ring and the relative position information, and the initial contour of the carotid artery lumen is generated accordingly; finally, the initial contour is evolved by the DRLSE algorithm that optimizes the external energy term to achieve contour segmentation with high accuracy and high robustness. Figure 1 As shown, it is divided into three steps, the specific steps are as follows:

[0023] Step 1: By analyzing the grayscale features of the lumen shape in the ultrasonic carotid cross-section image, a circular ring of a certain size is designed as a standard matching template, and the possible size of the carotid lumen is calculated by the image features of the statistical data set, and the standard template is scaled to obtain a multi-scale template group. The specific steps are as follows:

[0024] Step 1-1, design the carotid artery standard matching template:

[0025] Step 1-1-1: Based on the characteristics that the carotid artery lumen is approximately circular, with low grayscale values ​​inside, high grayscale values ​​in the vascular membrane, and low grayscale values ​​in the surrounding tissue, a circular ring is designed as a template, in which the inner circle simulates the lumen, the annular area simulates the vascular membrane, and the outer area of ​​the ring simulates the surrounding tissue.

[0026] Step 1-1-2: Use F carotid artery ultrasound images as template references, and take the ratio of the distance from the center point of the lumen to the inner membrane and the outer membrane respectively, which is approximately used as the ratio of the inner and outer circle radii of the standard matching template.

[0027] Step 1-1-3: For the f-th template reference image, manually select the region of interest (ROI) that just contains the carotid adventitia area, and use an approximate ring to simulate the carotid artery lumen. The outer circle of the simulated ring is denoted as C ot,f , where ot represents the outer circle, then the outer circle diameter D ot,f Defined as:

[0028]

[0029] Among them, W x,f is the pixel size in the x direction of the region, W y,f is the pixel size of the region in the y direction.

[0030] Step 1-1-4: Take the center point of the lumen of the fth image as the seed point, perform binarization on the image by using the region growing method, extract the edge of the carotid artery lumen, and take the maximum and minimum values ​​of the edge in the y direction as y max,f ,y min,f , the maximum and minimum values ​​in the x direction are x max,f ,x min,f Based on this, the vertical diameter and horizontal diameter of the lumen are defined as the difference between the maximum and minimum longitudinal and transverse values ​​of the edge, respectively. The inner circle of the simulated ring of the fth image is recorded as C in,f , where in represents the inner circle and the inner circle diameter D in,f It is defined as the average of the vertical diameter and the horizontal diameter and is calculated as:

[0031]

[0032] Step 1-1-5, count the ratios of the inner and outer diameters of the simulated annulus of all F ROI images, and take the average value β as the ratio of the inner and outer diameters of the simulated annulus of the standard matching template, calculated as follows:

[0033]

[0034] Step 1-1-6: The constructed carotid artery lumen standard matching template is based on this ratio to fully conform to the grayscale distribution characteristics of the carotid artery lumen and maintain robustness to possible interference in the ultrasound image. Take a square with a side length of ts as the initial template area, with the center coordinates (x c ,y c ), where c represents the center of the template and the outer circle of the template ring C to The radius is r to =ts / 2, where to represents the outer circle of the template ring; the inner circle C ti The radius is r ti =β·r to , where ti represents the inner circle of the template ring. Based on this design, the ring is simulated, the inner circle is low grayscale value, the annular area is high grayscale value, and the outer circle is low grayscale value. The standard matching template T is obtained, and the grayscale value Gr(x, y) of each pixel point (x, y) is specifically designed as follows:

[0035]

[0036] Step 1-2: Construct a multi-scale matching template group:

[0037] Step 1-2-1: Count the maximum and minimum values ​​of the number of pixels occupied by the carotid artery lumen diameter in the existing carotid artery image, and use them as the possible maximum value S of the template size. max , minimum value S min , set the scale factor at equal intervals.

[0038] Step 1-2-2: When there are SN ratios in the multi-scale ratio group, the sn-th template scale ratio coefficient si is:

[0039]

[0040] In this way, we get the multi-scale ratio group S SN =[si 1 ,si 2 ,…,si SN ], and the standard matching template T is grouped according to the multi-scale ratio S SN Each scale factor in is scaled SN times to obtain a new multi-scale template group T SN , so that the matching algorithm can maintain high matching accuracy for lumens of different sizes.

[0041] Step 2: For the carotid ultrasound image to be detected, traverse the templates of each scale in the multi-scale template group, record the area with the highest similarity for each scale template as the best matching area at that scale, generate a simulated circle and adjust its size adaptively, further analyze the pixel grayscale difference information and relative position information of the simulated circle to select the most likely lumen area, and generate a simulated carotid lumen contour. The specific steps are as follows:

[0042] Step 2-1, multi-scale template matching:

[0043] Step 2-1-1: For the carotid artery ultrasound image I to be detected, traverse all possible positions (pi, pj) in the image and calculate the position corresponding to the multi-scale template group T SN Templates of various scales in T sn The degree of matching uses the normalized cross-correlation (NCC) as the evaluation score RS(pi,pj) of the position (pi,pj):

[0044]

[0045] Among them, T sn For sn The size of the template after scaling under the scale factor is M×M. is the average value of the pixels of the template at this scale, defined as I(x+pi,y+pj) is the area of ​​the ultrasound image where the similarity is to be calculated; is the average value of the matching area in the image, defined as

[0046] Step 2-1-2, scale factor si sn The template is selected, and the area that is most similar to the template is the area with the highest score RG sn , record the corresponding region center coordinates as (x sn ,y sn ), where x sn =pi+ts·si sn / 2,y sn =pj+ts·si sn / 2.

[0047] Step 2-2: Adaptively generate a simulated circle:

[0048] Step 2-2-1, for the obtained si sn The best matching area under the scale coefficient is approximated by a circle with the most likely existence form of the carotid artery lumen in the area, and the radius of the approximate circle is r sn ts·si sn / 2. The radius is adjusted autonomously to obtain the radius of the simulated circle r after adjustment adj,sn , where adj means after adjustment to ensure that the circular area does not exceed the image boundary, as follows:

[0049] r adj,sn =min(r sn ,x sn ,y sn ,ts·si sn -y sn ,ts·si sn -x sn )

[0050] Step 2-2-2, take the center coordinate (x sn ,y sn ) is the center of the circle, and the radius of the simulated circle after adjustment is r adj,sn Draw a circle with radius C sn , simulating the carotid artery, the area represented by the circle is

[0051] Step 2-3, calculate the relative position coefficient:

[0052] Step 2-3-1: For templates of different scales T sn The simulated circle C sn There is overlap between them, which means that this area is more likely to be similar to the lumen, so the overlap between each simulated circular area and other areas is judged.sn1 and the scale factor si sn2 The two circles C sn1 and C sn2 , compare its center (x sn1 ,y sn1 ) and (x sn2 ,y sn2 ) distance and the radius of the two circles and r sn1 +r sn2 When the distance between the centers is less than the sum of the radii, the two circles overlap. sn1 ,C sn2 Overlap coefficient γ sn1,sn2 Calculated as:

[0053]

[0054] Step 2-3-2, set the position weight term coefficient to pnt, then the scale coefficient si sn The relative coefficient w of the position of the most likely area obtained by the corresponding template sn The calculation is as follows:

[0055]

[0056] Among them, γ ks,sn For two circles C ks ,C sn The overlap coefficient between .

[0057] Step 2-4: Calculate the grayscale value difference between the inner and outer sides of the simulated ring:

[0058] Step 2-4-1: Further evaluate the grayscale characteristics of the region in the form of a simulated ring. sn The outer radius of the optimal simulated ring under the scale coefficient template is r calculated in step 2-2. adj,sn The radius of the inner ring is β times the radius of the outer ring. The area inside the inner circle of the simulated ring is Q ir,sn , where ir represents the inner circle; the area between the rings is Q om,sn , where om represents the ring, calculate the average gray value difference between the two, and compare it with the position relative coefficient w obtained in step 2-3 sn The weighted value is used as the grayscale difference score of the shape area to calculate the final score diff of each area. sn , the specific calculation is:

[0059]

[0060] Where count represents the number of pixels in the area, and Gr(x,y) is the grayscale value of the pixel (x,y).

[0061] Step 2-4-2: Take the maximum value of the grayscale difference scores of the shape area obtained at all scales diff max The corresponding area is the most likely lumen area and serves as the target area for segmentation in the next stage.

[0062] Step 2-4-3, take the center point of the most likely lumen area (x b ,y b ) is the center of the circle, where b represents the most likely presence of the carotid artery in the image, and the corresponding adjusted inner radius of the simulated ring r b Draw a circle with radius C b , then C b To simulate the contour of the carotid artery lumen.

[0063] Step 3: Optimize the external energy term of the DRLSE algorithm by combining the shape and grayscale features of the carotid artery lumen, add two new energy terms, shape constraint term and shape region energy term, to constrain the shape and grayscale distribution, and establish an improved level set function evolution equation. The level set function evolution equation drives the contour evolution, and designs a two-step evolution stage of first quickly approaching the target contour and then fine segmentation to better meet the different characteristics of the contour during the evolution process, and finally achieve contour segmentation with high accuracy and high robustness. The specific steps are as follows:

[0064] Step 3-1, establish the level set function algorithm equation:

[0065] Step 3-1-1: The simulated carotid artery lumen contour C obtained in step 2 b As the initial contour, and as the initial value of the level set function φ that is evolved in this step. The level set function is defined as:

[0066] φ(x,y,t)=0

[0067] Among them, x and y represent the coordinates of the point in the two-dimensional space, and t represents the number of iterations.

[0068] Step 3-1-2: Drive the evolution of the level set function through the energy function E(φ), which is specifically defined as:

[0069] E(φ)=μR(φ)+E ext (φ)

[0070] Among them, μ is the regularization term coefficient, R(φ) is the level set function regularization term, which is specifically defined as:

[0071]

[0072] Where Ω is the neighborhood, p is the double potential well function, which is determined by the variable parameter s, so that the zero level set neighborhood is regularized. The specific definition is as follows:

[0073]

[0074] E ext (φ) is the external energy term, ext represents the external driving energy part, which drives the level set function to evolve towards the position with minimum energy. It is specifically defined as:

[0075] E ext (φ)=λ Lt L g (φ)+λ Ar A g (φ)+λ sc ξ sc (φ)+λ sa ξ sa (φ)

[0076] Among them, λ Lt Minimize the energy coefficient for a fixed weighted length, where Lt represents the length term; L g (φ) is the weighted length minimization energy term, which calculates the weighted line integral along the zero level set of the level set function. When the zero level set is at the boundary of the image contour, the energy of the length term reaches the minimum value to ensure the smoothness of the contour curve. It is specifically defined as:

[0077]

[0078] Among them, g is the boundary detection function, which is specifically defined as:

[0079]

[0080] Among them, G σ is a Gaussian kernel function with a standard deviation of σ, and I is the ultrasonic image to be detected. The noise can be removed to a certain extent through the convolution of the two.

[0081] λ Ar is a fixed weighted area energy coefficient, Ar represents the area term coefficient, A g (φ) is the weighted area energy term, and the integral region is the region where the level set function is less than zero. It can accelerate the curve evolution progress and ensure that when the initial boundary is far away from the image contour boundary, the function can quickly approach the ideal boundary. It is defined as follows:

[0082] A g (φ)=∫ Ω gH(-φ)dx

[0083] Where H is the Heaviside function.

[0084] λ sc is the shape constraint coefficient, a variable parameter, and sc represents the shape constraint. ξ sc(φ) is the shape constraint term, which compares the contour φ during the calculation evolution process with the given prior shape φ pc The difference is weighted by the boundary detection function g, and the segmentation result is limited to be as close to a circle as possible to weaken the adverse effects of over-segmentation or under-segmentation, so that the segmented lumen shape is consistent with the actual anatomical structure, maintaining the smoothness and accuracy of the segmentation contour, where pc represents the prior shape. The shape constraint term is specifically defined as follows:

[0085] ξ sc (φ)=∫ Ω (gH(-φ)-gH(-φ pc )dx

[0086] λ sa is the shape area energy coefficient, is a variable parameter, and sa represents the shape area term. sa (φ) is the shape region energy term, which is calculated by calculating the shape φ of each evolution and its external extended shape φ et The difference between the lumen and the membrane is simulated, which further highlights the grayscale distribution characteristics of the carotid artery lumen, where et represents the external extended shape. The shape region energy term is defined as follows:

[0087] ξ sa (φ)=∫ Ω (gH-φ)-gH(-φ et ))dx

[0088] Among them, φ et is the extended shape corresponding to the carotid artery contour to simulate the peripheral contour of the vascular membrane, which is adaptively generated by the shape φ of each evolution: et =φ⊕K. K is the side length of (1-β) / 2·r φ The square structural element, r φ is the average inner diameter of the evolved shape.

[0089] Step 3-2: Contour evolution:

[0090] Step 3-2-1: In numerical calculation, use numerical approximation of Dirac function δ ε (x) and the numerical approximation of the Heaviside function H ε (x) respectively approximates the Dirac function δ and the Heaviside function H, where ε is a small fixed parameter, specifically:

[0091]

[0092] After numerical approximate calculation, the energy functional E is obtained. ε (φ) is approximately:

[0093]

[0094] The energy functional E is calculated and solved by Euler Lagrange equation and gradient descent flow method. ε The above energy functional can be minimized by solving the following gradient flow, where d p =p'(s) / s:

[0095]

[0096] Step 3-2-2, use the inverse finite difference method to perform evolution iteration. The evolution here is divided into two stages. The first stage quickly approaches the target contour area and iterates m times; the second stage seeks a fine segmentation boundary and iterates n times. The specific evolution process is:

[0097]

[0098] Among them, kt is the number of iteration steps, (i,j) is the coordinate of a point on the contour, and is the approximate estimate of the evolution equation in the first and second stages, respectively, and is specifically defined as:

[0099]

[0100] The first stage crosses possible noise and patches at a faster speed to reduce the possibility of falling into the local optimum. It is necessary to emphasize the shape constraint, λ sc The coefficient takes a larger λ sc1 , where sc1 represents the sc coefficient in the first stage of evolution; there is no need to consider the influence of shape grayscale features too much, λ sa The coefficient takes a smaller λ sa1 , where sa1 represents the sa coefficient of the first stage of evolution. The second stage emphasizes the influence of the shape region energy term to ensure that the segmentation result forms a clear grayscale difference between the lumen and the adventitia, and takes a larger λ sa2 , where sa2 represents the sa coefficient of the second stage of evolution; the shape feature is no longer important, and a smaller λ is taken sc2 , where sc2 represents the sc coefficient of the second stage of evolution.

[0101] Step 3-2-3: After two stages of evolution, the lumen contour φ is obtained f As the final segmentation result, f represents the final result.

[0102] Example:

[0103] This embodiment designs a multi-scale matching template group by analyzing the shape grayscale features of the carotid artery image, and then realizes lumen positioning based on a method combining template matching and adaptive feature analysis, and finally realizes accurate lumen segmentation by evolving the contour of the DRLSE algorithm improved based on the grayscale characteristics of the carotid artery shape. The specific steps are as follows:

[0104] First, a large number of two-dimensional ultrasound carotid artery cross-sectional images were collected using an ultrasound imaging system, and the carotid artery lumen contour was manually drawn as the gold standard for algorithm evaluation.

[0105] Execute step 1: Design a multi-scale template group for carotid artery matching. Select F = 100 images with good image quality and containing complete and clear-edged carotid artery lumens as the original data set, and manually crop the carotid artery ROI. Then calculate the ratio of the inner and outer diameters of the simulated ring β = 0.86, and establish a standard matching template simulated ring T with a side length of 100, an inner radius of 43, and an outer radius of 50 based on this ratio, as shown in Figure 2 As shown. The size of the lumen is statistically collected and the proportional coefficient is obtained as S SN =[0.4, 0.5, 0.6, 0.7], and then obtain the multi-scale template group T SN .

[0106] Execute step 2: realize automatic positioning detection of the carotid artery lumen. Perform the same operation on all 3263 images in the dataset: compare the image to be detected I with the multi-scale template group T SN Perform similarity calculations to obtain the most similar regions at four scales, and make adaptively adjusted simulated circles, as shown in the following figure: Figure 3 As shown. Then, for each most similar region, the simulated ring pixel difference and position coefficient are further calculated. The experimental setting of the coincidence position coefficient is pnt = 1.1. Finally, the best similar region is selected and the center point (x b ,y b ) at the adjusted radius of the simulated ring r b Draw a circle with radius as the initial outline of the carotid artery. The schematic diagram is as follows Figure 4 As shown. When the center position of the positioning result (x b ,y b ) is located inside the gold standard contour, the positioning is considered correct, and the result is that 3137 images are correctly positioned, with a success rate of 96.14%.

[0107] Execute step 3: Accurately segment the carotid artery lumen based on the improved DRLSE algorithm. Set the prior shape to be the center of the best similarity region, r b The circle is made of radius. Set ε = 1.5, and the length minimizes the energy coefficient λ Lt =5, area minimization energy coefficient λ Ar= -1.5, the contour extension coefficient is (1-β) / 2 = 0.07 times the average radius of the contour. The number of iterations in the first stage is m = 100 times, and the shape constraint coefficient λ sa1 =0.01, shape area energy coefficient λ sa1 =0.1; the number of iterations in the second stage is n = 10 times, and the shape constraint coefficient λ sa2 =0.002, shape area energy coefficient λ sa2 =1.2. Based on the evolution equation, the contour is iterated according to the evolution steps to obtain the final carotid artery lumen contour, as shown in the schematic diagram Figure 5 As shown. The Dice coefficient DSC is used as the segmentation accuracy evaluation index, which is defined as:

[0108]

[0109] Among them, Q a is the segmented area obtained by the algorithm proposed in this invention, Q gs is the gold standard segmentation region.

[0110] The average Dice coefficient of the carotid artery lumen contour segmentation result was 0.9259 after counting all the images. The Dice coefficient obtained by segmentation using the traditional DRLSE algorithm with the same parameters was 0.8712, which shows that the segmentation effect has been significantly improved.

[0111] Through the above embodiments, it can be found that the multi-scale template positioning algorithm integrating the adaptive features of the carotid artery proposed in the present invention effectively meets the actual needs of accurate positioning of the carotid artery and shows good robustness. Compared with the traditional standard template matching paradigm, it has higher accuracy. The initial contour is generated based on the positioning result, and the improved level set segmentation algorithm is further used for evolution and iteration. In this process, the algorithm fully exploits the inherent characteristics of the carotid artery such as the edge, shape and grayscale, and the obtained segmentation contour shows a very high similarity with the result of manual fine segmentation. This shows that the algorithm has a strong anti-interference ability, can effectively filter out noise and artifact interference, and then obtain a carotid artery contour close to the real state. Comprehensively considered, the algorithm system described in the present invention can achieve automated and high-precision segmentation of the carotid artery, provide a new solution for clinical diagnosis and treatment decision-making, and has a practical potential for clinical promotion and application.

Claims

1. A method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization, characterized in that The method comprises the following steps: Step 1: By analyzing the grayscale features of the lumen shape in the ultrasonic carotid artery cross-section image, a circular ring is designed as a standard matching template for the carotid artery, and the possible size of the carotid artery lumen is calculated by the image features of the statistical data set, and the carotid artery standard template is scaled to obtain a multi-scale template group; Step 2: for the carotid artery ultrasound image to be detected, traverse the templates of each scale in the multi-scale template group, record the area with the highest similarity for each scale template as the best matching area at that scale, generate a simulated circle and adjust its size adaptively, further analyze the pixel grayscale difference information and relative position information of the simulated circle to select the most likely lumen area, and generate a simulated carotid artery lumen contour; Step 3. The external energy term of the DRLSE algorithm is optimized in combination with the shape and grayscale features of the carotid artery lumen. Two new energy terms, shape constraint term and shape region energy term, are added to constrain the shape and grayscale distribution. An improved level set function evolution equation is established. The level set function evolution equation drives the contour evolution, and a two-step evolution stage is designed to quickly approach the target contour and then perform fine segmentation to better meet the different characteristics of the contour during the evolution process, ultimately achieving contour segmentation with high accuracy and high robustness.

2. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 1, characterized in that The specific steps of step 1 are as follows: Step 1-1, design the carotid artery standard matching template: Step 1-1-1, based on the characteristics that the lumen of the carotid artery is approximately circular, with low grayscale value inside, high grayscale value of vascular membrane, and low grayscale value of surrounding tissue, a circular ring is designed as a template, where the inner circle simulates the lumen, the annular area simulates the vascular membrane, and the outer area of ​​the ring simulates the surrounding tissue; Step 1-1-2, using F carotid artery ultrasound images as template reference, taking the ratio of the distance from the lumen center point to the intima and adventitia respectively as the ratio of the inner and outer circle radii of the standard matching template; Step 1-1-3: For the fth template reference image, manually select the region of interest that just contains the carotid adventitia area, and use an approximate ring to simulate the carotid artery lumen. The outer circle of the simulated ring is denoted as C ot,f , where ot represents the outer circle, then the outer circle diameter D ot,f Defined as: Among them, W x,f is the pixel size in the x direction of the region, W y,f is the pixel size of the region in the y direction; Step 1-1-4: Take the center point of the lumen of the fth image as the seed point, perform binarization on the image by using the region growing method, extract the edge of the carotid artery lumen, and take the maximum and minimum values ​​of the edge in the y direction as y max,f ,y min,f The maximum and minimum values ​​in the x direction are x max,f ,x min,f Based on this, the vertical diameter and horizontal diameter of the lumen are defined as the difference between the maximum and minimum longitudinal and transverse values ​​of the edge, respectively. The inner circle of the simulated ring of the fth image is recorded as C in,f , where in represents the inner circle, the inner circle diameter D in,f It is defined as the average of the vertical diameter and the horizontal diameter and is calculated as: Step 1-1-5, count the ratios of the inner and outer diameters of the simulated annulus of all F ROI images, and take the average value β as the ratio of the inner and outer diameters of the simulated annulus of the standard matching template, calculated as follows: Step 1-1-6: Take a square with a side length of ts as the initial template area, and the center coordinates are (x c ,y c ), where c represents the center of the template and the outer circle of the template ring C to The radius is r to =ts / 2, where to represents the outer circle of the template ring; the inner circle C ti The radius is r ti =β·r to , where ti represents the inner circle of the template ring; based on this design, the simulated ring has a low gray value inside the inner circle, a high gray value in the annular area, and a low gray value outside the outer circle, and the standard matching template T is obtained. The gray value Gr(x,y) of each pixel point (x,y) is specifically designed as follows: Step 1-2: Construct a multi-scale matching template group: Step 1-2-1: Count the maximum and minimum values ​​of the number of pixels occupied by the carotid artery lumen diameter in the existing carotid artery image, and use them as the possible maximum value S of the template size. max , minimum value S min , set the scale factor at equal intervals; Step 1-2-2: When there are SN ratios in the multi-scale ratio group, the sn-th template scale ratio coefficient si is: In this way, we get the multi-scale ratio group S SN =[si1,si2,…,si SN ], and the standard matching template T is grouped according to the multi-scale ratio S SN Each scale factor in is scaled SN times to obtain a new multi-scale template group T SN .

3. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 1, characterized in that The specific steps of step 2 are as follows: Step 2-1, multi-scale template matching: Step 2-1-1: For the carotid artery ultrasound image I to be detected, traverse all possible positions (pi, pj) in the image and calculate the position corresponding to the multi-scale template group T SN Templates of various scales in T sn The degree of matching uses the normalized mutual correlation coefficient as the evaluation score RS(pi,pj) of the position (pi,pj): Among them, T sn For sn The size of the template after scaling under the scale factor is M×M. is the average value of the pixels of the scale template; I(x+pi,y+pj) is the area of ​​the ultrasound image to be calculated for similarity; is the average value of the matching area in the image; Step 2-1-2, scale factor si sn The template is selected, and the area that is most similar to the template is the area with the highest score RG sn , record the corresponding region center coordinates as (x sn ,y sn ), where x sn =pi+ts·si sn / 2,y sn =pj+ts·si sn / 2; Step 2-2: Adaptively generate a simulated circle: Step 2-2-1, for the obtained si sn The best matching area under the scale coefficient is approximated by a circle with the most likely existence form of the carotid artery lumen in the area, and the radius of the approximate circle is r sn ts·si sn / 2, and adjust the radius autonomously to obtain the adjusted simulated circle radius r adj,sn , where adj means adjusted to ensure that the circular area does not exceed the image boundary, as follows: r adj,sn =min(r sn ,x sn ,and sn ,ts·si sn -and sn ,ts·si sn -x sn ) Step 2-2-2, take the center coordinate (x sn ,y sn ) is the center of the circle, and the radius of the simulated circle after adjustment is r adj,sn Draw a circle with radius C sn , simulating the carotid artery, the area represented by the circle is Step 2-3, calculate the relative position coefficient: Step 2-3-1, for the scale factor si sn1 and the scale factor si sn2 The two circles C sn1 and C sn2 , compare its center (x sn1 ,y sn1 ) and (x sn2 ,y sn2 ) distance and the radius of the two circles and r sn1 +r sn2 When the distance between the centers is less than the sum of the radii, the two circles overlap, and the two circles C sn1 ,C sn2 Overlap coefficient γ sn1,sn2 Calculated as: Step 2-3-2, set the position weight term coefficient to pnt, then the scale coefficient si sn The relative coefficient w of the position of the most likely area obtained by the corresponding template sn The calculation is as follows: Among them, γ ks,sn For two circles C ks ,C sn The overlap coefficient between Step 2-4: Calculate the grayscale value difference between the inner and outer sides of the simulated ring: Step 2-4-1, si sn The outer radius of the optimal simulated ring under the scale coefficient template is r calculated in step 2-2. adj,sn The radius of the inner ring is β times the radius of the outer ring. The area inside the inner circle of the simulated ring is Q ir,sn , where ir represents the inner circle; the area between the rings is Q om,sn , where om represents the ring, calculate the average gray value difference between the two, and compare it with the position relative coefficient w obtained in step 2-3 sn The weighted value is used as the grayscale difference score of the shape area to calculate the final score diff of each area. sn , the specific calculation is: Among them, count represents the number of pixels in the area, and Gr(x,y) is the gray value of the pixel (x,y); Step 2-4-2: Take the maximum value of the grayscale difference scores of the shape area obtained at all scales diff max The corresponding area is the most likely lumen area and serves as the target area for segmentation in the next stage; Step 2-4-3, take the center point of the most likely lumen area (x b ,y b ) is the center of the circle, where b represents the most likely presence of the carotid artery in the image, and the corresponding adjusted inner radius of the simulated ring r b Draw a circle with radius C b , then C b To simulate the contour of the carotid artery lumen.

4. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 3, characterized in that Said 5. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 1, characterized in that The specific steps of step 3 are as follows: Step 3-1, establish the level set function algorithm equation: Step 3-1-1: The simulated carotid artery lumen contour C obtained in step 2 b As the initial contour, and as the initial value of the level set function φ; Step 3-1-2: Drive the evolution of the level set function through the energy function E(φ), which is specifically defined as: E(φ)=μR(φ)+E ext (f) Among them, μ is the regularization term coefficient, R(φ) is the level set function regularization term, E ext (φ) is the external energy term, ext represents the external driving energy part, which drives the level set function to evolve towards the position with minimum energy; Step 3-2: Contour evolution: Step 3-2-1: In numerical calculation, use numerical approximation of Dirac function δ ε (x) and the numerical approximation of the Heaviside function H ε (x) respectively approximates the Dirac function δ and the Heaviside function H, where ε is a small fixed parameter, specifically: After numerical approximate calculation, the energy functional E is obtained. ε (φ) is approximately: The energy functional E is calculated and solved by Euler Lagrange equation and gradient descent flow method. ε (φ) is the minimum, then the above energy functional is minimized by solving the following gradient flow, where d p =p'(s) / s: Step 3-2-2: Use the inverse finite difference method to perform two-stage evolution iteration. The first stage quickly approaches the target contour area and iterates m times. The second stage seeks a fine segmentation boundary and iterates n times. The specific evolution process is as follows: Among them, kt is the number of iteration steps, (i,j) is the coordinate of a point on the contour, and are approximate estimates of the evolution equation in the first and second stages respectively; Step 3-2-3: After two stages of evolution, the lumen contour φ is obtained f As the final segmentation result, f represents the final result.

6. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 5, characterized in that The level set function is defined as: φ(x,y,t)=0 Among them, x and y represent the coordinates of the point in the two-dimensional space, and t represents the number of iterations.

7. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 5, characterized in that The R(φ) is specifically defined as: Where Ω is the neighborhood, p is the double potential well function, which is determined by the variable parameter s, so that the zero level set neighborhood is regularized. The specific definition is as follows:

8. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 5, characterized in that The E ext (φ) is specifically defined as: E ext (φ)=λ Lt L g (φ)+λ Ar A g (φ)+λ sc x sc (φ)+λ sa x sa (f) Among them, λ Lt Minimize the energy coefficient for a fixed weighted length, where Lt represents the length term; L g (φ) is the weighted length minimization energy term; λ Ar is a fixed weighted area energy coefficient, Ar represents the area term coefficient, A g (φ) is the weighted area energy term; λ sc is the shape constraint coefficient, sc represents the shape constraint; ξ sc (φ) is the shape constraint; λ sa is the shape area energy coefficient, sa represents the shape area term; ξ sa (φ) is the shape region energy term.

9. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 8, characterized in that The L g (φ) is specifically defined as: Among them, g is the boundary detection function, which is specifically defined as: Among them, G σ is a Gaussian kernel function with a standard deviation of σ, and I is the ultrasound image to be detected; The A g (φ) is defined as follows: A g (φ)=∫ Ω gH(-φ)dx Where H is the Heaviside function; The shape constraint term ξ sc (φ) is specifically defined as follows: x sc (φ)=∫ Ω (gH(-φ)-gH(-φ) pc )dx Among them, φ pc For a given prior shape, pc represents the prior shape; Shape region energy term ξ sa (φ) is defined as follows: x sa (φ)=∫ Ω (gH(-φ)-gH(-φ) et ))dx Among them, et represents the external extension shape, φ et is the extended shape corresponding to the carotid artery contour, K is the side length of (1-β) / 2·r φ The square structural element, r φ is the average inner diameter of the evolved shape.

10. The method for automatic positioning and segmentation of carotid arteries based on adaptive feature optimization according to claim 5, characterized in that Said and The specific definition is: Among them, sc1 represents the sc coefficient of the first stage of evolution; sa1 represents the sa coefficient of the first stage of evolution; sa2 represents the sa coefficient of the second stage of evolution; sc2 represents the sc coefficient of the second stage of evolution.

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