CN114693710A

CN114693710A - Blood vessel lumen intimal contour extraction method and device, ultrasonic equipment and storage medium

Info

Publication number: CN114693710A
Application number: CN202011625595.1A
Authority: CN
Inventors: 胡浩晖; 高梁; 朱彦聪; 黎英云
Original assignee: Sonoscape Medical Corp
Current assignee: Sonoscape Medical Corp
Priority date: 2020-12-30
Filing date: 2020-12-30
Publication date: 2022-07-01

Abstract

The application discloses a method and a device for extracting a blood vessel lumen intima contour, an ultrasonic device and a computer readable storage medium, wherein the method comprises the following steps: acquiring an ultrasonic image of the cross section in the blood vessel, and preprocessing the ultrasonic image to obtain a target image; determining a lumen wall area in the target image, and determining an initial intima contour line corresponding to the lumen wall area; fitting the initial intima contour line to obtain a final lumen intima contour line; the final intraluminal contour is added to the ultrasound image. Therefore, according to the method for extracting the outline of the intima of the blood vessel lumen provided by the application, the initial outline of the intima is firstly found, and then the intima of the lumen of the ultrasound image of the cross section in the blood vessel is accurately fitted, so that the accuracy of extracting the outline of the intima of the blood vessel lumen is improved.

Description

Blood vessel lumen intimal contour extraction method and device, ultrasonic equipment and storage medium

Technical Field

The present application relates to the field of ultrasound image technology, and more particularly, to a method and an apparatus for extracting a contour of a vessel lumen intima, an ultrasound device, and a computer-readable storage medium.

Background

Intravascular ultrasound (IVUS) refers to a medical imaging technique using a special catheter with an ultrasound probe connected to the end, in combination with non-invasive ultrasound and invasive catheter techniques. The intima-adventitia edge of the coronary artery vessel wall is an important basis for diagnosing and quantitatively analyzing various parameters of coronary artery diseases, so that the accurate detection of the intima-adventitia edge in the IVUS image has great significance for clinical diagnosis and treatment of the coronary artery diseases. In the related technology, the IVUS image gray distribution statistical clustering can be performed by adopting an image clustering algorithm, so that the division of the inner membrane and the outer membrane of the blood vessel lumen is realized. However, due to the complexity of IVUS images, lesion areas exist in different degrees, and complicated image features such as artifacts and plaques exist, so that the accuracy of segmenting the outline of the blood vessel lumen intima is low.

Therefore, how to improve the accuracy of the blood vessel lumen intima contour extraction is a technical problem to be solved by those skilled in the art.

Disclosure of Invention

The application aims to provide a method and a device for extracting a blood vessel lumen intima contour, an ultrasonic device and a computer readable storage medium, and the accuracy of extracting the blood vessel lumen intima contour is improved.

In order to achieve the above object, the present application provides a method for extracting a contour of an intima of a blood vessel lumen, comprising:

acquiring an ultrasonic image of a cross section in a blood vessel, and preprocessing the ultrasonic image to obtain a target image;

determining a lumen wall area in the target image, and determining an initial intima contour line corresponding to the lumen wall area;

fitting the initial intima contour line to obtain a final lumen intima contour line;

adding the final endoluminal contour to the ultrasound image.

The preprocessing the ultrasonic image to obtain a target image includes:

down-sampling and filtering the ultrasonic image;

correspondingly, fitting the initial intima contour line to obtain a final lumen intima contour line includes:

and fitting the initial intima contour line to obtain a fitting lumen intima contour line, and performing up-sampling treatment on the fitting lumen intima contour line to obtain a final lumen intima contour line.

The preprocessing the ultrasonic image to obtain a target image includes:

catheter effects in the image are removed.

Wherein the removing the catheter effect in the image comprises:

determining pixel points with the first gray value not being zero in each direction by taking the central position of the image as a starting point as target positions;

calculating an average value of the distance between each target position and the image center position, and calculating the sum of the average value and a preset offset distance as a superposition distance;

and setting all gray values of pixel points with the distance from the central position of the image to the central position of the image smaller than the superposition distance as a lower limit value.

The preprocessing the ultrasound image to obtain a target image includes:

and carrying out significance enhancement on the interested region in the image.

The preprocessing the ultrasonic image to obtain a target image includes:

and carrying out normalization processing on the gray values of the pixel points in the image.

Wherein the determining a lumen wall region in the target image comprises:

determining the category of each pixel point in the target image so as to divide the target image into different clustering blocks;

and determining a lumen wall area in the clustering block, and removing external noise and internal noise of the lumen wall area.

The determining the category of each pixel point in the target image to segment the target image into different clustering blocks includes:

and carrying out clustering operation on pixel points in the target image by a fuzzy C-means clustering algorithm based on a gray level histogram so as to divide the target image into different clustering blocks.

The fuzzy C-means clustering algorithm based on the gray level histogram performs clustering operation on the pixel points in the target image to divide the target image into different clustering blocks, and the method comprises the following steps:

carrying out corrosion reconstruction on the target image by using morphological processing to obtain an intermediate image, and determining a clustering parameter; the clustering parameters comprise the number of clustering centers, kernel processing scales and fuzzy factors;

determining different gray values as initial clustering centers according to the range of the gray level histogram of the intermediate image and the number of the clustering centers, and determining a gray value membership function based on the fuzzy factor and Euclidean clustering of the gray level values in the gray level histogram and the initial clustering centers;

updating the initial clustering center and the gray value membership function through iteration until the fuzzy C mean value objective function is optimized or the iteration times reach preset times;

and determining the maximum value in the updated clustering center as a second gray threshold, and setting all gray values of pixel points with gray values larger than or equal to the second gray threshold in the intermediate image as an upper limit value and gray values of pixel points with gray values smaller than the second gray threshold in the intermediate image as a lower limit value so as to divide the target image into different clustering blocks.

Wherein the determining lumen wall regions in the cluster block comprises:

determining the area and the area center position of each clustering block, and determining the clustering block with the largest area and the closest distance between the area center position and the image center position as a lumen wall area.

Wherein removing external noise from the lumen wall region comprises:

calculating Euclidean distances between the region center positions of all the clustering blocks and the region center position of the lumen wall region;

counting the overlapping area between the surrounding rectangles corresponding to all the clustering blocks and the surrounding rectangles corresponding to the lumen wall area;

and determining the clustering block with the Euclidean distance larger than a preset distance threshold value and the overlapping area smaller than a first preset area threshold value as the external noise of the lumen wall area, and removing the external noise.

Wherein removing internal noise of the lumen wall region comprises:

performing polar coordinate conversion on the image without the external noise to obtain a first polar coordinate image;

determining the area of each clustering block, the gray average value of pixel points and the number of overlapped pixel points with the lumen wall area in the first polar coordinate image;

and determining the clustering blocks with the area smaller than a second preset area threshold, the gray average value larger than a third gray threshold and the number of overlapped pixel points larger than a preset number as the internal noise of the tube cavity wall area, and removing the internal noise.

Wherein, the determining the initial intimal contour line corresponding to the lumen wall region includes:

performing polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image, and taking a pixel point of which the first gray value in each row in the first two-dimensional image is not zero as a contour point;

and utilizing an interpolation method to complement the missing part of the connecting line of the contour points, and carrying out rectangular coordinate conversion to obtain the initial intima contour line.

Wherein after removing the external noise and the internal noise of the lumen wall region, the method further comprises:

determining the transverse maximum width of the lumen wall area, and judging whether the ratio of the transverse maximum width to the width of the image without the external noise and the internal noise is greater than a preset value;

if so, performing polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image;

if not, updating the clustering parameters, and re-entering the step of determining different gray values as initial clustering centers according to the range of the gray level histogram of the intermediate image and the number of the clustering centers.

Wherein fitting the initial intimal contour line includes:

and fitting the initial intimal contour line by using a vector field convolution active contour model.

To achieve the above object, the present application provides a blood vessel lumen intima contour extraction device, comprising:

the preprocessing module is used for acquiring an ultrasonic image of the cross section in the blood vessel and preprocessing the ultrasonic image to obtain a target image;

the determining module is used for determining a lumen wall area in the target image and determining an initial intima contour line corresponding to the lumen wall area;

the fitting module is used for fitting the initial intima contour line to obtain a final lumen intima contour line;

an adding module for adding the final intraluminal contour line to the ultrasound image.

To achieve the above object, the present application provides an ultrasound apparatus comprising:

a memory for storing a computer program;

a processor for implementing the steps of the blood vessel lumen intima contour extraction method when executing the computer program;

a display for displaying an ultrasound image of an intravascular cross-section and an intraluminal contour in the ultrasound image.

To achieve the above object, the present application provides a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the vessel lumen intima contour extraction method as described above.

According to the scheme, the method for extracting the intima contour of the blood vessel lumen comprises the following steps: acquiring an ultrasonic image of a cross section in a blood vessel, and preprocessing the ultrasonic image to obtain a target image; determining a lumen wall area in the target image, and determining an initial intima contour line corresponding to the lumen wall area; fitting the initial intima contour line to obtain a final lumen intima contour line; adding the final endoluminal contour to the ultrasound image.

Therefore, according to the method for extracting the outline of the intima of the blood vessel lumen provided by the application, the initial outline of the intima is firstly found, and then the intima of the lumen of the ultrasound image of the cross section in the blood vessel is accurately fitted, so that the accuracy of extracting the outline of the intima of the blood vessel lumen is improved. The application also discloses a blood vessel lumen intimal contour extraction device, ultrasonic equipment and a computer readable storage medium, which can also realize the technical effects.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:

FIG. 1 is a flow diagram illustrating a method for vessel lumen intimal contour extraction in accordance with an exemplary embodiment;

FIGS. 2a and 2b are schematic diagrams illustrating before and after removal of a catheter effect according to an exemplary embodiment;

FIGS. 3a and 3b are schematic diagrams illustrating a saliency enhancement and normalization process before and after according to an exemplary embodiment;

FIG. 4a is a schematic diagram illustrating an ellipse after fitting according to an exemplary embodiment;

FIG. 4b is a schematic diagram illustrating an edge image according to an exemplary embodiment;

FIG. 4c is a schematic diagram illustrating a final endoluminal contour according to an exemplary embodiment;

FIG. 5 is a flow chart illustrating another method of vessel lumen intimal contour extraction in accordance with an exemplary embodiment;

FIG. 6a is a schematic diagram illustrating morphological processing according to an exemplary embodiment;

FIG. 6b is a schematic diagram illustrating fuzzy clustering in accordance with an exemplary embodiment;

FIG. 6c is a schematic diagram illustrating a threshold segmentation in accordance with an exemplary embodiment;

FIG. 7 is a schematic illustration of a connected region shown in accordance with an exemplary embodiment;

FIG. 8a is a schematic illustration showing the removal of an outer candidate lumen wall region in accordance with an exemplary embodiment;

FIG. 8b is a schematic diagram of a first polar image shown in accordance with an exemplary embodiment;

FIG. 8c is a schematic illustration showing the removal of an inner candidate lumen wall region in accordance with an exemplary embodiment;

FIG. 9a is a schematic diagram illustrating a second polar image according to an exemplary embodiment;

FIG. 9b is a schematic diagram illustrating contour points in an initial intimal contour in accordance with an exemplary embodiment;

FIG. 9c is a schematic diagram illustrating interpolation of contour points in accordance with an exemplary embodiment;

FIG. 9d is a schematic diagram illustrating an initial intimal contour line in accordance with an exemplary embodiment;

FIG. 10 is a flow chart illustrating yet another method for vessel lumen intimal contour extraction in accordance with an exemplary embodiment;

FIG. 11 is a block diagram illustrating a vascular intraluminal lining contour extraction device in accordance with an exemplary embodiment;

FIG. 12 is a block diagram of an ultrasound device shown in accordance with an exemplary embodiment.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. In addition, in the embodiments of the present application, "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or a sequential order.

The embodiment of the application discloses a method for extracting the intima contour of a blood vessel lumen, which improves the accuracy of extracting the intima contour of the blood vessel lumen.

Referring to fig. 1, a flowchart of a method for extracting a contour of an intraluminal lining of a blood vessel according to an exemplary embodiment is shown, as shown in fig. 1, including:

s101: acquiring an ultrasonic image of a cross section in a blood vessel, and preprocessing the ultrasonic image to obtain a target image;

the execution body of the application may be an ultrasound device with the purpose of extracting the vessel lumen intima in an ultrasound image of the intravascular cross section. In this step, the ultrasound image of the intravascular cross section is first preprocessed to obtain a target image. As a possible implementation, the preprocessing of the ultrasound image may include: and performing down-sampling and filtering processing on the ultrasonic image.

In a specific implementation, due to the large size of the ultrasound image of the intravascular cross section, it needs to be down-sampled, and the down-sampling process can be used to increase the speed of subsequent image processing. For example, a sampling rate of 0.5 is set, i.e., one data is taken out of every two data in the horizontal and vertical directions, so that the image size is reduced by one time. And then, filtering the down-sampled image to eliminate the influence of partial noise on the inner film edge extraction. For example, the Filter coefficient may be set to [1,4,6,4,1]/16, and during the filtering process, the horizontal convolution operation may be performed on the original image data, and then the vertical convolution operation may be performed on the result to complete the filtering process.

Preprocessing the ultrasound image may further include: removing catheter effects in the image. It can be understood that, since the center of the image of the ultrasound image of the intravascular cross section is a catheter, and ring halo artifacts are near the center of the image, which easily interfere with the subsequent contour extraction, the highlight ring halo artifacts near the catheter can be removed in the preprocessing process. As a possible implementation, the removing the duct effect in the image includes: determining pixel points with the first gray value not being zero in each direction by taking the central position of the image as a starting point as target positions; calculating an average value of the distance between each target position and the image center position, and calculating the sum of the average value and a preset offset distance as a superposition distance; and setting all gray values of pixel points with the distance from the central position of the image to be smaller than the superposition distance as a lower limit value to obtain a second intermediate image. In this embodiment, a pixel point with a first gray value not being zero is searched from the image center position to the 360-degree direction of the circumference as a target position, an average value of distances between each target position of the 360-degree circumference and the image center position is calculated, and then a sum of the average value and a preset offset distance is determined as a superimposition distance, where the preset offset distance is set empirically, and may be set to 15 pixels, for example. And finally, setting all the gray values of the pixel points which are smaller than the superposition distance from the central position of the image as a lower limit value, for example, setting all the gray values as zero. Before removing the channeling effect, as shown in fig. 2a, and after removing the channeling effect, as shown in fig. 2 b.

Preprocessing the ultrasound image may further include: salient enhancement is carried out on interested areas in the image to improve the tissue map near the blood vessel wallGray values are imaged to highlight the intraluminal lining contours. As a possible implementation, the salient enhancement of the region of interest in the image includes: determining a region of interest in the image; the gray value of each pixel point in the region of interest is greater than a first gray threshold value; calculating the pixel average value of all pixel points in the region of interest, and determining a mapping curve based on the pixel average value; and processing the gray value of each pixel point in the image by using the mapping curve. It will be appreciated that the purpose of the significant enhancement is to highlight regions of interest in the tissue near the vessel wall, suppressing regions of non-interest within the vessel lumen. In this embodiment, the number of each gray value in the image is counted to calculate the pixel average value of all the pixel points in the region of interest, and a mapping curve y is established as k × log₂And x, resetting the gray value of each pixel point in the image by using the mapping curve, wherein x is the original gray value of the pixel point, y is the updated gray value of the pixel point, and k is the average value of the pixels. Meanwhile, the gray value of the highlight area is calculated, a mapping curve is established, and finally the original IVUS is subjected to significance enhancement through the mapping curve.

Further, the preprocessing the ultrasound image may further include: and carrying out normalization processing on the gray values of the pixel points in the image. In specific implementation, the gray value of a pixel point in an image is normalized to obtain a target image, and the gray value is prevented from exceeding the interval of 0-255. As shown in fig. 3a, the significance enhancement and normalization processing is shown in fig. 3 b.

It should be noted that the sequence relationship among the several steps of the preprocessing is not strictly limited in this embodiment, and the number of times each step is executed is not strictly limited, and the preprocessing can be flexibly adjusted according to the imaging condition of the actual ultrasound image. As a preferred embodiment, the down-sampling and filtering processing is performed on the ultrasound image, the catheter effect in the image is removed, the region of interest in the image is enhanced in significance, and the normalization processing is performed on the gray value of the pixel point in the image.

S102: determining a lumen wall area in the target image, and determining an initial intima contour line corresponding to the lumen wall area;

the purpose of this step is to determine the lumen wall region in the target image and to use the contour of the lumen wall region as the initial intima contour. As a preferred embodiment, the determining the lumen wall region in the target image includes: determining the category of each pixel point in the target image so as to divide the target image into different clustering blocks; and determining a lumen wall area in the clustering block, and removing external noise and internal noise of the lumen wall area. In the specific implementation, a clustering algorithm is utilized to cluster pixel points in a target image, the target image is divided into different clustering blocks, then a lumen wall area is determined in all the clustering blocks, and other clustering blocks are taken as noise to be removed.

Preferably, the determining the category of each pixel point in the target image to segment the target image into different clustering blocks includes: and carrying out clustering operation on pixel points in the target image by a fuzzy C-means clustering algorithm based on a gray level histogram so as to divide the target image into different clustering blocks. It can be understood that, in the fuzzy C-means clustering algorithm based on the gray level histogram, the clustering centers are different gray levels, so that the target image is divided into continuous classes in gray level and position, and then different clustering blocks for segmenting the vascular lumen wall are further extracted.

S103: fitting the initial intima contour line to obtain a final lumen intima contour line;

s104: adding the final endoluminal contour to the ultrasound image.

Because the intima contour line determined by fuzzy clustering deviates from the actual blood vessel lumen, the initial intima contour line is subjected to evolution fitting, and the final lumen intima contour line is extracted and displayed in an ultrasonic image of the cross section in the blood vessel.

As a preferred embodiment, the fitting the initial intimal contour line includes: and fitting the initial intimal contour line by using a vector field convolution active contour model. In a specific implementation, the ellipse fitting is performed on the initial intima contour line to reduce the complexity of contour line fitting calculation, and as shown in fig. 4a, the initial intima contour line v(s) ═ x(s), y (s)) can be expressed as:

wherein (x)_c,y_c) And r and theta are the distance radius and the angle between the contour point in the initial intima contour line and the image center position respectively.

The contour line in the contour model satisfies the following equation:

wherein E_intThe initial intima contour line external force field comprises two external force field parameters alpha and beta, wherein the parameter alpha controls contour line tension, the curve is more difficult to stretch when the value of the parameter alpha is larger, the curve shrinks more quickly, the parameter beta controls curve rigidity, the curve is more difficult to bend when the value of the parameter beta is larger, and the curve is smoother. And E_ext[v(s)]F, convolved with the image edge by a vector field convolution kernel, as an external force term for an image feature_vfcTo represent F_vfc(x,y)＝f(x,y)*K(x,y)＝[f(x,y)*u(x,y),f(x,y)*v(x,y)]Where f (x, y) is the edge image detected by phase consistency of the ultrasound image of the cross-section in the blood vessel, as shown in fig. 4b, K (x, y) is a vector field convolution kernel defined as K (x, y) ═ u (x, y), v (x, y)]M (x, y) n (x, y), u (x, y), v (x, y) are the vector field convolution kernel components in the horizontal and vertical methods, respectively, while n (x, y) points to the unit vector at the kernel origin (0,0), and m (x, y) is the modulus value of each vector. Minimizing the above energy equation E_SnakeThen the following euler formula needs to be satisfied:

if v(s) is taken as a function of time, it can be expressed as v (s, t), so that by minimizing the above formula, it can be obtained that v (s, t) is taken as a variableThe optimal intraluminal contours can be found by a number of iterations, as shown in fig. 4 c. The iterative function with v (s, t) as a variable is:

it should be noted that, since the ultrasound image of the intravascular cross section is preprocessed, the final lumen intima contour line is positioned on the down-sampled image, and in order to actually restore the lumen intima contour line of the ultrasound image of the intravascular cross section, the final lumen intima contour line needs to be up-sampled according to the previously set sampling rate, and the final lumen intima contour line is positioned in the ultrasound image of the original intravascular cross section. Namely, the step of fitting the initial intima contour line to obtain a final lumen intima contour line comprises the following steps: and fitting the initial intima contour line to obtain a fitting lumen intima contour line, and performing up-sampling treatment on the fitting lumen intima contour line to obtain a final lumen intima contour line.

Therefore, according to the method for extracting the intima contour of the blood vessel lumen provided by the embodiment of the application, the initial contour line of the intima is firstly found, and then the intima of the lumen of the ultrasonic image of the cross section in the blood vessel is accurately fitted, so that the accuracy of extracting the intima contour of the blood vessel lumen is improved.

The embodiment of the application discloses a method for extracting the intimal contour of a blood vessel lumen, and compared with the previous embodiment, the embodiment further explains and optimizes the technical scheme. Specifically, the method comprises the following steps:

referring to fig. 5, a flowchart of another vessel lumen intimal contour extraction method is shown according to an exemplary embodiment, as shown in fig. 5, including:

s201: acquiring an ultrasonic image of a cross section in a blood vessel, and preprocessing the ultrasonic image to obtain a target image;

s202: performing corrosion reconstruction on the target image by using morphological processing to obtain an intermediate image, and determining a clustering parameter; the clustering parameters comprise the number of clustering centers, kernel processing scales and fuzzy factors;

in a specific implementation, since speckle noise exists in the target image, it is necessary to reconstruct an intermediate image by erosion using morphological processing, as shown in fig. 6 a. In the process of carrying out fuzzy C-means clustering based on a gray level histogram on the intermediate image, firstly, clustering parameters including the number of clustering centers, kernel processing scale and fuzzy factors are determined.

S203: determining different gray values as initial clustering centers according to the range of the gray level histogram of the intermediate image and the number of the clustering centers, and determining a gray value membership function based on the fuzzy factor and Euclidean clustering of the gray level values in the gray level histogram and the initial clustering centers;

in this step, different gray values can be determined by the range of the gray histogram of the intermediate image and the number of the cluster centers, and are used as initial cluster centers. Further, a gray value membership function can be deduced according to a membership function matrix in a standard fuzzy C-means clustering algorithm:

wherein C is the number of cluster centers, k is 1, 2, 3, and C, m is a blurring factor, d is_ij＝||x_i-v_iAnd | | is the Euclidean distance from the gray value of the jth pixel point to the ith clustering center.

S204: updating the initial clustering center and the gray value membership function through iteration until the fuzzy C mean value objective function is optimized or the iteration times reach preset times;

in this step, the initial clustering center and the gray value membership function are updated through loop iteration, so that the fuzzy C-means objective function is optimized or the iteration frequency reaches a preset frequency to terminate the loop iteration, and finally the final gray value membership function and the clustering center can be obtained, wherein the calculation formula is as follows:

clustering center:

an objective function:

wherein v is_iIs the ith cluster center, x_iIs the gray value of the jth pixel point, and n is the total number of pixel points in the fourth intermediate image. And determining the category of each pixel point in the fourth intermediate image according to the final grey value membership function, and dividing the fourth intermediate image into different parts, as shown in fig. 6 b.

S205: determining the maximum value in the updated clustering center as a second gray threshold, and setting all gray values of pixel points with gray values larger than or equal to the second gray threshold in the intermediate image as an upper limit value and all gray values of pixel points with gray values smaller than the second gray threshold in the intermediate image as a lower limit value so as to divide the target image into different clustering blocks;

in specific implementation, the maximum value in the final clustering center is determined as the second gray threshold, for the pixel points in the intermediate image, the gray value of the pixel point whose gray value is greater than or equal to the second gray threshold is set as an upper limit value, where the upper limit value may be 255, and the gray value of the pixel point whose gray value is less than the second gray threshold is set as a lower limit value, where the lower limit value may be 0, so as to segment the blood vessel lumen region, as shown in fig. 6 c.

S206: determining a lumen wall area in the clustering block, and removing external noise and internal noise of the lumen wall area;

as a possible implementation, the determining the lumen wall region in the clustering block includes: and determining the area and the region center position of each clustering block, and determining the clustering block with the largest area and the closest distance between the region center position and the image center position as a lumen wall region.

It can be understood that the image after fuzzy C-means clustering is divided into different regions, and the different image regions are identified by the image connectivity method, as shown in fig. 7. Determining the lumen wall area according to the characteristics of the lumen wall area closest to the center of the image, namely determining the area and the area center position of each clustering block, and determining the clustering block with the largest area and the closest distance between the area center position and the image center position as the final lumen wall area.

In this step, in order to eliminate the influence of other interference-like blocks such as pericardium and artifact, it is necessary to remove other clustering blocks except the final lumen wall region, i.e. to remove external noise and internal noise of the lumen wall region.

It can be understood that since the external noise is far away from the final lumen wall region, the euclidean distance between the region center position of the cluster block and the region center position of the final lumen wall region can be calculated under the rectangular coordinate system for removal. The internal noise is mostly caused by factors such as guide wire artifacts and strong reflection of calcified plaques, is located in the final lumen wall area, and cannot be removed in the above way, so that the image can be subjected to polar coordinate conversion, and the internal noise can be removed. Of course, the external noise can be removed in the polar coordinate image, but the external noise and the internal noise are removed in the rectangular coordinate image and the polar coordinate image respectively, so that confusion between the external noise and the internal noise can be avoided, and the removal effect is better.

As a possible embodiment, removing external noise from the lumen wall region includes: calculating Euclidean distances between the region center positions of all the clustering blocks and the region center position of the lumen wall region; counting the overlapping area between the surrounding rectangles corresponding to all the clustering blocks and the surrounding rectangles corresponding to the lumen wall area; and determining the clustering block with the Euclidean distance larger than a preset distance threshold value and the overlapping area smaller than a first preset area threshold value as the external noise of the lumen wall area, and removing the external noise.

In the present embodiment, the removal is performed by presetting a distance threshold and a region overlapping area threshold. Firstly, calculating the Euclidean distance between the final region center position of the lumen wall region and the region center positions of all the clustering blocks, determining the surrounding rectangles corresponding to the clustering blocks, counting the overlapping area between the surrounding rectangle corresponding to each clustering block and the final surrounding rectangle corresponding to the lumen wall region, when the overlapping area is smaller than a first preset area threshold value and the Euclidean distance is larger than a preset distance threshold value, defining the clustering block as an external candidate lumen wall region, and removing the external noise, as shown in FIG. 8 a.

As a possible embodiment, removing internal noise of the lumen wall region includes: performing polar coordinate conversion on the image without the external noise to obtain a first polar coordinate image; determining the area of each clustering block, the gray average value of pixel points and the number of overlapped pixel points with the lumen wall area in the first polar coordinate image; and determining the clustering blocks with the area smaller than a second preset area threshold, the gray average value larger than a third gray threshold and the number of overlapped pixel points larger than a preset number as the internal noise of the tube cavity wall area, and removing the internal noise.

In the present embodiment, the image from which the external noise is removed is subjected to polar coordinate conversion to obtain a first polar coordinate image, as shown in fig. 8 b. The rectangular coordinates (x, y) and polar coordinates (r, θ) are transformed as follows:

in the first polar coordinate image, calculating the area of the clustering block and the gray average value of the pixel points, and determining the internal noise by combining the number of the overlapped pixel points with the final lumen wall area. In a specific implementation, the final lumen wall area and all the cluster blocks may be projected onto the abscissa of the second polar coordinate image, and the number of overlapped pixel points is determined according to the overlapping length of the projection corresponding to each cluster block and the projection corresponding to the final lumen wall area. When the area of the region is smaller than the second preset area threshold, the average grayscale value is greater than the third grayscale threshold, and the number of overlapped pixel points is greater than the preset number, the cluster block is defined as the internal noise, and the internal noise is removed, as shown in fig. 8 c.

S207: performing polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image, and taking a pixel point of which the first gray value in each row in the first two-dimensional image is not zero as a contour point;

s208: utilizing an interpolation method to complement the missing part of the connecting line of the contour points, and carrying out rectangular coordinate conversion to obtain an initial intima contour line;

in a specific implementation, the image with the external noise and the internal noise removed is subjected to polar coordinate conversion to obtain a second polar coordinate image, as shown in fig. 9a, and an edge map of the lumen region is detected through phase consistency. Since the lumen intima contour line is located on the upper portion of the image after the polar coordinate change, the pixel point of each column of the second polar coordinate image with the first gray value not being zero is taken as the contour point in the initial intima contour line, as shown in fig. 9 b. Spline cubic interpolation is performed on the missing part, i.e. the part with zero ordinate, and the point of the whole intima edge smoothness is found, as shown in fig. 9 c. And finally, performing coordinate transformation on the contour points obtained by calculation, and outputting the initial intima contour line in a line segment connection mode, as shown in fig. 9 d.

S209: fitting the initial intima contour line in the ultrasonic image by using a vector field convolution active contour model to obtain a final lumen intima contour line;

s210: adding the final endoluminal contour to the ultrasound image.

According to the method for extracting the intima contour of the blood vessel lumen, the intima contour of the blood vessel lumen is extracted by combining two methods, namely fuzzy clustering and an active contour model. Firstly, preprocessing an ultrasonic image of an intravascular cross section, then searching an initial intima contour line by adopting a fuzzy C-means clustering algorithm of a gray histogram, and finally carrying out evolution fitting on the intima of a lumen of the ultrasonic image of the intravascular cross section by a vector field convolution active contour model according to the initial intima contour line. Therefore, according to the method for extracting the intima contour of the blood vessel lumen provided by the embodiment of the application, the intima initial contour line is searched based on the fuzzy C-means clustering of the gray histogram, and the vector field convolution active contour model accurately fits the intima of the lumen of the ultrasonic image of the cross section in the blood vessel according to the initial intima contour line, so that the accuracy of extracting the intima contour of the blood vessel lumen is improved.

referring to fig. 10, a flowchart of yet another method for extracting a contour of an endovascular lumen intima in a blood vessel according to an exemplary embodiment is shown, as shown in fig. 10, including:

s301: acquiring an ultrasonic image of the cross section in a blood vessel, and preprocessing the ultrasonic image to obtain a target image;

s302: carrying out corrosion reconstruction on the target image by using morphological processing to obtain an intermediate image, and determining a clustering parameter; the clustering parameters comprise the number of clustering centers, kernel processing scales and fuzzy factors;

s303: determining different gray values as initial clustering centers according to the range of the gray level histogram of the intermediate image and the number of the clustering centers, and determining a gray value membership function based on the fuzzy factor and Euclidean clustering of the gray level values in the gray level histogram and the initial clustering centers;

s304: updating the initial clustering center and the gray value membership function through iteration until the fuzzy C mean value objective function is optimized or the iteration times reach preset times;

s305: determining the maximum value in the updated clustering center as a second gray threshold, and setting all gray values of pixel points with gray values larger than or equal to the second gray threshold in the intermediate image as an upper limit value and all gray values of pixel points with gray values smaller than the second gray threshold in the intermediate image as a lower limit value so as to divide the target image into different clustering blocks;

s306: determining the area and the area center position of each clustering block, and determining the clustering block with the largest area and the closest distance between the area center position and the image center position as a lumen wall area;

s307: calculating Euclidean distances between the region center positions of all the clustering blocks and the region center position of the lumen wall region;

s308: counting the overlapping area between the surrounding rectangles corresponding to all the clustering blocks and the surrounding rectangles corresponding to the lumen wall area;

s309: determining the clustering block with the Euclidean distance larger than a preset distance threshold value and the overlapping area smaller than a first preset area threshold value as the external noise of the lumen wall area, and removing the external noise;

s310: performing polar coordinate conversion on the image without the external noise to obtain a first polar coordinate image;

s311: determining the area of each clustering block, the gray average value of pixel points and the number of overlapped pixel points with the lumen wall area in the first polar coordinate image;

s312: and determining the cluster blocks of which the area is smaller than a second preset area threshold, the gray average value is larger than a third gray threshold and the number of overlapped pixel points is larger than a preset number as the internal noise of the tube cavity wall area, and removing the internal noise.

S313: determining the transverse maximum width of the lumen wall area, and judging whether the ratio of the transverse maximum width to the width of the image without the external noise and the internal noise is greater than a preset value; if yes, entering S315; if not, the process goes to S314;

s314: the clustering parameters are updated, and the process re-enters S303.

It should be noted that due to the complexity of coronary vessels, the shape of the imaged IVUS image varies, and there is a lot of noise and artifact interference, so that the clustering parameters need to be adjusted according to different tissue shapes. In this embodiment, after the final lumen wall region is extracted, the maximum lateral width thereof is determined. And judging whether the ratio of the transverse maximum width to the width of the sixth intermediate image is greater than a preset value, if so, indicating that the fuzzy clustering effect is good, and better segmenting the lumen area of the blood vessel so as to facilitate the initial positioning of the subsequent intima contour. If not, the clustering parameters need to be updated, and the operations from S303 to S316 are repeated. The preset value is not limited herein, and may be set to 0.7, for example.

S315: performing polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image, and taking a pixel point of which the first gray value in each row in the first two-dimensional image is not zero as a contour point;

s316: utilizing an interpolation method to complement the missing part of the connecting line of the contour points, and carrying out rectangular coordinate conversion to obtain an initial intima contour line;

s317: fitting the initial intima contour line in the ultrasonic image by using a vector field convolution active contour model to obtain a final lumen intima contour line;

s318: adding the final endoluminal contour to the ultrasound image.

Therefore, the embodiment discloses the updating strategy of the clustering parameters and the judgment standard of the fuzzy clustering effect, improves the accuracy of extracting the initial intima contour by fuzzy clustering, and further improves the accuracy of extracting the intima contour of the blood vessel lumen.

In the following, a blood vessel lumen intima contour extraction device provided by an embodiment of the present application is introduced, and a blood vessel lumen intima contour extraction device described below and a blood vessel lumen intima contour extraction method described above may be referred to each other.

Referring to fig. 11, a block diagram of a blood vessel lumen intima contour extraction device according to an exemplary embodiment is shown, as shown in fig. 11, including:

the preprocessing module 100 is configured to acquire an ultrasound image of a cross section in a blood vessel and preprocess the ultrasound image to obtain a target image;

a determining module 200, configured to determine a lumen wall area in the target image, and determine an initial intima contour line corresponding to the lumen wall area;

a fitting module 300, configured to fit the initial intimal contour line to obtain a final lumen intimal contour line;

an adding module 400 for adding the final endoluminal contour to the ultrasound image.

Therefore, the blood vessel lumen intima contour extraction device provided by the embodiment of the application firstly searches for the initial intima contour line, secondly performs accurate fitting on the intima of the lumen of the ultrasonic image of the cross section in the blood vessel, and improves the accuracy of blood vessel lumen intima contour extraction.

On the basis of the foregoing embodiment, as a preferred implementation manner, the preprocessing module 100 is specifically a module that acquires an ultrasound image of an intravascular cross section, and performs down-sampling and filtering processing on the ultrasound image;

correspondingly, the fitting module 300 is specifically a module for fitting the initial intima contour line to obtain a fitted intima contour line, and performing upsampling processing on the fitted intima contour line to obtain a final intima contour line.

On the basis of the above embodiment, as a preferred implementation manner, the preprocessing module 100 is embodied as a module for acquiring an ultrasound image of an intravascular cross section and removing a catheter effect in the image.

On the basis of the above embodiment, as a preferred implementation, the preprocessing module 100 includes:

an acquisition unit for acquiring an ultrasound image of an intravascular cross section;

the first determining unit is used for determining pixel points, with the central position of the image as a starting point, of which the first gray value in each direction is not zero as a target position;

the calculating unit is used for calculating the average value of the distance between each target position and the image center position, and calculating the sum of the average value and a preset offset distance as a superposition distance;

and the setting unit is used for setting all the gray values of the pixel points with the distance from the central position of the image to be less than the superposition distance as a lower limit value.

On the basis of the above embodiment, as a preferred implementation manner, the preprocessing module 100 is specifically a module for acquiring an ultrasound image of an intravascular cross section and performing significant enhancement on a region of interest in the image.

On the basis of the foregoing embodiment, as a preferred implementation manner, the preprocessing module 100 is specifically a module that acquires an ultrasound image of an intravascular cross section and performs normalization processing on a gray value of a pixel point in the image.

On the basis of the foregoing embodiment, as a preferred implementation manner, the determining module 200 includes:

the segmentation submodule is used for determining the category of each pixel point in the target image so as to segment the target image into different clustering blocks;

the first determining submodule is used for determining a lumen wall area in the clustering block and removing external noise and internal noise of the lumen wall area;

and the second determining submodule is used for determining the initial intima contour line corresponding to the lumen wall area.

On the basis of the foregoing embodiment, as a preferred implementation manner, the segmentation sub-module is a unit that performs a clustering operation on pixel points in the target image based on a fuzzy C-means clustering algorithm of a gray histogram to segment the target image into different clustering blocks.

On the basis of the above embodiment, as a preferred implementation, the partitioning sub-module includes:

the reconstruction unit is used for carrying out corrosion reconstruction on the target image by using morphological processing to obtain an intermediate image and determining clustering parameters; the clustering parameters comprise the number of clustering centers, kernel processing scales and fuzzy factors;

the second determining unit is used for determining different gray values as initial clustering centers according to the range of the gray histogram of the intermediate image and the number of the clustering centers, and determining a gray value membership function based on the fuzzy factor and Euclidean clustering of the gray values in the gray histogram and the initial clustering centers;

the updating unit is used for updating the initial clustering center and the gray value membership function through iteration until the fuzzy C mean value objective function is optimized or the iteration times reach preset times;

and the setting unit is used for determining the maximum value in the updated clustering center as a second gray threshold, and setting all the gray values of the pixels of which the gray values are greater than or equal to the second gray threshold in the intermediate image as upper limit values and all the gray values of the pixels of which the gray values are less than the second gray threshold as lower limit values so as to divide the target image into different clustering blocks.

On the basis of the foregoing embodiment, as a preferred implementation, the first determining sub-module includes:

the third determining unit is used for determining the area and the area center position of each clustering block, and determining the clustering block with the largest area and the closest distance between the area center position and the image center position as a lumen wall area;

a removal unit for removing external noise and internal noise of the lumen wall region.

On the basis of the above embodiment, as a preferred implementation, the removing unit includes:

the calculation subunit is used for calculating Euclidean distances between the region center positions of all the clustering blocks and the region center position of the lumen wall region;

the statistic subunit is used for counting the overlapping area between the surrounding rectangles corresponding to all the clustering blocks and the surrounding rectangles corresponding to the lumen wall area;

and the first removing subunit is used for determining the clustering block of which the Euclidean distance is greater than a preset distance threshold and the overlapping area is smaller than a first preset area threshold as the external noise of the lumen wall area, and removing the external noise.

the conversion subunit is used for performing polar coordinate conversion on the image without the external noise to obtain a first polar coordinate image;

the determining subunit is used for determining the area of each clustering block, the gray average value of pixel points and the number of overlapped pixel points with the lumen wall area in the first polar coordinate image;

and the second removing subunit is used for determining the clustering blocks with the area smaller than a second preset area threshold, the gray average value larger than a third gray threshold and the overlapped pixel point number larger than a preset number as the internal noise of the tube cavity wall area, and removing the internal noise.

On the basis of the foregoing embodiment, as a preferred implementation, the second determining sub-module includes:

the conversion unit is used for carrying out polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image, and taking a pixel point of which the first gray value in each row in the first two-dimensional image is not zero as a contour point;

and the supplementing unit is used for supplementing the missing part of the connecting line of the contour points by using an interpolation method and performing rectangular coordinate conversion to obtain the initial intima contour line.

On the basis of the foregoing embodiment, as a preferred implementation, the second determining sub-module further includes:

the judging unit is used for determining the transverse maximum width of the lumen wall area and judging whether the ratio of the transverse maximum width to the width of the image without the external noise and the internal noise is greater than a preset value; if yes, starting the working process of the conversion unit; if not, updating the clustering parameters, and restarting the working process of the second determining unit.

On the basis of the foregoing embodiment, as a preferred implementation manner, the fitting module 300 is specifically a module that fits the initial intimal contour line by using a vector field convolution active contour model to obtain a final intimal contour line of the lumen.

With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.

Based on the hardware implementation of the program module, and in order to implement the method of the embodiment of the present application, an embodiment of the present application further provides an ultrasound apparatus, and fig. 12 is a structural diagram of an ultrasound apparatus according to an exemplary embodiment, as shown in fig. 12, the ultrasound apparatus includes:

a communication interface 1 capable of information interaction with other devices such as network devices and the like;

and the processor 2 is connected with the communication interface 1 to realize information interaction with other equipment, and is used for executing the blood vessel lumen intima contour extraction method provided by one or more technical schemes when running a computer program. And the computer program is stored on the memory 3;

a display 4 for displaying an ultrasound image of an intravascular cross-section and an intraluminal contour in the ultrasound image.

Of course, in practice, the various components in the ultrasound device are coupled together by a bus system 5. It will be appreciated that the bus system 5 is used to enable connection communication between these components. The bus system 5 comprises, in addition to a data bus, a power bus, a control bus and a status signal bus. But for the sake of clarity the various buses are labeled as bus system 5 in figure 12.

The memory 3 in the embodiment of the present application is used to store various types of data to support the operation of the ultrasound apparatus. Examples of such data include: any computer program for operating on an ultrasound device.

It will be appreciated that the memory 3 may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 2 described in the embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.

The method disclosed in the above embodiment of the present application may be applied to the processor 2, or implemented by the processor 2. The processor 2 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 2. The processor 2 described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 2 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in the memory 3, and the processor 2 reads the program in the memory 3 and in combination with its hardware performs the steps of the aforementioned method.

When the processor 2 executes the program, the corresponding processes in the methods according to the embodiments of the present application are realized, and for brevity, are not described herein again.

In an exemplary embodiment, the present application further provides a storage medium, i.e. a computer storage medium, specifically a computer readable storage medium, for example, including a memory 3 storing a computer program, which can be executed by a processor 2 to implement the steps of the foregoing method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.

Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially implemented in the form of a software product, which is stored in a storage medium and includes several instructions to enable an ultrasound device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for extracting a contour of an intima of a blood vessel lumen is characterized by comprising the following steps:

adding the final endoluminal contour to the ultrasound image.

2. The method for extracting the intimal contour of a blood vessel according to claim 1, wherein the preprocessing the ultrasound image to obtain a target image comprises:

down-sampling and filtering the ultrasonic image;

3. The method for extracting the intimal contour of a blood vessel lumen according to claim 1 or 2, wherein the preprocessing the ultrasonic image to obtain a target image comprises:

catheter effects in the image are removed.

4. The method for extracting the contour of the endovascular lumen according to claim 3, wherein the removing the catheter effect in the image comprises:

calculating an average value of the distance between each target position and the central position of the image, and calculating the sum of the average value and a preset offset distance as a superposition distance;

5. The method for extracting the intimal contour of a blood vessel according to claim 1, wherein the preprocessing the ultrasound image to obtain a target image comprises:

6. The method for extracting the intimal contour of a blood vessel according to claim 1, wherein the preprocessing the ultrasound image to obtain a target image comprises:

7. The method for extracting the contour of the endovascular lumen according to claim 1, wherein the determining the lumen wall area in the target image comprises:

8. The method according to claim 7, wherein the determining the category of each pixel point in the target image to segment the target image into different cluster blocks comprises:

9. The method for extracting the intima contour of a blood vessel according to claim 8, wherein the fuzzy C-means clustering algorithm based on the gray histogram performs a clustering operation on the pixel points in the target image to segment the target image into different cluster blocks, and comprises:

10. The method according to claim 7, wherein the determining lumen wall regions in the cluster block comprises:

11. The method for extracting the contour of the endovascular lumen according to claim 7, wherein removing the external noise of the luminal wall region comprises:

12. The method for extracting the contour of the endovascular lumen according to claim 11, wherein removing the internal noise of the luminal wall region comprises:

13. The method for extracting the intimal contour of the blood vessel lumen according to claim 7, wherein the determining the initial intimal contour line corresponding to the lumen wall region includes:

performing polar coordinate conversion on the image without the external noise and the internal noise to obtain a second polar coordinate image, and taking pixel points of which the first gray value of each column is not zero in the first two-coordinate image as contour points;

14. The method for extracting the contour of the endovascular lumen according to claim 9, further comprising, after removing the external noise and the internal noise of the luminal wall region:

15. The method for extracting the intimal contour of the blood vessel lumen according to claim 1 or 2, wherein the fitting the initial intimal contour line comprises:

16. A blood vessel lumen intimal contour extraction device, comprising:

17. An ultrasound device, comprising:

a memory for storing a computer program;

a processor for implementing the steps of the vessel lumen intimal contour extraction method as claimed in any one of claims 1 to 15 when the computer program is executed;

18. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the vessel lumen intima contour extraction method as defined in any one of claims 1 to 15.