CN107204001B - Automatic midium segmentation method in carotid artery ultrasonic image - Google Patents

Automatic midium segmentation method in carotid artery ultrasonic image Download PDF

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CN107204001B
CN107204001B CN201610149681.7A CN201610149681A CN107204001B CN 107204001 B CN107204001 B CN 107204001B CN 201610149681 A CN201610149681 A CN 201610149681A CN 107204001 B CN107204001 B CN 107204001B
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李豪
张诗杰
马睿
陈惠人
奚水
张珏
方竞
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Feiyinuo Technology Co ltd
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    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
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Abstract

The invention relates to the field of image processing, and discloses an automatic intima-media segmentation method in a carotid artery ultrasonic image, which comprises the following steps: 1) inhibiting background information except for the intima-media by using a multi-scale Gaussian nuclear multiplication method to obtain an edge map with a prominent intima-media boundary; 2) acquiring partial contour line segments of the tunica media-adventitia boundary and the tunica intima-lumen boundary according to the edge map obtained in the step 2) by using an Otsu threshold method and an initial contour detection method of a Sobel operator; 3) designing a two-leg ant colony optimization algorithm on the basis of the initial contour obtained in the step 2), and connecting line segments of the initial contour to obtain a complete contour; 4) further optimizing the contour obtained in the step 3) by using a contour optimization algorithm based on a Snake model, so that the final contour is smoother, continuous and close to a real edge.

Description

Automatic midium segmentation method in carotid artery ultrasonic image
Technical Field
The invention relates to an automatic intima-media segmentation method in a carotid artery ultrasonic image, and belongs to the field of medical image processing.
Background
Cardiovascular disease is a leading disease that threatens human health. Atherosclerosis causes the thickening of the arterial vessel wall and the narrowing of the vessel cavity, thus being the main cause of cardiovascular diseases. The vessel wall is divided into an outer membranous layer, a middle membranous layer and an inner membranous layer which are mutually clung from inside to outside. The Intima-Media thickness (IMT) is defined as the distance from the luminal-Intima (LI) boundary to the Media-Adventitia (MA) boundary, i.e. the thickness of the Intima and Media portions is added. An increase in IMT is an early clinical symptom of atherosclerosis. Numerous studies have demonstrated that there is a significant correlation between carotid IMT and cardiovascular and cerebrovascular disease and that it can be a strong predictor of future cardiovascular events. Today, intima-media thickness in the carotid artery has been considered as an important indicator for preliminary discrimination between the degree of carotid atherosclerosis and the condition of cardiovascular pathologies.
The medical ultrasonic imaging technology can carry out clearer imaging on the carotid artery, and is an effective carotid atherosclerosis screening means. By means of the ultrasonic imaging technology, the carotid intima-media membrane can be imaged, and then the boundary of the intima-media membrane can be segmented, so that the measurement of IMT is realized. However, the method of acquiring the intima-media boundary by the manual segmentation by the doctor in clinical use is time-consuming and tedious, and there is a large difference between observers, so that there is an urgent need for an image segmentation method capable of automatically segmenting the intima-media boundary.
At present, a plurality of carotid intima-media segmentation algorithms exist, and more accurate intima-media segmentation can be realized. However, ultrasound images suffer from inherent speckle noise, low signal-to-noise ratio, and insufficient anatomical detail. Serious speckle noise can even cover the inner and middle membrane layers, and the success rate of the inner and middle membrane automatic segmentation method is influenced. Aiming at the problem, the invention provides an inner and middle membrane automatic segmentation method with better robustness based on algorithms such as ant colony optimization and the like.
An Ant Colony Optimization (ACO) is a bionic intelligent optimization algorithm developed based on the inspiration of ant colony crawling characteristics, and is characterized in that the overall optimal solution of the problem is determined jointly through the local optimality of bionic adaptive individuals. The algorithm has a self-learning function and a strong searching capability, has the advantages of parallelization, strong robustness and positive feedback, and is introduced into the field of image segmentation. For the intima-media segmentation, an ant colony algorithm can be adopted to convert the contour extraction problem of the MA and LI boundaries into an optimization problem for solving.
Disclosure of Invention
In order to replace the manual segmentation step, the invention provides an automatic segmentation method for a tunica media in an ultrasonic image. The invention provides a two-leg ant colony optimization algorithm according to a unique double-layer parallel interface structure of an intima-media structure, and realizes automatic segmentation of the intima-media by combining a multi-scale Gaussian kernel multiplication method, an edge detection operator and a Snake model.
The technical scheme of the invention is as follows:
an automatic division method for tunica media in an ultrasonic image comprises the following steps:
1) inhibiting background information except for the intima-media by using a multi-scale Gaussian nuclear phase multiplication method, and enhancing the intima-media boundary to obtain an edge map with a prominent intima-media boundary;
2) acquiring partial contour line segments of the LI and MA boundaries by using an initial contour detection method based on an Otsu threshold method and a Sobel operator according to the edge map obtained in the step 2);
3) designing a two-leg ACO algorithm on the basis of the initial contour obtained in the step 2), and connecting the initial contour line segments to obtain a complete contour;
4) further optimizing the contour obtained in the step 3) by using a contour optimization algorithm based on a Snake model, so that the final contour is smoother, continuous and close to a real edge.
And 1) suppressing background information except for the inner tunica media by using a multi-scale Gaussian nuclear multiplication method, highlighting the LI and MA boundaries of the inner tunica media, and obtaining an edge map. The edge map is defined as the product of the convolution results of the image with two filters having gaussian density kernels of different scales, and only the part with positive values is retained:
Figure BDA0000943030600000021
wherein the content of the first and second substances,
Figure BDA0000943030600000022
Figure BDA0000943030600000023
is a two-dimensional Gaussian function of small scale, sigma1The value is 1-3. And Gσ2(y) is a one-dimensional Gaussian function of large scale, σ2The value is 10-20.
Step 2) an initial contour detection method based on an Otsu threshold value method and a Sobel operator is used, and the method mainly comprises the following steps:
(1) based on the edge map obtained in the step 1), obtaining a binary image by using an Otsu threshold method;
(2) extracting the upper edge and the lower edge of the MA interface and the upper edge and the lower edge of the LI interface from the binary image by using a horizontal Sobel operator;
(3) deleting redundant edge lines and defective edge lines;
(4) and for the reserved part, taking the middle point of the upper edge line and the lower edge line as the final single edge line after each pair of edge lines are combined.
And 3) adopting a two-leg ACO algorithm to connect the initial contour line segments into a complete contour. It is characterized in that two ants are placed at the starting point of the intima-media membrane and climb from the left end to the right end of the blood vessel. One ant is fixed above the other ant, and then the two ants climb simultaneously, so that the ant can be regarded as two legs of one ant. In addition, the contour line segment obtained by the initial contour detection is defined as a forced path of the ant, when the ant crawls to the part, the ant is forced to crawl along the initial contour line segment, and gaps between the line segments are connected by the ACO algorithm. The method is characterized by comprising the following steps:
(1) equation of transition probability
Ants from manually selected Ps(xs,ys) The dots start, crawl from left to right, moving 1 abscissa to the right each time they are fixed. At time t, the process of moving the l (l ═ 1,2) th leg of the kth ant from pixel (x, i) to the neighboring pixel (x +1, j) is according to the lower transition probability equation:
Figure BDA0000943030600000031
wherein tau refers to pheromone, η refers to density of the pixel, α and β respectively determine relative influence of the pheromone and the heuristic information, wherein α takes the value of 1-3, and β takes the value of 3-5.
Figure BDA0000943030600000041
The set of positions where the ith leg representing the kth ant is allowed in the next crawl corresponds to the right neighborhood of pixel (x, i). It has to be noted that the neighborhood pixels cannot be shared by both legs at the same time. Thus, if the right neighborhoods of the two legs coincide, the coincident pixels will be driven from
Figure BDA0000943030600000042
And (4) excluding.
(2) Global update law
After all ants complete crawling, the pheromone alternates according to the following formula:
τ(x,i)(t+1)＝ρτ(x,i)(t)+Δτ(x,i)
wherein, tau(x,i)(t) is the number of pheromones at pixel (x, i) at the t-th iteration, τ(x,i)(t +1) is the number of pheromones at pixel (x, i) at the next iteration. Initial value of pheromone tau0The value is 0.1-1. Rho is an attenuation constant and is used for simulating the volatilization of pheromone, and the value is 0.5-1. Delta tau(x,i)Is the number of pheromones released in this iteration, and the calculation formula is as follows:
Figure BDA0000943030600000043
where Q is a constant and m is the number of ants. Q is 1, and m is 10-50. C (k) is a consumption function of the kth ant in the path searching process, which is defined as follows:
Figure BDA0000943030600000044
wherein IN(x,y)(0≤IN(xi,yi) ≦ 1) is the gray value of the pixel (x, i) after normalization. D (k) is antkManually selected end point P before the end point distance of the crawl pathe(xe,ye) The distance of (c). a is a penalty coefficient, the weight of the error distance D (k) in the consumption function is set, and the value is 1-2.
The above step 4) further contours using a contour optimization algorithm based on a Snake model, which contains a smoothing energy term (smoothing energy), an edge energy term (boundary energy) and a uniform energy term (uniform energy), and is implemented by minimizing the following energy functional:
Figure BDA0000943030600000045
wherein y is1(x) And y2(x) Representing the profile of the interface LI and MA, respectively, the parameter μ controls the weight of the smooth energy term and ν controls the weight of the uniform energy term. The uniform energy term links the two mutually independent profiles of LI and MA, keeping them at a uniform distance. Mu is 0.1-0.3, and v is 1-2.
The invention has the following advantages:
according to the invention, firstly, a multi-scale Gaussian kernel multiplication method is designed according to the characteristics of the carotid artery ultrasonic image, the image is preprocessed, background information except intima-media is inhibited, and the intima-media boundary is enhanced. Then, on the basis of the preprocessed image, a two-leg ant colony optimization algorithm is designed, and is combined with an Otsu threshold method, a Sobel edge detection operator, an ant colony optimization algorithm, a Snake model and other algorithms, so that automatic segmentation of the tunica media in the carotid artery ultrasonic image is achieved. The experimental result based on the clinical image shows that the method obtains an accurate segmentation result, and the error of the segmentation result is smaller than the error between observers of manual segmentation; meanwhile, the method achieves a success rate of 98.7% in clinical data testing, has good robustness, and can deal with images seriously polluted by speckle noise.
Drawings
FIG. 1 is a flow chart of the present invention for segmenting the intima-media of the carotid artery.
Fig. 2 is the result of each step in the initial contour detection process.
Fig. 3 is a typical example of the permitted location set of ant upper leg (upper leg) and ant lower leg (lower leg) in the ant colony optimization algorithm.
FIG. 4 is a diagram illustrating an example of the segmentation result according to the present invention.
Detailed Description
The present invention is further illustrated by the following examples to better understand the technical solutions of the present invention. The method comprises the following steps:
1. an edge map f (x, y) with prominent intima-media boundaries is obtained using a multi-scale gaussian kernel multiplication method. The f (x, y) calculation method is to multiply two filters with gaussian density kernels of different scales with the image convolution result and only retain the positive part of the value:
Figure BDA0000943030600000051
wherein the content of the first and second substances,
Figure BDA0000943030600000061
Figure BDA0000943030600000062
is a two-dimensional Gaussian function with small scale, in this embodiment, sigma1The value is 1.5. And Gσ2(y) is a one-dimensional Gaussian function of large scale, in this embodiment σ2The value is 15.
2. And acquiring partial initial contour line segments of the LI and MA boundaries by using an initial contour detection method based on an Otsu threshold method and a Sobel operator. As shown in fig. 2, the method comprises the following steps:
1) acquiring a binary image by using an Otsu threshold method based on the edge map (a) obtained in the previous step, as shown in a map (b);
2) extracting the upper edge and the lower edge of the MA interface and the upper edge and the lower edge of the LI interface from the binarization image (b) by using a horizontal Sobel operator; as shown in FIG. (c), the white line represents the upper edge and the gray line represents the lower edge.
3) After step 2), the ideal result should contain 2 pairs (4) of contours, corresponding to the upper and lower edges of the LI and MA interfaces, respectively. However, the edge lines obtained using the Sobel operator often contain redundant false edge lines or gaps. Therefore, more or less than 2 pairs of edge lines will be deleted as seen in the longitudinal direction, resulting in the result shown in (d).
4) Finally, for the remaining part, the midpoint of the upper edge line and the lower edge line is taken as the final single edge line after each pair of edge lines is merged, as shown in (e).
3. And (3) designing a two-leg ACO algorithm on the basis of the initial contour obtained in the step (2), and connecting the initial contour line segments to obtain a complete contour. The method comprises the following steps:
1) the pheromone matrix is initialized.
2) Placing a straight ant at an initial point P of manual selections(xs,ys) Nearby (upper leg placed 2 pixels above the point and lower leg placed 2 pixels below the point).
3) Ant from Ps(xs,ys) Starting to crawl to the right, moving 1 abscissa to the right every time fixedly, wherein the total steps of crawling are equal to xe-xs。
At time t, the process of moving the l (l ═ 1,2) th leg of the kth ant from pixel (x, i) to the neighboring pixel (x +1, j) is according to the lower transition probability equation:
Figure BDA0000943030600000071
where τ refers to the pheromone and η to the density of the pixel α and β determine the relative influence of the pheromone and heuristic information, respectively, in this example the values α -1 and β -4.
Figure BDA0000943030600000072
The set of positions where the ith leg representing the kth ant is allowed in the next crawl corresponds to the right neighborhood of pixel (x, i); however, if the right neighborhoods of the two legs coincide, the coincident pixels will be driven from
Figure BDA0000943030600000073
And (4) excluding. Therefore, the upper legs of the ants are always above the lower legs, and the final contours can be ensured not to be crossed or overlapped. FIG. 3 illustrates a typical neighborhood during crawling of two-leg ants
Figure BDA0000943030600000074
Ants are now crawling from x to x +1 position on the abscissa, and the light squares in the figure are the permitted position sets of the upper leg (upper leg) and the lower leg (lower leg) of the ants, and are excluded from the permitted position sets of the upper leg and the lower leg because the dark squares exist in the crawling permitted position sets of the upper leg and the lower leg at the same time.
4) And calculating the pheromone of the pixel point on the ant path.
5) And repeating the steps 2-4 until all the ants finish the crawling task.
6) And updating the pheromone matrix and calculating the volatilization amount of the pheromone.
The pheromone is replaced according to the following formula:
τ(x,i)(t+1)＝ρτ(x,i)(t)+Δτ(x,i)
wherein, tau(x,i)(t) is the number of pheromones at pixel (x, i) at the t-th iteration, τ(x,i)(t +1) is the number of pheromones at pixel (x, i) at the next iteration. In this embodiment, the initial value of the pheromone is τ00.5. ρ is an attenuation constant used to simulate the occurrence of pheromones, and ρ is 0.6 in this embodiment. Delta tau(x,i)Is the number of pheromones released in this iteration, and the calculation formula is as follows:
Figure BDA0000943030600000081
where Q is a constant and m is the number of ants. In the embodiment, Q is 1, and m is 20. C (k) is a consumption function of the kth ant in the path searching process, which is defined as follows:
Figure BDA0000943030600000082
wherein IN(x,y)(0≤IN(xi,yi) ≦ 1) is the gray value of the pixel (x, i) after normalization. D (k) is antkManually selected end point P before the end point distance of the crawl pathe(xe,ye) The distance of (c). a is a penalty coefficient, and a weight of the error distance d (k) in the consumption function is set, where a is 1 in this embodiment.
7) Repeating steps 2-6 for a number of iterations. The number of iterations is set to 10 in this embodiment.
8) And selecting the ant path with the minimum consumption equation as an optimal path, namely the outline of the interface of the LI and the MA.
4. The contours are further optimized using a contour optimization algorithm based on a Snake model, which is implemented by minimizing the energy functional:
Figure BDA0000943030600000083
wherein y is1(x) And y2(x) Representing the profile of the interface LI and MA, respectively, the parameter μ controls the weight of the smooth energy term and ν controls the weight of the uniform energy term. In this embodiment, μ ═ 0.1 and ν ═ 1.4 were selected.
Fig. 4 shows the segmentation results of CGACO on three different exemplary graphs. Fig. a is a very noisy image. The inner middle membrane is not clear due to noise pollution. As shown in fig. b, the exact segmentation of the intima-media is not affected by severe speckle noise. Fig. c is an image of a carotid artery with a curve, and fig. e is an image of a carotid artery with a typical significant thickening of the intima-media. The graph d and the graph f correspond to the segmentation results thereof, respectively.

Claims (6)

1. An automatic division method for tunica media in an ultrasonic image is characterized by comprising the following steps:
1) inhibiting background information except for the intima-media by using a multi-scale Gaussian nuclear phase multiplication method, and enhancing the edge of the intima-media to obtain an edge image with a prominent intima-media boundary;
2) acquiring partial contour line segments of the intima-lumen boundary and the intima-media boundary according to the edge map obtained in the step 1) by using an Otsu threshold method and an initial contour detection method based on a Sobel operator;
3) designing a two-leg ant colony optimization algorithm on the basis of the initial contour obtained in the step 2), connecting line segments of the initial contour to obtain a complete contour, wherein in the step 3), at the time t, the transfer probability equation according to which the process that the l-th leg of the kth ant moves from the pixel (x, i) to the adjacent pixel (x +1, j) is as follows:
Figure FDA0002222793150000011
wherein l is 1,2, tau is pheromone, η is the density of the pixel, α and β respectively determine the relative influence of the pheromone and the heuristic information, wherein α takes the value of 1-3, and β takes the value of 3-5;
Figure FDA0002222793150000012
the set of allowed positions for the ith leg representing the kth ant in the next crawling;
4) further optimizing the contour obtained in the step 3) by using a contour optimization algorithm based on a Snake model, so that the final contour is smoother, continuous and close to a real edge.
2. The method as claimed in claim 1, wherein in the step 1), the edge map is calculated by taking a product of a filter having two gaussian density kernels with different scales and a convolution result of the image and retaining only a positive portion of the value.
3. The method of claim 2, wherein two gaussian density kernel filters are used, one on a small scale and one on a large scale, and are defined as follows:
Figure FDA0002222793150000013
σ1the value is 1-3, sigma2The value is 10-20.
4. The method of claim 1, said step 2) of obtaining an initial contour, comprising the steps of:
1) acquiring a binary image by using an Otsu threshold value method; 2) extracting the upper edge and the lower edge of an intima-media interface and the upper edge and the lower edge of an intima-lumen interface from the binary image by using a horizontal Sobel operator; 3) deleting redundant edge lines and defective edge lines; 4) and for the reserved part, taking the middle point of the upper edge line and the lower edge line as the final single edge line after each pair of edge lines are combined.
5. The method as claimed in claim 1, wherein the location set for which the ith leg of the kth ant is allowed in the next crawling is set
Figure FDA0002222793150000021
Is the right neighborhood of pixel (x, i); however, if the right neighborhoods of the two legs are overlapped, the overlapped pixels will be driven from the right neighborhoods of the two legs
Figure FDA0002222793150000022
And (4) excluding.
6. The method of claim 1, wherein in step 4), the Snake model is implemented by minimizing an energy functional as follows:
Figure FDA0002222793150000023
wherein y is1(x) And y2(x) The profiles of the LI and MA interfaces, respectively; the parameter μ controls the weight of the smoothing energy term (smoothingenergy); ν controls the weight of uniform energy term (uniform energy); mu is 0.1-0.3; v is 1-2; f (x, y)i) An edge map is shown.
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CN108765432B (en) * 2018-05-07 2020-08-07 山东大学 Automatic carotid intima-media boundary segmentation method and system
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102163326A (en) * 2010-12-22 2011-08-24 武汉沃生科学技术研究中心有限公司 Method for automatic computerized segmentation and analysis on thickness uniformity of intima media of carotid artery blood wall in sonographic image
CN102800089A (en) * 2012-06-28 2012-11-28 华中科技大学 Main carotid artery blood vessel extraction and thickness measuring method based on neck ultrasound images
CN104599271A (en) * 2015-01-20 2015-05-06 中国科学院半导体研究所 CIE Lab color space based gray threshold segmentation method
CN105105741A (en) * 2015-07-15 2015-12-02 无锡海鹰电子医疗系统有限公司 Envelope line extracting and feature point tracking method of pulse wave image

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102163326A (en) * 2010-12-22 2011-08-24 武汉沃生科学技术研究中心有限公司 Method for automatic computerized segmentation and analysis on thickness uniformity of intima media of carotid artery blood wall in sonographic image
CN102800089A (en) * 2012-06-28 2012-11-28 华中科技大学 Main carotid artery blood vessel extraction and thickness measuring method based on neck ultrasound images
CN104599271A (en) * 2015-01-20 2015-05-06 中国科学院半导体研究所 CIE Lab color space based gray threshold segmentation method
CN105105741A (en) * 2015-07-15 2015-12-02 无锡海鹰电子医疗系统有限公司 Envelope line extracting and feature point tracking method of pulse wave image

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于蚁群聚类算法的动脉硬化无创检测;张丽娜等;《计算机工程与应用》;20160302(第7期);235-241页 *

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Patentee after: Feiyinuo Technology (Suzhou) Co.,Ltd.

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Address before: 215123 5F, building a, No. 27, Xinfa Road, Suzhou Industrial Park, Jiangsu Province

Patentee before: VINNO TECHNOLOGY (SUZHOU) Co.,Ltd.

Patentee before: Peking University