Method and device for tracking blood vessel plaque through medical color Doppler ultrasound equipment

Abstract

The invention provides a method and a device for tracking blood vessel plaques by medical color Doppler ultrasound equipment. The method comprises the following steps: screening corresponding sampling points in cache information according to a tracking instruction issued by a user, and respectively obtaining a small neighborhood matrix corresponding to each sampling point, searching related information corresponding to each small neighborhood matrix in the B-mode ultrasound image to establish a large neighborhood matrix corresponding to each sampling point, constructing a matching data matrix by using the large neighborhood matrixes, performing matching calculation on the matching data matrix and the small neighborhood matrixes to obtain a plurality of matching calculation results, and storing the matching calculation results in the B-mode ultrasound image. The plaque feature corresponding to each matching calculation result is recognized, and each plaque feature is marked in the corresponding B ultrasonic image to be displayed, the implementation is high, and the method can be simply and rapidly deployed in the medical color ultrasound equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical color Doppler ultrasound, and particularly relates to a method and device for a medical color Doppler ultrasound device to track vascular plaques. Background Art

[0002] In a medical color Doppler ultrasound device system, the detection of vascular plaques generally uses images such as grayscale and elasticity to show the characteristics of the plaques, so as to facilitate the observation and judgment of the plaques in blood vessels by users such as doctors. With the development of technology, in medical ultrasound devices, grayscale imaging is a common and widely used imaging category in different examination modes; in addition, characteristic imaging such as elasticity is also developing rapidly. Although such imaging categories are not as common and widely used as grayscale imaging, they also play a very important role in the identification and characteristic representation of plaques in arteries and veins. With the development of characteristic imaging such as elasticity, an imaging form has been formed in which colors are overlaid on grayscale images to show the characteristics of plaques at different positions. However, this imaging form also has a defect, that is, the overlaid colors cannot show the accurate positions of the plaques as clearly as grayscale. Especially for plaques in arterial blood vessels, they are affected by more factors such as vascular pulsation and respiratory vibration. And in common medical ultrasound devices, it is necessary to set several sampling points on the characteristic imaging to sample and collect data such as tension and elasticity, and it is much more difficult for users to directly sample and analyze the characteristic data at different positions of such plaques than to sample and analyze on conventional grayscale images. Manual operation is even more disadvantageous for users such as doctors to sample and evaluate the plaque characteristic indicators.

[0003] Therefore, the present invention provides a method and device for a medical color Doppler ultrasound device to track vascular plaques. Summary of the Invention

[0004] A method and device for a medical color Doppler ultrasound device to track vascular plaques according to the present invention propose a method that is easy to implement, real-time, and has high precision to obtain position information from a grayscale image, and a calculation method for accurately calculating plaque characteristics that is convenient for users to sample and judge, which can be conveniently and quickly deployed in a medical color Doppler ultrasound device system.

[0005] The present invention provides a method for a medical color Doppler ultrasound device to track vascular plaques, including:

[0006] Step 1: Screen corresponding sampling points in the cache information according to a tracking instruction issued by a user, and respectively obtain a small neighborhood matrix corresponding to each of the sampling points;

[0007] Step 2: Search for relevant information corresponding to each of the small neighborhood matrices in a B-mode ultrasound image respectively to establish a large neighborhood matrix corresponding to each of the sampling points;

[0008] Step 3: Construct a matching data matrix using the large neighborhood matrix, and perform matching calculations on the matching data matrix and the small neighborhood matrix to obtain a number of matching calculation results;

[0009] Step 4: Identify the plaque features corresponding to each matching calculation result, and mark each of the plaque features in the corresponding B-mode image for display.

[0010] In an implementable manner,

[0011] The said Step 1 includes:

[0012] Step 11: Screen a number of first-frame B-mode data from the cache information according to the tracking instruction issued by the user, identify the pixel points corresponding to each of the first-frame B-mode data in the B-mode image, and determine a number of sampling points;

[0013] Step 12: Use a preset small neighborhood matching template to screen a number of relevant data corresponding to each of the sampling points from the cache information, and establish a small neighborhood matrix corresponding to each of the sampling points.

[0014] In an implementable manner,

[0015] The said Step 2 includes:

[0016] Step 21: Turn on the tracking function according to the tracking instruction issued by the user, and control a preset probe to scan each of the small neighborhood matrices in the B-mode image to obtain a number of groups of matrix data corresponding to each of the small neighborhood matrices;

[0017] Step 22: Respectively obtain the first-frame B-mode data and the matrix data corresponding to each of the sampling points, and construct a large neighborhood matrix corresponding to each of the sampling points from the first-frame B-mode data and the matrix data.

[0018] In an implementable manner,

[0019] The said Step 3 includes:

[0020] Step 31: Generate matrix boundary information according to the large neighborhood matrix, and combine the matrix boundary information to generate a matching data matrix;

[0021] Step 32: Respectively perform boundary matching and fusion on each of the matching data matrices and the corresponding small neighborhood matrices to obtain corresponding fusion boundaries, calculate the boundary values corresponding to each of the fusion boundaries respectively, and generate a matching calculation result corresponding to each of the sampling points.

[0022] In an implementable manner,

[0023] The said Step 31 includes:

[0024] Step 311: Consider the position of each said sampling point as (i0, j0), consider the size of the small neighborhood matrix corresponding to each said sampling point as e*e, and consider the size of the large neighborhood matrix corresponding to each said sampling point as h*h;

[0025] Step 312: Calculate four groups of matrix boundary information of the said matching data matrix by using formula (1);

[0026]

[0027] Among them, Eup1 represents the upper matrix boundary information, Edown1 represents the lower matrix boundary information, Eleft1 represents the left matrix boundary information, and Eright1 represents the right matrix boundary information;

[0028] Step 312: Calculate the information restriction threshold corresponding to each said matrix boundary information respectively by using formula (2);

[0029]

[0030] Among them, Eup2 represents the upper information restriction threshold corresponding to the upper matrix boundary information, Edown2 represents the lower information restriction threshold corresponding to the lower matrix boundary information, Eleft2 represents the left information restriction threshold corresponding to the left matrix boundary information, and Eright2 represents the right information restriction threshold corresponding to the right matrix boundary information;

[0031] Step 313: Combine the said matrix boundary information within the said information restriction threshold to generate a matching data matrix.

[0032] In an implementable manner,

[0033] The said step 4 includes:

[0034] Step 41: Identify a number of current frame features included in the said B-ultrasound image, and respectively identify the plaque features corresponding to each said matching calculation result in the said B-ultrasound image;

[0035] Step 42: Respectively identify the plaque positions and feature values corresponding to each said plaque feature in the said small neighborhood matrix and the large neighborhood matrix, and mark and display them in the said B-ultrasound image.

[0036] In an implementable manner,

[0037] It further includes:

[0038] Respectively perform matching verification on each said matching calculation result to obtain the number of elements and the number of matching times included in each said matching calculation result;

[0039] When the number of elements in the same matching calculation result is inconsistent with the number of matches, re-match the matching calculation result.

[0040] In an implementable manner,

[0041] It further includes:

[0042] Determine a number of ROI patches of the user according to the tracking instruction issued by the user, and perform real-time tracking and static following on each ROI patch in the B-mode ultrasound image respectively, obtain real-time index information of each ROI patch or its sampling point and display it.

[0043] The present invention provides a device for a medical color Doppler ultrasound device to track vascular plaques, including:

[0044] A sampling and analysis module, configured to screen corresponding sampling points in the cached information according to the tracking instruction issued by the user, and respectively obtain a small neighborhood matrix corresponding to each sampling point;

[0045] A depth analysis module, configured to respectively find relevant information corresponding to each small neighborhood matrix in the B-mode ultrasound image to establish a large neighborhood matrix corresponding to each sampling point;

[0046] A matching execution module, configured to construct a matching data matrix by using the large neighborhood matrix, perform a matching calculation on the matching data matrix and the small neighborhood matrix, and obtain a number of matching calculation results;

[0047] A plaque recognition module, configured to recognize plaque characteristics corresponding to each matching calculation result, and respectively mark each plaque characteristic on the corresponding B-mode ultrasound image for display.

[0048] The achievable beneficial effects of the above technical solution are as follows: In a medical color Doppler ultrasound system, the detection of vascular, especially carotid artery plaque lesions, can be performed through examinations such as gray-scale imaging, strain imaging, and elastography. However, in the display of characteristics such as strain and elasticity, some auxiliary means are often required to achieve accurate positioning of vascular plaques. This patent proposes a calculation method that is easy to implement, real-time, and easy to accurately locate sampling points to obtain plaque characteristic data at points, and can be used for position tracking and data sampling of sampling points of vascular plaques. When the user manually measures, after placing the sampling point, the position of the point can be tracked in real time on the image, realizing the real-time refresh of the position of the dynamic point and ensuring the stability of the data within the sampling point. This patent is convenient and fast, and the data is processed by the backend and displayed on the screen.

[0049] To improve the accuracy of vascular plaque tracking and perform real-time dynamic tracking, sample points are screened from the cached information according to the tracking instructions issued by the user. By constructing a small neighborhood matrix and a large neighborhood matrix for each sample point, and further constructing a matching data matrix to perform matching calculations with the small neighborhood matrix, the plaque features presented by the matching calculation results are marked on the B-mode ultrasound image, completing the plaque tracking process, and constructing a plaque tracking method that is highly feasible and can be simply and quickly deployed in medical color ultrasound devices.

[0050] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification and the drawings.

[0051] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0052] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0053] Figure 1 It is a schematic working flow diagram of a method for a medical color ultrasound device to track vascular plaques in an embodiment of the present invention;

[0054] Figure 2 It is a schematic composition diagram of a device for a medical color ultrasound device to track vascular plaques in an embodiment of the present invention. Detailed Embodiments

[0055] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0056] Embodiment 1

[0057] This embodiment provides a method for a medical color ultrasound device to track vascular plaques, as Figure 1 shown, including:

[0058] Step 1: Screen corresponding sample points from the cached information according to the tracking instructions issued by the user, and respectively obtain the small neighborhood matrix corresponding to each of the sample points;

[0059] Step 2: Search for the relevant information corresponding to each of the small neighborhood matrices in the B-mode ultrasound image to establish the large neighborhood matrix corresponding to each of the sample points;

[0060] Step 3: Construct a matching data matrix using the large neighborhood matrix, and perform matching calculations between the matching data matrix and the small neighborhood matrix to obtain a number of matching calculation results;

[0061] Step 4: Identify the plaque features corresponding to each matching calculation result, and mark each of the plaque features in the corresponding B-mode ultrasound image for display.

[0062] In this example, the small neighborhood matrix represents a matrix with the sampling point as the central element;

[0063] In this example, the large neighborhood matrix represents a matrix with the small neighborhood matrix as the central element;

[0064] In this example, the plaque feature represents the feature presented by the plaque in the patient's blood vessel.

[0065] The working principle and beneficial effects of the above technical solution: In order to improve the accuracy of blood vessel plaque tracking and perform real-time dynamic tracking, sampling points are screened from the cached information according to the tracking instructions issued by the user. By constructing the small neighborhood matrix and the large neighborhood matrix of the sampling point, and further constructing a matching data matrix to perform matching calculations with the small neighborhood matrix, the plaque features presented by the matching calculation results are marked in the B-mode ultrasound image, completing the process of plaque tracking, and constructing a plaque tracking method that is highly feasible and can be simply and quickly deployed in medical color ultrasound equipment.

[0066] Example 2

[0067] Based on Example 1, for the method of a medical color ultrasound device for tracking blood vessel plaques, Step 1 includes:

[0068] Step 11: Screen a number of first-frame B-mode data from the cached information according to the tracking instructions issued by the user, identify the pixel points corresponding to each of the first-frame B-mode data in the B-mode ultrasound image, and determine a number of sampling points;

[0069] Step 12: Use a preset small neighborhood matching template to screen a number of relevant data corresponding to each sampling point from the cached information, and establish a small neighborhood matrix corresponding to each sampling point.

[0070] In this example, the first-frame B-mode data represents the starting point of the B-mode data included in the cached information;

[0071] In this example, the B-mode data represents the data presented by the B-mode ultrasound image;

[0072] In this example, the relevant data represents the data related to the sampling point.

[0073] Working principle and beneficial effects of the above technical solution: Filter the corresponding first-frame B-mode data in the cached information according to the user's instruction to determine the sampling points, and use the relevant data of the sampling points to construct a small neighborhood matrix for subsequent plaque tracking.

[0074] Embodiment 3

[0075] Based on Embodiment 1, in the method for a medical color ultrasound device to track vascular plaques, step 2 includes:

[0076] Step 21: Activate the tracking function according to the tracking instruction issued by the user, and control the preset probe to scan each of the small neighborhood matrices in the B-mode image to obtain several groups of matrix data corresponding to each of the small neighborhood matrices;

[0077] Step 22: Respectively obtain the first-frame B-mode data and matrix data corresponding to each of the sampling points, and construct a large neighborhood matrix corresponding to each of the sampling points with the first-frame B-mode data and the matrix data.

[0078] Working principle and beneficial effects of the above technical solution: When the user issues a tracking instruction, it indicates that the scanning mode is activated, several groups of matrix data of each small neighborhood matrix are obtained, and then the first-frame B-mode data and matrix data of each sampling point are combined to generate a large neighborhood matrix, which serves as the basis for subsequent plaque tracking.

[0079] Embodiment 4

[0080] Based on Embodiment 1, in the method for a medical color ultrasound device to track vascular plaques, step 3 includes:

[0081] Step 31: Generate matrix boundary information according to the large neighborhood matrix, and combine the matrix boundary information to generate a matching data matrix;

[0082] Step 32: Respectively perform boundary matching and fusion on each of the matching data matrices and the corresponding small neighborhood matrices to obtain corresponding fusion boundaries, and calculate the boundary values corresponding to each of the fusion boundaries respectively to generate a matching calculation result corresponding to each of the sampling points.

[0083] Working principle of the above technical solution: In order to improve the matching accuracy between matrices, the method of matrix boundary information fusion is adopted to match the matching data matrix and the small neighborhood matrix, and the corresponding matching calculation results are obtained, which serves as the basis for screening plaques.

[0084] Embodiment 5

[0085] Based on Embodiment 4, in the method for a medical color ultrasound device to track vascular plaques, step 31 includes:

[0086] Step 311: Consider the position of each said sampling point as (i0, j0), consider the size of the small neighborhood matrix corresponding to each said sampling point as e*e, and consider the size of the large neighborhood matrix corresponding to each said sampling point as h*h;

[0087] Step 312: Calculate four groups of matrix boundary information of the matching data matrix by using formula (1);

[0088]

[0089] Among them, Eup1 represents the upper matrix boundary information, Edown1 represents the lower matrix boundary information, Eleft1 represents the left matrix boundary information, and Eright1 represents the right matrix boundary information;

[0090] Step 312: Calculate the information restriction threshold corresponding to each said matrix boundary information respectively by using formula (2);

[0091]

[0092] Among them, Eup2 represents the upper information restriction threshold corresponding to the upper matrix boundary information, Edown2 represents the lower information restriction threshold corresponding to the lower matrix boundary information, Eleft2 represents the left information restriction threshold corresponding to the left matrix boundary information, and Eright2 represents the right information restriction threshold corresponding to the right matrix boundary information;

[0093] Step 313: Combine the matrix boundary information within the said information restriction threshold to generate a matching data matrix.

[0094] In this example, the process of generating the matching data matrix includes:

[0095] Combine the said matrix boundary information by using formula (3);

[0096] value1 = COR(m1, m2);

[0097] Among them, value1 is the matching result, the larger the value, the better the matching effect; m1 is the small neighborhood, m2 is the large neighborhood, COR mainly represents the calculation process of correlation, but does not exclude the calculation of other indicators such as complexity and consistency. After each matching occurs, select the coordinates of the point with the best current matching effect as the displacement result position of the corresponding point in the new frame of image.

[0098] The working principle and beneficial effects of the above technical solution: Use formulas to determine four groups of matrix boundary information and the information restriction threshold corresponding to each group of matrix boundary information, so as to combine the matrix boundaries to generate a matching data matrix. In this way, it is possible to avoid the disorder of boundary information and achieve precise fusion.

[0099] Example 6

[0100] Based on Example 1, for the method of a medical color Doppler ultrasound device for tracking vascular plaques, step 4 includes:

[0101] Step 41: Identify a number of current frame features included in the B-mode ultrasound image, and respectively identify the plaque features corresponding to each of the matching calculation results in the B-mode ultrasound image;

[0102] Step 42: Respectively identify the plaque positions and feature values corresponding to each of the plaque features in the small neighborhood matrix and the large neighborhood matrix, and mark and display them in the B-mode ultrasound image.

[0103] In this example, the current frame feature represents the feature presented by the contour included in the B-mode ultrasound image.

[0104] Working principle and beneficial effects of the above technical solution: By identifying the current frame features of the B-mode ultrasound image, using the matching calculation results to determine the corresponding plaque features, and then marking the corresponding plaque positions and plaque feature values in the B-mode ultrasound image, it provides a technical reference for users.

[0105] Example 7

[0106] Based on Example 4, the method of a medical color Doppler ultrasound device for tracking vascular plaques further includes:

[0107] Respectively perform matching verification on each of the matching calculation results to obtain the number of elements and the number of matches included in each of the matching calculation results;

[0108] When the number of elements in the same matching calculation result is inconsistent with the number of matches, re-match the matching calculation result.

[0109] Working principle and beneficial effects of the above technical solution: In order to reflect the rigor in the medical field and the sense of responsibility towards patients, verify the number of elements and the number of matches included in each matching calculation result. If the two are not balanced, re-match to ensure the accuracy of this B-mode ultrasound.

[0110] Example 8

[0111] Based on Example 1, the method of a medical color Doppler ultrasound device for tracking vascular plaques further includes:

[0112] Determine a number of ROI plaques of the user according to the tracking instruction issued by the user, and respectively perform real-time tracking and static following on each of the ROI plaques in the B-mode ultrasound image to obtain the real-time index information of each of the ROI plaques or their sampling points and display it.

[0113] Working principle and beneficial effects of the above technical solution: Tracking and following the ROI plaque in the B-ultrasound image according to the tracking instruction issued by the user, and supplying the real-time index information of each ROI plaque or its sampling point for the user to view, ensuring the stability of the data within the sampling point.

[0114] Example 9

[0115] This example provides a device for a medical color Doppler ultrasound device to track vascular plaques, as Figure 2 shown, including:

[0116] A sampling and analysis module, configured to screen corresponding sampling points in the cached information according to the tracking instruction issued by the user, and respectively obtain a small neighborhood matrix corresponding to each sampling point;

[0117] A depth analysis module, configured to respectively find relevant information corresponding to each small neighborhood matrix in the B-ultrasound image to establish a large neighborhood matrix corresponding to each sampling point;

[0118] A matching execution module, configured to construct a matching data matrix by using the large neighborhood matrix, perform matching calculation on the matching data matrix and the small neighborhood matrix, and obtain a plurality of matching calculation results;

[0119] A plaque recognition module, configured to recognize the plaque characteristics corresponding to each matching calculation result, and respectively mark each plaque characteristic on the corresponding B-ultrasound image for display.

[0120] In this example, the small neighborhood matrix represents a matrix with the sampling point as the central element;

[0121] In this example, the large neighborhood matrix represents a matrix with the small neighborhood matrix as the central element;

[0122] In this example, the plaque characteristics represent the characteristics presented by the plaques in the patient's blood vessels.

[0123] Working principle and beneficial effects of the above technical solution: In order to improve the accuracy of vascular plaque tracking and perform real-time dynamic tracking, sampling points are screened in the cached information according to the tracking instruction issued by the user. By constructing the small neighborhood matrix and large neighborhood matrix of the sampling point, and further constructing a matching data matrix to perform matching calculation with the small neighborhood matrix, the plaque characteristics presented by the matching calculation results are marked on the B-ultrasound image, completing the process of plaque tracking, and constructing a plaque tracking method that is highly feasible and can be simply and quickly deployed in medical color Doppler ultrasound devices.

[0124] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for a medical color Doppler ultrasound device to track vascular plaques, characterized in that, Including: Step 1: Screen corresponding sampling points from the cache information according to the tracking instruction issued by the user, and respectively obtain the small neighborhood matrices corresponding to each of the sampling points; Step 2: Search for the relevant information corresponding to each of the small neighborhood matrices in the B-mode image respectively to establish the large neighborhood matrix corresponding to each of the sampling points; Step 3: Use the large neighborhood matrix to construct a matching data matrix, and perform matching calculations on the matching data matrix and the small neighborhood matrix to obtain a number of matching calculation results; Step 4: Identify the plaque features corresponding to each matching calculation result, and respectively mark each of the plaque features on the corresponding B-mode image for display.

2. The method for tracking vascular plaques by a medical color Doppler ultrasound device according to claim 1, wherein, The said Step 1 includes: Step 11: Screen a number of first-frame B-mode data from the cache information according to the tracking instruction issued by the user, respectively identify the pixel points corresponding to each of the first-frame B-mode data in the B-mode image, and determine a number of sampling points; Step 12: Use a preset small neighborhood matching template to screen a number of relevant data corresponding to each of the sampling points from the cache information, and establish the small neighborhood matrix corresponding to each of the sampling points.

3. The method for a medical color Doppler ultrasound device to track vascular plaques according to claim 1, characterized in that, The said Step 2 includes: Step 21: Turn on the tracking function according to the tracking instruction issued by the user, and control the preset probe to scan each of the small neighborhood matrices in the B-mode image respectively to obtain a number of groups of matrix data corresponding to each of the small neighborhood matrices; Step 22: Respectively obtain the first-frame B-mode data and the matrix data corresponding to each of the sampling points, and construct the large neighborhood matrix corresponding to each of the sampling points with the first-frame B-mode data and the matrix data.

4. The method for tracking vascular plaques by a medical color Doppler ultrasound device according to claim 1, characterized in that, The said Step 3 includes: Step 31: Generate matrix boundary information according to the large neighborhood matrix, and combine the matrix boundary information to generate a matching data matrix; Step 32: Respectively perform boundary matching and fusion on each of the matching data matrices and the corresponding small neighborhood matrices to obtain the corresponding fusion boundaries, respectively calculate the boundary values corresponding to each of the fusion boundaries, and generate the matching calculation results corresponding to each of the sampling points.

5. The method for a medical color Doppler ultrasound device to track vascular plaques according to claim 4, characterized in that, The said Step 31 includes: Step 311: Consider the position of each of the sampling points as (i0, j0), consider the size of the small neighborhood matrix corresponding to each of the sampling points as e*e, and consider the size of the large neighborhood matrix corresponding to each of the sampling points as h*h; Step 312: Calculate four groups of matrix boundary information of the matching data matrix using formula (1); where, Eup1 represents the upper matrix boundary information, Edown1 represents the lower matrix boundary information, Elft1 represents the left matrix boundary information, and Eright1 represents the right matrix boundary information; Step 312: Calculate the information limit threshold corresponding to each of the matrix boundary information respectively using formula (2); where, Eup2 represents the upper information limit threshold corresponding to the upper matrix boundary information, Edown2 represents the lower information limit threshold corresponding to the lower matrix boundary information, Eleft2 represents the left information limit threshold corresponding to the left matrix boundary information, and Eright2 represents the right information limit threshold corresponding to the right matrix boundary information; Step 313: Combine the matrix boundary information within the information limit threshold to generate a matching data matrix.

6. The method for a medical color Doppler ultrasound device to track blood vessel plaques according to claim 1, characterized in that, The said step 4 includes: Step 41: Identify a number of current frame features included in the B-ultrasound image, and respectively identify the plaque features corresponding to each of the matching calculation results in the B-ultrasound image; Step 42: Respectively identify the plaque positions and eigenvalues corresponding to each of the plaque features in the small neighborhood matrix and the large neighborhood matrix, and mark and display them in the B-ultrasound image.

7. The method for a medical color Doppler ultrasound device to track blood vessel plaques according to claim 4, characterized in that, It also includes: Respectively perform matching verification on each of the matching calculation results to obtain the number of elements and the number of matches included in each of the matching calculation results; When the number of elements in the same matching calculation result is inconsistent with the number of matches, re-match the matching calculation result.

8. The method for a medical color Doppler ultrasound device to track vascular plaques according to claim 1, characterized in that, It also includes: Determine a number of ROI plaques of the user according to the tracking instruction issued by the user, and respectively perform real-time tracking and static following on each of the ROI plaques in the B-ultrasound image, and obtain and display the real-time index information of each of the ROI plaques.

9. A device for tracking vascular plaques in a medical color Doppler ultrasound device, characterized in that, It includes: A sampling analysis module, configured to screen corresponding sampling points in the cached information according to the tracking instruction issued by the user, and respectively obtain the small neighborhood matrix corresponding to each of the sampling points; A depth analysis module, configured to respectively find the relevant information corresponding to each of the small neighborhood matrices in the B-ultrasound image to establish the large neighborhood matrix corresponding to each of the sampling points; A matching execution module, configured to construct a matching data matrix by using the large neighborhood matrix, perform matching calculation on the matching data matrix and the small neighborhood matrix to obtain a number of matching calculation results; A plaque recognition module, configured to identify the plaque features corresponding to each matching calculation result, and respectively mark and display each of the plaque features in the corresponding B-ultrasound image.

Images