WO2018058697A1 - 血管的内中膜自动识别测量方法及超声仪 - Google Patents

血管的内中膜自动识别测量方法及超声仪 Download PDF

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Publication number
WO2018058697A1
WO2018058697A1 PCT/CN2016/101493 CN2016101493W WO2018058697A1 WO 2018058697 A1 WO2018058697 A1 WO 2018058697A1 CN 2016101493 W CN2016101493 W CN 2016101493W WO 2018058697 A1 WO2018058697 A1 WO 2018058697A1
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blood vessel
edge
posterior wall
target
position coordinates
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PCT/CN2016/101493
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English (en)
French (fr)
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姚斌
黄灿
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深圳华声医疗技术有限公司
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Publication of WO2018058697A1 publication Critical patent/WO2018058697A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0891—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
    • A61B8/467—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means
    • A61B8/469—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means for selection of a region of interest
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques

Definitions

  • the invention relates to the technical field of measurement, in particular to a method for automatically identifying and measuring an inner film of a blood vessel and an ultrasound system.
  • IMT carotid intima-media thickness
  • intima-media thickness which is the sum of intima-media thickness and median thickness
  • myocardial infarction myocardial infarction
  • IMT measurement technology is currently widely used in medical ultrasound practice of carotid artery diagnosis.
  • the measurement of IMT is mainly through pattern recognition of the image of conventional B-mode ultrasound, find the most likely carotid intima and display it, and then calculate the corresponding measurement parameters (such as intima thickness IMT, blood vessel diameter and blood vessels). Outer diameter value, etc.). Due to the actual carotid artery image, different people have different image characteristics. In the prior art, after the B-mode image of the carotid artery is frozen, the user can freely select a ROI frame (ROI, region). Of Interest, the region of interest), then initiate the pre-set IMT algorithm to automatically find the carotid intima suspects within the ROI box and calculate the corresponding measurements.
  • ROI ROI frame
  • the process of the user freely selecting the ROI box always requires the user to perform many manual operations, such as moving the position of the ROI box, and enlarging or reducing the size of the ROI box, that is, the process of selecting the ROI box requires the user to repeatedly operate the keyboard and control the mouse.
  • the trackball is more complicated to operate, resulting in a lower rate of diagnosis.
  • the main object of the present invention is to provide a method for measuring the inner media of the blood vessel, which is intended to simplify the operation process and improve the diagnosis efficiency.
  • the present invention provides a method for automatically identifying an inner membrane in a blood vessel, and the method for automatically identifying an intravascular film measurement includes the following steps:
  • the position of the intima and media of the posterior wall of the target vessel in the ROI frame is identified, and the intima-media thickness IMT of the posterior wall of the target vessel is measured and displayed.
  • the blood vessel edge candidate points for acquiring blood vessels on each echo line on the B-mode image are specifically:
  • the blood vessel edge candidate point includes an upper blood vessel edge candidate point and a lower blood vessel edge candidate point.
  • the determining, according to a preset blood vessel edge recognition rule, a set of upper and lower blood vessel edges corresponding to each echo line in each of the blood vessel edge candidate points on each of the echo lines comprises:
  • the weighting coefficient table including a depth weight coefficient corresponding to the blood vessel depth and a width weight coefficient corresponding to the blood vessel width;
  • each echo line corresponding to each of the blood vessel edge candidate points on each of the echo lines according to a corresponding weight coefficient in the weighting coefficient table and a gray level gradient corresponding to different positions on the echo line
  • the uppermost set of blood vessels and the lower edge of the blood vessel with the highest weight are used as the upper edge of the blood vessel and the lower edge of the blood vessel on the corresponding echo line.
  • the position coordinates of the upper edge of the blood vessel and the lower blood vessel edge of each group are acquired, and the position coordinates of the upper edge of the blood vessel and the lower blood vessel edge of each group are sorted according to a preset depth direction, and each of the The position coordinates are clustered to determine the posterior wall of the target vessel including:
  • a longest continuous wall of the blood vessel is obtained, and the continuous posterior wall of the longest blood vessel is used as the posterior wall of the target blood vessel.
  • the predetermined depth direction is a direction from shallow to deep.
  • the present invention also provides an ultrasound system, the ultrasound system comprising:
  • An acquisition module configured to acquire a blood vessel edge candidate point of a blood vessel on each echo line on the B-mode image
  • An analysis module determining, according to a preset blood vessel edge recognition rule, a set of upper blood vessel edges and a lower blood vessel edge corresponding to each echo line in each of the blood vessel edge candidate points on each of the echo lines;
  • Processing module acquiring position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel of each group, and sorting the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel according to a preset depth direction, and the coordinates of each position Perform clustering to determine the posterior wall of the target vessel;
  • Determining a module determining a size and a position of a ROI frame in the current B-mode image according to a length of the target blood vessel rear wall and a preset height;
  • Intima recognition module for identifying the position of the intima and media of the posterior wall of the target vessel in the ROI frame;
  • Measurement module for measuring and displaying the intima-media thickness IMT of the posterior wall of the target vessel.
  • the blood vessel edge candidate points of the blood vessel are acquired on each echo line on the B-mode image according to the sorting result of the gray level gradients at different positions on the echo line; the blood vessel edge candidate points include the upper edge of the blood vessel and the blood vessel Edge candidate points.
  • the analysis module comprises:
  • a weighting coefficient table establishing unit configured to establish a weighting coefficient table corresponding to a blood vessel depth and a blood vessel width, the weighting coefficient table including a depth weight coefficient corresponding to a blood vessel depth and a width weight coefficient corresponding to a blood vessel width;
  • a blood vessel edge determining unit configured to respectively determine, according to a corresponding weight coefficient in the weighting coefficient table and a gray level gradient corresponding to different positions on the echo line, in the blood vessel edge candidate points on each of the echo lines
  • Each echo line corresponds to the highest set of upper and lower blood vessel edges, and serves as the upper and lower blood vessel edges on the corresponding echo line.
  • the determining module is specifically configured to:
  • the predetermined depth direction is a direction from shallow to deep.
  • the invention provides a method for automatically identifying and detecting an inner membrane in a blood vessel, wherein the method for automatically identifying the inner membrane of the blood vessel comprises the steps of: acquiring a blood vessel edge candidate point on each echo line on the B-mode image; a blood vessel edge recognition rule, wherein a set of upper blood vessel edges and a lower blood vessel edge corresponding to each echo line are respectively determined in the blood vessel edge candidate points on each of the echo lines; and the upper edge of the blood vessel is obtained for each group Position coordinates of the lower edge of the blood vessel, sorting the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel according to a preset depth direction, and clustering the coordinates of the respective positions to determine the posterior wall of the target blood vessel; Determining the length and position of the ROI frame in the current B-mode image, and determining the position of the intima and media of the posterior wall of the target vessel in the ROI frame, and The intima-media thickness IMT of the posterior wall of the target vessel is measured and displayed.
  • the method for automatically detecting the inner media of the blood vessel of the present invention can automatically determine the size and position of the ROI frame in the current B-mode image and automatically recognize the position of the inner membrane of the blood vessel in the ROI frame without requiring the user to manually To determine the size and position of the ROI box in the current B-mode image, which simplifies the operation process and improves the efficiency of disease diagnosis.
  • FIG. 1 is a schematic flow chart of a first embodiment of an inner-inner film automatic identification measuring method for a blood vessel according to the present invention
  • FIG. 2 is a schematic diagram of functional modules of a first embodiment of the ultrasound apparatus of the present invention.
  • FIG. 3 is a schematic diagram of a refinement function module of an analysis module in a second embodiment of the ultrasound apparatus of the present invention.
  • the invention provides a method for measuring the inner film automatic identification of blood vessels.
  • the method for automatically detecting the inner film of the blood vessel comprises the following steps:
  • Step S10 acquiring a blood vessel edge candidate point of the blood vessel on each echo line on the B-mode image
  • the method for automatically identifying the inner membrane in blood vessels is mainly applied to the practice of medical ultrasound, and is used for the recognition and diagnosis of B-mode images of cardiovascular diseases and related diseases, so as to inform the patient in advance about the degree of risk of occurrence of related diseases.
  • the intima-media thickness IMT of the carotid artery of the human body has a significant correlation with many cardiovascular and cerebrovascular diseases such as myocardial infarction. Therefore, the patient can be informed of the myocardial in advance according to the measurement of the intima-media thickness IMT of the carotid artery.
  • the degree of risk of infarction the method for automatically detecting the inner film of the blood vessel according to the embodiment of the present invention will be described in detail by taking the automatic identification measurement of the intima of the carotid artery as an example.
  • the carotid artery in the human body must have a certain depth range and a certain width range on the B-ultrasound image, and the depth range and the width range cannot be arbitrarily changed. Therefore, the carotid artery is in B.
  • the possibility of different positions of the super image is large and small, presenting a form of distribution probability, some positions are highly likely to be the edge of the blood vessel (ie, the blood vessel wall), and some are low in the possibility of the edge of the blood vessel, therefore, Both the depth of the carotid artery and the width of the carotid artery can be used to identify the weighting coefficients of the carotid artery image.
  • the display position of the blood vessel edge of the carotid artery also has a high gray scale gradient on the B-ultrasound image; and the blood flow itself has a low echo characteristic, and therefore, the neck
  • the blood flow in the arterial blood vessels will have a distinct bright-dark difference on the B-ultrasound image, that is, the gray gradient of the blood vessel edge of the carotid artery is also one of the main reference factors for identifying the blood vessel image on the B-ultrasound image.
  • the method for automatically identifying the inner media of the blood vessel is to first obtain a blood vessel edge candidate point of each blood vessel on each echo line on the B-mode image.
  • the method for automatically identifying the inner film in the blood vessel of the embodiment of the present invention firstly obtains a blood vessel edge candidate point on each echo line on the B-mode image according to the sorting result of the gray level gradient at different positions on the echo line.
  • the blood vessel edge candidate points include the upper blood vessel edge candidate point and the lower blood vessel edge candidate point as the blood vessel edge candidate points of the blood vessel.
  • the obtaining of the blood vessel edge candidate point in the embodiment is a candidate point for finding a blood vessel edge of the artery of the arteries in a shallow to deep direction along the depth direction of each echo line on the B-mode image, too shallow
  • the points are first discarded, and then the points on the echo lines of the B-mode image are respectively found as the points where the gray level gradient is the extreme value, and the point where the gray level gradient on the echo line is the extreme value is taken as the blood vessel edge of the echo line.
  • a candidate point, the blood vessel edge candidate point includes an upper blood vessel edge candidate point and a lower blood vessel edge candidate point.
  • Step S20 determining, according to a preset blood vessel edge recognition rule, a set of upper blood vessel edges and a lower blood vessel edge corresponding to each echo line in each of the blood vessel edge candidate points on each of the echo lines;
  • the method for automatically identifying the endocardium of the blood vessel acquires the candidate points of the blood vessel edge of the carotid artery on each echo line on the B-mode image, and according to the preset rule of the blood vessel edge, in each place A set of upper and lower blood vessel edges corresponding to each echo line are respectively determined in the blood vessel edge candidate points on the echo line.
  • the weighting coefficient table includes A depth weight coefficient corresponding to the blood vessel depth and a width weight coefficient corresponding to the blood vessel width.
  • the weight coefficient by taking the depth weight coefficient as an example: as the carotid artery of the main artery of the human body, the depth of the blood vessel has a normal position.
  • two adjustable boundary empirical parameter values that is, the minimum depth parameter can be set.
  • the value and the maximum depth parameter value, and then using the two boundary empirical parameter values, can be programmed to generate a multi-parameter adjustable smooth gradient curve, so that different depth values can be mapped to different weight coefficients.
  • the corresponding weight coefficient in the weighting coefficient table and the gray level gradient corresponding to different positions on the echo line may be used on each of the echo lines.
  • the upper edge of the blood vessel and the lower edge of the blood vessel with the highest weight corresponding to each echo line are respectively determined in the candidate points of the blood vessel edge, and are taken as the upper edge of the blood vessel and the lower edge of the blood vessel on the corresponding echo line.
  • Step S30 acquiring position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel of each group, and sorting the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel according to a preset depth direction, and the coordinates of each position Perform clustering to determine the posterior wall of the target vessel;
  • the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel are respectively acquired, and then according to a preset depth.
  • the direction sorts the position coordinates of the upper and lower blood vessel edges of each group, and clusters the position coordinates (where the sorting and clustering are basic algorithms in image recognition technology, where No further details are made, so that a longest continuous wall of blood vessels is obtained, and the continuous wall of the longest blood vessel is used as the posterior wall of the target blood vessel.
  • the preset depth direction is a direction from shallow to deep.
  • Step S40 determining the size and position of the ROI frame in the current B-mode image according to the length of the target blood vessel rear wall and the preset height;
  • the position of the rear wall of the target blood vessel is used as a reference position of the ROI frame, and the length of the rear wall of the target blood vessel is used as a reference width of the ROI frame, and the ROI is further
  • the height of the frame can be set according to a preset optimal height value, so that the parameter coordinates of the ROI frame can be obtained, thereby determining the size and position of the ROI frame.
  • the ROI box may be manually moved according to the user's viewing requirements, by moving the location.
  • the ROI box is used to specify the area of the image that you want to view.
  • Step S50 identifying the positions of the intima and media of the posterior wall of the target vessel in the ROI frame, and measuring and displaying the intima-media thickness IMT of the posterior wall of the target vessel.
  • identifying the positions of the intima and media of the posterior wall of the target blood vessel in the ROI frame is a basic application of the conventional ultrasound IMT recognition technology, and belongs to conventional techniques.
  • the energy value of each pixel in the vicinity of the rear wall of the target blood vessel can be calculated according to a preset energy function in the posterior wall of the target blood vessel of the ROI frame to find the pole.
  • the matching point of the value can obtain the specific position of the intima and media of the posterior wall of the target vessel on the image.
  • measuring and displaying the intima-media thickness IMT of the posterior wall of the target vessel is also a conventional technique, and will not be described herein.
  • the method for automatically detecting an intima of the blood vessel comprises the steps of: acquiring a blood vessel edge candidate point of a blood vessel on each echo line on the B-mode image; according to a preset blood vessel edge recognition rule, Determining a set of upper and lower blood vessel edges corresponding to each echo line in the blood vessel edge candidate points on the echo line; obtaining position coordinates of the upper edge of the blood vessel and the lower blood vessel edge of each group, according to a preset The depth direction of each group sorts the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel, and clusters the coordinates of each position to determine the posterior wall of the target vessel; according to the length of the posterior wall of the target vessel and the preset Height, determining the size and position of the ROI frame in the current B-mode image; identifying the location of the intima and media of the posterior wall of the target vessel in the ROI frame, and measuring and displaying the posterior wall of the target vessel Intima thickness IMT.
  • the method for automatically detecting the inner media of the blood vessel of the present invention can automatically determine the size and position of the ROI frame in the current B-mode image and automatically recognize the position of the inner membrane of the blood vessel in the ROI frame without requiring the user to manually To determine the size and position of the ROI box in the current B-mode image, which simplifies the operation process and improves the efficiency of disease diagnosis.
  • the present invention also provides an ultrasound apparatus.
  • the ultrasound apparatus 100 provided by the present invention includes an acquisition module 101, an analysis module 102, a processing module 103, a determination module 104, and a measurement module 105.
  • the acquiring module 101 is configured to acquire a blood vessel edge candidate point of a blood vessel on each echo line on the B-mode image;
  • the ultrasound system provided by the embodiment of the invention is applied to the practice of medical ultrasound, and is used for the recognition and diagnosis of B-ultrasound images of cardiovascular diseases and related diseases, so as to inform the patient in advance about the degree of risk of the occurrence of the relevant diseases.
  • the intima-media thickness IMT of the carotid artery of the human body has a significant correlation with many cardiovascular and cerebrovascular diseases such as myocardial infarction, so the intima-media thickness IMT of the human carotid artery can be measured by an ultrasound system, according to the measurement.
  • the ultrasound system of the embodiment of the present invention will be described in detail by taking an automatic identification measurement of the intima of the carotid artery as an example.
  • the carotid artery in the human body must have a certain depth range and a certain width range on the B-ultrasound image, and the depth range and the width range cannot be arbitrarily changed. Therefore, the carotid artery is in B.
  • the possibility of different positions of the super image is large and small, presenting a form of distribution probability, some positions are highly likely to be the edge of the blood vessel (ie, the blood vessel wall), and some are low in the possibility of the edge of the blood vessel, therefore, Both the depth of the carotid artery and the width of the carotid artery can be used to identify the weighting coefficients of the carotid artery image.
  • the display position of the blood vessel edge of the carotid artery also has a high gray scale gradient on the B-ultrasound image; and the blood flow itself has a low echo characteristic, and therefore, the neck
  • the blood flow in the arterial blood vessels will have a distinct bright-dark difference on the B-ultrasound image, that is, the gray gradient of the blood vessel edge of the carotid artery is also one of the main reference factors for identifying the blood vessel image on the B-ultrasound image.
  • the acquisition module 101 acquires a blood vessel edge candidate point of a blood vessel on each echo line on the B-mode image. Specifically, in the embodiment of the present invention, the acquiring module 101 acquires a blood vessel edge candidate point on each echo line on the B-mode image according to the sorting result of the gray level gradient at different positions on the echo line, with the largest gradient.
  • the blood vessel edge candidate points include an upper blood vessel edge candidate point and a lower blood vessel edge candidate point.
  • the acquiring module 101 acquires the blood vessel edge candidate point by searching for the blood vessel edge of the artery of the artery from the shallow to deep direction along the depth direction of each echo line on the B-mode image. For candidate points, the points that are too shallow are discarded first, and then the points on the echo lines of the B-mode image are respectively found as the points where the gray gradient is extreme, and the points on the echo line whose gray level is the extreme value are taken as A blood vessel edge candidate point of the echo line, the blood vessel edge candidate point including an upper blood vessel edge candidate point and a lower blood vessel edge candidate point.
  • the analyzing module 102 is configured to determine, according to a preset blood vessel edge recognition rule, a set of upper blood vessels and blood vessels corresponding to each echo line in each of the blood vessel edge candidate points on each of the echo lines edge;
  • the analyzing module 102 is configured according to a preset blood vessel edge recognition rule. A set of upper and lower blood vessel edges corresponding to each echo line is determined in each of the blood vessel edge candidate points on each of the echo lines.
  • the processing module 103 is configured to acquire position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel of each group, and sort the position coordinates of the upper edge of the blood vessel and the lower edge of the blood vessel according to a preset depth direction, and Each of the position coordinates is clustered to determine a posterior wall of the target vessel;
  • the processing module 103 acquires the upper edge of the blood vessel and the blood vessel under each group. Position coordinates of the edge, and then sorting the position coordinates of the upper and lower blood vessel edges of each group according to a preset depth direction, and clustering the position coordinates (wherein the sorting and clustering are both It is a basic algorithm in image recognition technology, which will not be described here, so as to obtain a longest continuous wall of blood vessels, and the continuous wall of the longest blood vessel is used as the posterior wall of the target blood vessel.
  • the preset depth direction is a direction from shallow to deep.
  • the determining module 104 is configured to determine a size and a position of a ROI frame in the current B-mode image according to a length of the target blood vessel rear wall and a preset height;
  • the determining module 104 uses the position of the rear wall of the target blood vessel as the reference position of the ROI frame, and the length of the rear wall of the target blood vessel as the reference width of the ROI frame.
  • the height of the ROI frame may be set according to an optimal height value set in advance, so that the parameter coordinates of the ROI frame may be obtained, thereby determining the size and position of the ROI frame. That is, in the embodiment, the determining module 104 can automatically determine the size and position of the ROI frame in the current B-mode image, instead of requiring the user to manually move the position of the ROI frame as mentioned in the prior art. It also enlarges or reduces the size of the ROI box, which simplifies the operation process and improves the efficiency of disease diagnosis.
  • the inner media membrane recognition module 105 is configured to identify a position of an intima and a medial membrane of a posterior wall of the target blood vessel in the ROI frame;
  • the inner media recognition module 105 identifies the position of the intima and media of the posterior wall of the target blood vessel in the ROI frame, which is the basic of the conventional ultrasound IMT recognition technology.
  • Application which belongs to the conventional technology, can be implemented in various ways.
  • the inner media recognition module 105 can calculate the energy function according to a preset energy function in the posterior wall of the target blood vessel of the ROI frame. The energy value of each pixel near the posterior wall of the target vessel is found to have a matching point with an extreme value, and the specific position of the intima and media of the posterior wall of the target vessel on the image can be obtained.
  • the ROI box may be manually moved according to the user's viewing requirements, by moving the location.
  • the ROI box is used to specify the area of the image that you want to view.
  • the measuring module 106 is configured to measure and display an intima-media thickness IMT of the posterior wall of the target blood vessel.
  • the measurement and display of the inner film thickness IMT of the posterior wall of the target blood vessel by the measuring module 106 is also a conventional conventional technique, and details are not described herein again.
  • the analysis module 102 further includes a weighting coefficient table establishing unit 1021 and a blood vessel edge determining unit 1022.
  • the weighting coefficient table establishing unit 1021 is configured to establish a weighting coefficient table corresponding to the blood vessel depth and the blood vessel width, wherein the weighting coefficient table includes a depth weight coefficient corresponding to the blood vessel depth and corresponds to the blood vessel width. Width weight coefficient;
  • a coefficient table including a depth weight coefficient corresponding to a blood vessel depth and a width weight coefficient corresponding to a blood vessel width is necessary to establish a weighting corresponding to the blood vessel depth and the blood vessel width.
  • two adjustable boundary empirical parameter values that is, the minimum depth parameter can be set.
  • the value and the maximum depth parameter value, and then using the two boundary empirical parameter values can be programmed to generate a multi-parameter adjustable smooth gradient curve, so that different depth values can be mapped to different weight coefficients.
  • the blood vessel edge determining unit 1022 the blood vessel edge candidate on each of the echo lines according to a corresponding weight coefficient in the weighting coefficient table and a gray level gradient corresponding to different positions on the echo line Determine the upper set of upper and lower blood vessels of each echo line corresponding to each echo line and use them as the upper and lower blood vessels of the corresponding echo line.
  • the blood vessel edge determining unit 1022 may be configured according to corresponding weight coefficients in the weighting coefficient table and different positions on the echo line. a gray level gradient, wherein each of the blood vessel edge candidate points on each of the echo lines respectively determines a set of upper blood vessel edges and lower blood vessel edges corresponding to each of the echo lines, and uses the corresponding echo line as a corresponding echo line Upper blood vessel edge and lower blood vessel edge.
  • the ultrasound system comprises an acquisition module, an analysis module, a processing module, a determination module and a measurement module.
  • the acquiring module is configured to acquire a blood vessel edge candidate point of a blood vessel on each echo line on the B-mode image; and the analyzing module: according to a preset blood vessel edge recognition rule, on each of the echo lines Determining a set of upper and lower blood vessel edges corresponding to each echo line respectively in the blood vessel edge candidate points;
  • the processing module : acquiring position coordinates of the upper edge of the blood vessel and the lower blood vessel edge of each group, according to a preset Sorting the position coordinates of the upper and lower blood vessel edges of each group in the depth direction, and clustering the position coordinates to determine the posterior wall of the target blood vessel;
  • the determining module according to the posterior wall of the target blood vessel a length and a preset height, determining a size and a position of a ROI frame in the current B-mode image;
  • the inner film recognition module for positioning the intima and media of the posterior wall
  • the ultrasound apparatus of the present invention can automatically determine the size and position of the ROI frame in the current B-mode image and automatically recognize the position of the inner membrane of the blood vessel within the ROI frame without requiring the user to manually determine the ROI in the current B-mode image.
  • the size and position of the frame simplifies the operation process and improves the efficiency of disease diagnosis.

Abstract

一种血管的内中膜自动识别测量方法以及超声仪，包括：在B超图像上的各回波线上获取血管的血管边缘候选点；根据预设的血管边缘识别规则，在各回波线上的血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；获取各组血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组血管上边缘和血管下边缘的位置坐标进行排序，并对各位置坐标进行聚类，确定目标血管后壁；根据目标血管后壁的长度及预设的高度，确定ROI框的大小和位置；对ROI框中目标血管后壁的内膜和中膜的位置进行识别，并测量和显示目标血管后壁的内中膜厚度IMT。可简化操作过程，提高诊断效率。

Description

血管的内中膜自动识别测量方法及超声仪
技术领域
本发明涉及测量技术领域，尤其涉及一种血管的内中膜自动识别测量方法及超声仪。
背景技术
在动脉粥样硬化病变基础上发生的心脑血管疾病是现代人最主要的疾病之一，而动脉粥样硬化有一个长期隐藏发展的过程，据研究表明，颈动脉内中膜厚度IMT（IMT，inti ma-media thickness，内中膜厚度，也即内膜厚度和中膜厚度之和）与心肌梗塞等诸多心脑血管疾病有着明显的相关性，因此可以根据对颈动脉内中膜厚度IMT的测量结果，提前预知相关疾病的风险程度。然而，目前唯有超声技术可以实时无创伤地对IMT进行有效地测量。因此，IMT测量技术目前被广泛应用于颈动脉诊断的医学超声实践中。
IMT的测量主要是通过对常规B型超声的图像进行模式识别，找到最可能的颈动脉内中膜并显示出来，然后计算出相应的测量参数（如内中膜厚度IMT、血管内径值及血管外径值等）。由于实际颈动脉的图像，不同的人具有不同的图像特点，现有技术是将颈动脉部位的B超图像冻结后，由用户自由选择一个ROI框（ROI，region of interest，感兴趣的区域），然后启动预先设置的IMT算法来自动寻找该ROI框内的颈动脉内中膜疑似物，并计算出相应的测量值。然而，用户自由选择ROI框的过程始终是需要用户进行许多的手动操作，如移动ROI框的位置，还有放大或缩小ROI框的大小，即选择ROI框的过程需要用户反复操作键盘和控制鼠标的轨迹球，操作较复杂，从而导致诊疗效率低下。
发明内容
本发明的主要目的在于提供一种血管的内中膜自动识别测量方法，旨在简化操作过程和提高诊断效率。
为了实现上述目的，本发明提供一种血管的内中膜自动识别测量方法，所述血管内中膜测量的自动识别方法包括以下步骤：
在B超图像上的各回波线上获取血管的血管边缘候选点；
根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，并测量和显示所述目标血管后壁的内中膜厚度IMT。
优选地，所述在B超图像上的各回波线上获取血管的血管边缘候选点具体为：
根据回波线上不同位置的灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
优选地，所述根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘包括：
建立与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数；
根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
优选地，所述获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁包括：
获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；
根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
优选地，所述预设的深度方向为由浅至深的方向。
此外，为实现上述目的，本发明还提供一种超声仪，所述超声仪包括：
获取模块：用于在B超图像上的各回波线上获取血管的血管边缘候选点；
分析模块：根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
处理模块：获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
确定模块：根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
内中膜识别模块：用于对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别；
测量模块：用于测量和显示所述目标血管后壁的内中膜厚度IMT。
优选地，根据回波线上不同位置灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
优选地，所述分析模块包括：
加权系数表建立单元：用于建立与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数；
血管边缘确定单元：用于根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
优选地，所述确定模块具体用于：
获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；并根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
优选地，所述预设的深度方向为由浅至深的方向。
本发明提供一种血管的内中膜自动识别测量方法，该血管的内中膜自动识别测量方法包括以下步骤：在B超图像上的各回波线上获取血管的血管边缘候选点；根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，并测量和显示所述目标血管后壁的内中膜厚度IMT。本发明血管的内中膜自动识别测量方法，由于能够自动确定当前B超图像中ROI框的大小和位置并自动识别血管的内中膜在所述ROI框内的位置，而不需要用户通过手动去确定当前B超图像中ROI框的大小和位置，从而简化了操作过程，提高了疾病诊断效率。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图示出的结构获得其他的附图。
图1为本发明血管的内中膜自动识别测量方法第一实施例的流程示意图；
图2为本发明超声仪第一实施例的功能模块示意图；
图3为本发明超声仪第二实施例中分析模块的细化功能模块示意图。
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明提供一种血管的内中膜自动识别测量方法，参照图1，在一实施例中，该血管的内中膜自动识别测量方法包括以下步骤：
步骤S10，在B超图像上的各回波线上获取血管的血管边缘候选点；
本发明实施例提供的血管的内中膜自动识别测量方法主要应用于医学超声实践中，用于心脑血管等相关疾病的B超图像的识别诊断，以提前告知病人相关疾病发生的风险程度。例如，人体的颈动脉血管的内中膜厚度IMT就与心肌梗塞等诸多心脑血管疾病有着明显的相关性，因此可以根据颈动脉血管的内中膜厚度IMT的测量结果，提前告知病人其心肌梗塞发生的风险程度。本实施例中，以对颈动脉的内中膜的自动识别测量为例对本发明实施例血管的内中膜自动识别测量方法进行详细说明。
具体地，由医学解剖知识可知，人体内的颈动脉血管在B超图像上必定具有一定的深度范围和一定的宽度范围，并且其深度范围和宽度范围不能任意变化，因此，颈动脉血管在B超图像的不同位置的可能性有大有小，呈现一种分布概率的形式，有的位置是血管边缘（即血管壁）的可能性高，有的位置是血管边缘的可能性低，因此，颈动脉血管的深度及颈动脉血管的宽度均可被用于识别颈动脉血管图像的加权系数。同时，由于颈动脉的血管边缘具有高回声的特性，因此颈动脉的血管边缘在B超图像上的显示位置也具有较高的灰度梯度；而血流本身具有低回声的特性，因此，颈动脉血管中的血流在B超图像上将有明显的亮暗区别，即颈动脉的血管边缘的灰度梯度也是在B超图像上识别血管图像的主要参考因素之一。
本发明实施例血管的内中膜自动识别测量方法，首先是在B超图像上的各回波线上获取血管的血管边缘候选点。具体地，本发明实施例血管的内中膜自动识别测量方法首先是根据回波线上不同位置灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点，以梯度最大的几个点作为血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。具体地，本实施例对所述血管边缘候选点的获取，是通过对B超图像上的各回波线沿着深度方向由浅至深的方向寻找劲动脉的血管边缘的候选点，太过浅表的点首先抛弃掉，然后在B超图像上的各回波线上分别寻找出灰度梯度为极值的点，将回波线上灰度梯度为极值的点作为该回波线的血管边缘候选点，所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
步骤S20，根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
具体地，本发明实施例血管的内中膜自动识别测量方法在B超图像上的各回波线上获取到颈动脉血管的血管边缘候选点之后，根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘。在本实施例中，要确定每一条回波线对应的一组血管上边缘和血管下边缘，需要事先建立一个与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数。下面以深度权重系数为例对权重系数进行说明：作为人体主要动脉的颈动脉，其血管的深度有一个常规位置，根据经验值，可以设置2个可调的边界经验参数值，即最小深度参数值与最大深度参数值，然后利用这两个边界经验参数值，可以编程生成一条多参数可调的光滑渐变曲线，从而可以将不同的深度数值映射为不同的权重系数。
本实施例在建立所述加权系数表后，则可以根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
步骤S30，获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
具体地，在本实施例中，在确定每一条回波线对应的血管上边缘和血管下边缘之后，接着获取各组所述血管上边缘和血管下边缘的位置坐标，然后按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类（其中，所述排序和聚类都是图像识别技术中的基本算法，此处不再赘述），从而获得一段最长的血管连续后壁，并将所述最长的血管连续后壁作为目标血管后壁。本实施例中，所述预设的深度方向为由浅至深的方向。
步骤S40，根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
具体地，在本实施例中，以所述目标血管后壁的位置作为所述ROI框的基准位置，将所述目标血管后壁的长度作为所述ROI框的基准宽度，另外，所述ROI框的高度可以根据预先设置的最优的高度值进行设定，从而可以得到所述ROI框的参数坐标，进而确定所述ROI框的大小和位置。
另外，需要说明的是，在本实施例中，如果用户对B超图像的其他区域的内中膜感兴趣，也可以根据用户的查看需求，通过手动的方式移动所述ROI框，通过移动所述ROI框来指定想要查看的图像区域。
步骤S50，对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，并测量和显示所述目标血管后壁的内中膜厚度IMT。
可以理解的是，在本实施例中，对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，是常规超声IMT识别技术的基本应用，属于常规技术，有多种方式可以实现，例如，可以在所述ROI框的所述目标血管后壁中，按照预先设定好的能量函数来计算所述目标血管后壁附近各像素点的能量数值，找出具有极值的配对点，即可获得所述目标血管后壁对应的血管内膜和中膜在图像上的具体位置。另外，测量和显示所述目标血管后壁的内中膜厚度IMT也是现有的常规技术，此处不再赘述。
本发明提供的该血管的内中膜自动识别测量方法，包括以下步骤：在B超图像上的各回波线上获取血管的血管边缘候选点；根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，并测量和显示所述目标血管后壁的内中膜厚度IMT。本发明血管的内中膜自动识别测量方法，由于能够自动确定当前B超图像中ROI框的大小和位置并自动识别血管的内中膜在所述ROI框内的位置，而不需要用户通过手动去确定当前B超图像中ROI框的大小和位置，从而简化了操作过程，提高了疾病诊断效率。
本发明还提供一种超声仪，参照图2，在一实施例中，本发明提供的超声仪100包括获取模块101、分析模块102、处理模块103、确定模块104和测量模块105。
其中，所述获取模块101：用于在B超图像上的各回波线上获取血管的血管边缘候选点；
本发明实施例提供的超声仪应用于医学超声实践中，用于心脑血管等相关疾病的B超图像的识别诊断，以提前告知病人相关疾病发生的风险程度。例如，人体的颈动脉血管的内中膜厚度IMT就与心肌梗塞等诸多心脑血管疾病有着明显的相关性，因此可以通过超声仪对人体颈动脉血管的内中膜厚度IMT进行测量，根据测量结果，提前告知病人其心肌梗塞发生的风险程度。以下各实施例中，以对颈动脉的内中膜的自动识别测量为例来对本发明实施例超声仪进行详细说明。
具体地，由医学解剖知识可知，人体内的颈动脉血管在B超图像上必定具有一定的深度范围和一定的宽度范围，并且其深度范围和宽度范围不能任意变化，因此，颈动脉血管在B超图像的不同位置的可能性有大有小，呈现一种分布概率的形式，有的位置是血管边缘（即血管壁）的可能性高，有的位置是血管边缘的可能性低，因此，颈动脉血管的深度及颈动脉血管的宽度均可被用于识别颈动脉血管图像的加权系数。同时，由于颈动脉的血管边缘具有高回声的特性，因此颈动脉的血管边缘在B超图像上的显示位置也具有较高的灰度梯度；而血流本身具有低回声的特性，因此，颈动脉血管中的血流在B超图像上将有明显的亮暗区别，即颈动脉的血管边缘的灰度梯度也是在B超图像上识别血管图像的主要参考因素之一。
本发明实施例超声仪，首先是获取模块101在B超图像上的各回波线上获取血管的血管边缘候选点。具体地，本发明实施例中，所述获取模块101根据回波线上不同位置灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点，以梯度最大的几个点作为血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。具体地，本实施例中，所述获取模块101对所述血管边缘候选点的获取，是通过对B超图像上的各回波线沿着深度方向由浅至深的方向寻找劲动脉的血管边缘的候选点，太过浅表的点首先抛弃掉，然后在B超图像上的各回波线上分别寻找出灰度梯度为极值的点，将回波线上灰度梯度为极值的点作为该回波线的血管边缘候选点，所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
所述分析模块102：用于根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
具体地，本发明实施例中，所述获取模块101在B超图像上的各回波线上获取到颈动脉血管的血管边缘候选点之后，所述分析模块102根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘。
所述处理模块103：用于获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
具体地，在本实施例中，由所述分析模块102确定每一条回波线对应的血管上边缘和血管下边缘之后，接着由所述处理模块103获取各组所述血管上边缘和血管下边缘的位置坐标，然后按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类（其中，所述排序和聚类都是图像识别技术中的基本算法，此处不再赘述），从而获得一段最长的血管连续后壁，并将所述最长的血管连续后壁作为目标血管后壁。本实施例中，所述预设的深度方向为由浅至深的方向。
所述确定模块104：用于根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
具体地，在本实施例中，所述确定模块104以所述目标血管后壁的位置作为所述ROI框的基准位置，将所述目标血管后壁的长度作为所述ROI框的基准宽度，另外，所述ROI框的高度可以根据预先设置的最优的高度值进行设定，从而可以得到所述ROI框的参数坐标，进而确定所述ROI框的大小和位置。即本实施例中，所述确定模块104能够自动确定当前B超图像中ROI框的大小和位置，而不是向现有技术中提到的那样，需要用户通过手动方式去移动ROI框的位置，还有放大或缩小ROI框的大小，从而简化了操作过程，同时，提高了疾病诊断效率。
所述内中膜识别模块105：用于对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别；
可以理解的是，在本实施例中，所述内中膜识别模块105对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，是常规超声IMT识别技术的基本应用，属于常规技术，有多种方式可以实现，例如，所述内中膜识别模块105可以在所述ROI框的所述目标血管后壁中，按照预先设定好的能量函数来计算所述目标血管后壁附近各像素点的能量数值，找出具有极值的配对点，即可获得所述目标血管后壁对应的血管内膜和中膜在图像上的具体位置。
另外，需要说明的是，在本实施例中，如果用户对B超图像的其他区域的内中膜感兴趣，也可以根据用户的查看需求，通过手动的方式移动所述ROI框，通过移动所述ROI框来指定想要查看的图像区域。
所述测量模块106：用于测量和显示所述目标血管后壁的内中膜厚度IMT。
本实施例中，所述测量模块106对所述目标血管后壁的内中膜厚度IMT的测量和显示也是现有的常规技术，此处不再赘述。
进一步地，参照图2，基于本发明超声仪第一实施例，在本发明超声仪第二实施例中，上述分析模块102还包括加权系数表建立单元1021和血管边缘确定单元1022。
其中，所述加权系数表建立单元1021，用于建立与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数；
在本实施例中，要确定每一条回波线对应的一组血管上边缘和血管下边缘，需要事先由所述加权系数表建立单元1021建立一个与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数。下面以深度权重系数为例对权重系数进行说明：作为人体主要动脉的颈动脉，其血管的深度有一个常规位置，根据经验值，可以设置2个可调的边界经验参数值，即最小深度参数值与最大深度参数值，然后利用这两个边界经验参数值，可以编程生成一条多参数可调的光滑渐变曲线，从而可以将不同的深度数值映射为不同的权重系数。
所述血管边缘确定单元1022：用于根据所述加权系数表中的相应权重系数和及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘
本实施例在所述加权系数表建立单元1021建立所述加权系数表后，则所述血管边缘确定单元1022可以根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
本发明提供的该超声仪，包括获取模块、分析模块、处理模块、确定模块和测量模块。其中，所述获取模块用于在B超图像上的各回波线上获取血管的血管边缘候选点；所述分析模块：根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；所述处理模块：获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；所述确定模块：根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；所述内中膜识别模块：用于对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别；所述测量模块：用于测量和显示所述目标血管后壁的内中膜厚度IMT。本发明超声仪由于能够自动确定当前B超图像中ROI框的大小和位置并自动识别血管的内中膜在所述ROI框内的位置，而不需要用户通过手动去确定当前B超图像中ROI框的大小和位置，从而简化了操作过程，提高了疾病诊断效率。
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (14)

  1. 一种血管的内中膜自动识别测量方法，其特征在于，所述血管内中膜测量的自动识别方法包括以下步骤：
    在B超图像上的各回波线上获取血管的血管边缘候选点；
    根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
    根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
    对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别，并测量和显示所述目标血管后壁的内中膜厚度IMT。
  2. 如权利要求1所述的血管的内中膜自动识别测量方法，其特征在于，所述在B超图像上的各回波线上获取血管的血管边缘候选点具体为：
    根据回波线上不同位置灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
  3. 如权利要求2所述的血管的内中膜自动识别测量方法，其特征在于，所述根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘包括：
    建立与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数；
    根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
  4. 如权利要求1所述的血管的内中膜自动识别测量方法，其特征在于，所述获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁包括：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；
    根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  5. 如权利要求2所述的血管的内中膜自动识别测量方法，其特征在于，所述获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁包括：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；
    根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  6. 如权利要求3所述的血管的内中膜自动识别测量方法，其特征在于，所述获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁包括：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；
    根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  7. 如权利要求1所述的血管的内中膜自动识别测量方法，其特征在于，所述预设的深度方向为由浅至深的方向。
  8. 一种超声仪，其特征在于，所述超声仪包括：
    获取模块：用于在B超图像上的各回波线上获取血管的血管边缘候选点；
    分析模块：根据预设的血管边缘识别规则，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的一组血管上边缘和血管下边缘；
    处理模块：获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类，确定目标血管后壁；
    确定模块：根据所述目标血管后壁的长度及预设的高度，确定当前B超图像中ROI框的大小和位置；
    内中膜识别模块：用于对所述ROI框中所述目标血管后壁的内膜和中膜的位置进行识别；
    测量模块：用于测量和显示所述目标血管后壁的内中膜厚度IMT。
  9. 如权利要求8所述的超声仪，其特征在于，所述获取模块具体用于：
    根据回波线上不同位置灰度梯度的排序结果，在B超图像上的各回波线上获取血管的血管边缘候选点；所述血管边缘候选点包括血管上边缘候选点和血管下边缘候选点。
  10. 如权利要求9所述的超声仪，其特征在于，所述分析模块包括：
    加权系数表建立单元：用于建立与血管深度和血管宽度一一相对应的加权系数表，所述加权系数表包括与血管深度相对应的深度权重系数以及与血管宽度相对应的宽度权重系数；
    血管边缘确定单元：用于根据所述加权系数表中的相应权重系数及回波线上不同位置所对应的灰度梯度，在各所述回波线上的所述血管边缘候选点中分别确定每一条回波线对应的权重最高的一组血管上边缘和血管下边缘，并将其作为相应回波线上的血管上边缘和血管下边缘。
  11. 如权利要求8所述的超声仪，其特征在于，所述确定模块具体用于：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；并根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  12. 如权利要求9所述的超声仪，其特征在于，所述确定模块具体用于：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；并根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  13. 如权利要求10所述的超声仪，其特征在于，所述确定模块具体用于：
    获取各组所述血管上边缘和血管下边缘的位置坐标，按照预设的深度方向对各组所述血管上边缘和血管下边缘的位置坐标进行排序，并对各所述位置坐标进行聚类；并根据聚类结果，获得一段最长的血管连续后壁，将所述最长的血管连续后壁作为目标血管后壁。
  14. 如权利要求8所述的超声仪，其特征在于，所述预设的深度方向为由浅至深的方向。
PCT/CN2016/101493 2016-09-28 2016-10-08 血管的内中膜自动识别测量方法及超声仪 WO2018058697A1 (zh)

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