[go: up one dir, main page]

WO2018068195A1 - Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image - Google Patents

Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image Download PDF

Info

Publication number
WO2018068195A1
WO2018068195A1 PCT/CN2016/101753 CN2016101753W WO2018068195A1 WO 2018068195 A1 WO2018068195 A1 WO 2018068195A1 CN 2016101753 W CN2016101753 W CN 2016101753W WO 2018068195 A1 WO2018068195 A1 WO 2018068195A1
Authority
WO
WIPO (PCT)
Prior art keywords
axis direction
point
ridge
image
vascular
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2016/101753
Other languages
English (en)
Chinese (zh)
Inventor
周寿军
陆培
王澄
陈明扬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Institute of Advanced Technology of CAS
Original Assignee
Shenzhen Institute of Advanced Technology of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Institute of Advanced Technology of CAS filed Critical Shenzhen Institute of Advanced Technology of CAS
Priority to CN201680001077.8A priority Critical patent/CN106537451B/zh
Priority to PCT/CN2016/101753 priority patent/WO2018068195A1/fr
Publication of WO2018068195A1 publication Critical patent/WO2018068195A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • the present application belongs to the field of medical image processing, and in particular, to a method and a device for extracting a vascular ridge point based on an image gradient vector flow field.
  • the tubular target (vessel) is high signal, the background is low signal, and the contour of the tubular target cross section is Gaussian, the local gray maximum point perpendicular to the radial direction of the tubular target is Think of the tubular target ridge point.
  • the traditional method for extracting vascular ridge points is to determine local ridge points by first-order differential and second-order differential of images according to the definition of local gamma maxima.
  • the extreme point is determined by the first-order differential, and for the pixel in the image, the point where the gradient value is 0 is a sufficient condition of the local extreme point, that is, the satisfaction
  • the second-order differential characteristic the point at which the eigenvalue ⁇ i corresponding to the eigenvector v i of the Heessian matrix perpendicular to the radial direction of the tubular target is negative, as a necessary condition for the presence of the vascular ridge point.
  • the above method for extracting vascular ridge points is very sensitive to the noise existing in the background of the image, and usually erroneously recognizes the local noise bright points as extreme points, thereby greatly increasing the number of non-target ridge points in the image.
  • the present invention provides a method and a device for extracting a vascular ridge point based on an image gradient vector flow field, which is used to solve the problem that the ridge point extraction method based on local extremum point definition is susceptible to high-intensity noise points and is extracted.
  • the problem of low ridge point accuracy is not high.
  • a technical solution of the present application is to provide a blood vessel ridge point value-based method based on an image gradient vector flow field, including:
  • the gradient vector flow field model is used to obtain the gradient vector flow field of the enhanced image
  • the pixel point is a vascular ridge point in the direction of the coordinate axis.
  • Another technical solution of the present application is to provide a blood vessel ridge point extraction device based on an image gradient vector flow field, the device comprising:
  • a gradient vector flow field calculation module for obtaining a gradient vector flow field of the enhanced image by using a gradient vector flow field model
  • the ridge point detecting module calculates, according to the gradient vector flow field of the enhanced image, a cosine value of a vector at a pixel point in the enhanced image and a vector angle between two adjacent pixel points of the pixel along a coordinate axis direction, If the cosine value satisfies a threshold condition, the pixel point is a vascular ridge point in the direction of the coordinate axis.
  • the application can effectively suppress the influence of background noise, improve the extraction efficiency of vascular ridge points in angiographic images, increase the number of detection of vascular target ridge points and improve the detection accuracy of vascular ridge points, and provide a basis for subsequent extraction of blood vessel centerline and blood vessel modeling. .
  • FIG. 1 is a flowchart of a method for extracting a vascular ridge point based on an image gradient vector flow field according to an embodiment of the present application
  • FIG. 2a is a schematic view of an isolated ridge point according to an embodiment of the present application.
  • 2b is a schematic view of an isolated ridge group in the embodiment of the present application.
  • 3a is a magnetic resonance three-dimensional angiography image of an embodiment of the present application.
  • Figure 3b is a maximum density projection image of the image data processed by the multi-scale vascular enhancement function of Figure 3a;
  • 4a is an image of a blood vessel enhanced embodiment of the present application.
  • FIG. 4b is a schematic diagram of a gradient vector flow field for obtaining an image using the gradient vector flow field model for the image shown in FIG. 4a;
  • FIG. 5a is a three-dimensional simulation diagram of an angiographic image according to an embodiment of the present application.
  • Figure 5b is a result of the ridge point extraction of the image shown in Figure 5a;
  • FIG. 6 is a structural diagram of a blood vessel ridge point extracting apparatus based on an image gradient vector flow field according to an embodiment of the present application.
  • FIG. 1 is a flowchart of a method for extracting a vascular ridge point based on an image gradient vector flow field according to an embodiment of the present application.
  • the embodiment can effectively suppress the influence of background noise, improve the extraction efficiency of vascular ridge points in angiographic images, increase the number of detection of vascular target ridge points, and improve the detection accuracy of vascular ridge points, and provide for subsequent extraction and vascular modeling of blood vessel centerlines. basis.
  • the method includes:
  • Step 101 Enhance the blood vessel target in the angiographic image.
  • Step 102 Calculate a gradient vector flow field of the enhanced image by using a gradient vector flow field model.
  • the gradient vector flow field model is a globally optimized vector field.
  • the vector of each point (pixel point) in the gradient vector flow field of the image points to the ridge point of the blood vessel target, that is, the local gray maximum point of the radial direction of the blood vessel target. According to this feature, the ridge points in the angiographic image can be accurately, stably and quickly extracted by the following step 103.
  • Step 103 Calculate, according to the gradient vector flow field of the enhanced image, respectively, a cosine value of a vector at a pixel point in the enhanced image and a vector angle between two adjacent pixel points of the pixel along a coordinate axis direction.
  • the pixel point is a vascular ridge point in the direction of the coordinate axis.
  • the angiographic image described in the present application may be a two-dimensional image or a three-dimensional image.
  • the coordinate axis direction includes a ⁇ x, ⁇ y direction
  • the coordinate axis direction includes ⁇ x, ⁇ y, ⁇ z directions.
  • step 103 is repeated until all pixel points of the enhanced image and all coordinate axes are traversed to obtain all vascular ridge points.
  • the method further comprises: removing the isolated points from the obtained vascular ridge points.
  • the process of removing isolated points from all obtained vascular ridge points includes:
  • the isolated ridge point A is deleted; in Fig. 2b, if there are no other ridge points in the ring, the ridge points A, B are The isolated ridge group consisting of C and D is deleted.
  • the above step 101 may enhance the vascular target in the three-dimensional angiographic image by the first multi-scale vascular enhancement function as follows:
  • v 0 (s) is a first multi-scale vascular enhancement function
  • R A , R B and S are three measure functions
  • R A is used to distinguish between sheet and line structures
  • R B is used to distinguish between point structures and line structures
  • S is used to distinguish background pixels
  • the threshold is used to control the sensitivity of the vascular enhancement algorithm to R A , R B and S
  • ⁇ 1 , ⁇ 2 and ⁇ 3 are the three eigenvalues of the Hessian matrix H and satisfy
  • , D is the dimension of the image
  • s is a certain pixel point.
  • the Hessian matrix H is a square matrix composed of a third-order partial derivative, which can be calculated by an existing method.
  • the thresholds ⁇ and ⁇ are generally taken as 0.5, and the value of the threshold c depends on the gray scale range of the image. Usually takes half of the maximum Hessian matrix norm.
  • step 101 above enhances the vascular target in the two-dimensional angiographic image by a second multi-scale vascular enhancement function as follows:
  • v 0 '(s) is a second multi-scale vascular enhancement function
  • R B ' and S' are measure functions
  • R B ' is used to distinguish between point structures and line structures
  • S' is used to distinguish background pixels
  • ⁇ and c are threshold values
  • ⁇ 1 and ⁇ 2 are two of Hessian matrix H The feature value, and satisfies
  • , D is the dimension of the image.
  • the vascular target gray value is enhanced and the background noise is suppressed.
  • the multi-scale vascular enhancement function is obtained by multi-scale Gaussian filtering and Hessian matrix eigenvalue calculation of angiographic images.
  • the specific calculation process refers to Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: Proceedings of the International Conference on Medical Image Computing Computer Assisted Intervention. Lect. Notes Comp. Sci., 1496, pp. 130-137, which is not described in detail herein.
  • FIG. 3a is a magnetic resonance three-dimensional angiography image of the embodiment of the present application
  • FIG. 3b is a maximum density projection image of the image data processed by the blood vessel enhancement function of FIG. 3a.
  • the background noise of the image processed by the multi-scale vascular enhancement function is suppressed, and the point on the center line of the blood vessel has the maximum gray value perpendicular to the direction of the blood vessel.
  • V(x, y) (u(x, y), v(x, y))
  • u(x, y), v(x, y) are respectively
  • the two components of the vector V can be obtained by minimizing the energy functional.
  • the specific formula is as follows:
  • ⁇ is the energy functional
  • (x, y) is the coordinate of the pixel of the enhanced image
  • u x , u y , v x , v y are the first-order partial derivatives of the components u and v respectively for x and y
  • (x, y) is the edge of the enhanced image
  • ⁇ is the control parameter.
  • ⁇ can be set according to the quality of the image (such as noise).
  • the ⁇ value the smaller the dynamic range of the gradient vector flow field, which can detect finer blood vessels, but the more susceptible it is to noise.
  • f x (x, y) and f y (x, y) are the values of the blood vessel edge function f(x, y) in the x and y directions of the enhanced image
  • u(x, y) is the two components of the gradient vector flow field
  • V(x, y) (u(x, y), v(x, y))
  • (x, y) is the coordinates of the pixel points of the enhanced image
  • ⁇ is the control parameter.
  • using the gradient vector flow field model to obtain the gradient vector flow field of the enhanced image further includes:
  • n is the number of iterations.
  • V(x, y) (u(x, y), v(x, y)).
  • V(x, y, z) (u(x, y, z), v(x, y, z), w(x, y, z)).
  • FIG. 4a is a blood vessel enhanced image of the embodiment of the present application
  • FIG. 4b is a gradient vector flow for obtaining an image using the gradient vector flow field model for the image shown in FIG. 4a.
  • a schematic diagram of the field as can be seen from Figure 4b, the vector arrow points to the vascular target centerline, the gradient at the centerline of the vessel target is strongest, and the gradient field away from the centerline is zero.
  • the pixel point For the x-axis direction of the two-dimensional angiography image, it is determined whether a certain pixel point satisfies the following formula, and if satisfied, the pixel point is a ridge point in the x-axis direction:
  • (i, j) is the coordinates of a certain pixel point
  • (i-1, j) and (i+1, j) are (i, j) two adjacent pixel points in the x-axis direction
  • V x ( i, j) , V x (i-1, j) , V x (i+1, j) are (i, j), (i-1, j), (i+1, j) pixel points, respectively
  • the components of the vector in the x-axis direction, V y (i,j) , V y (i-1,j) , V y (i+1,j) are (i,j), (i-1,j), respectively.
  • the component of the (i+1,j) pixel at the y-axis direction, and T 0 is the threshold.
  • the pixel point For the y-axis direction of the two-dimensional angiography image, it is determined whether a certain pixel point satisfies the following formula, and if satisfied, the pixel point is a ridge point in the y-axis direction:
  • (i, j) is the coordinates of a certain pixel point
  • (i, j-1) and (i, j+1) are (i, j) two adjacent pixel points in the y-axis direction
  • V x ( i, j) , V x (i, j-1) , V x (i, j+1) are (i, j), (i, j-1), (i, j+1) pixel points, respectively
  • the component of the vector in the x-axis direction, V y (i,j) , V y (i-1,j) , V y (i,j+1) are (i,j), (i,j-1)
  • the component of the (i, j+1) pixel at the y-axis direction, and T 0 is the threshold.
  • the vector at each pixel point is a normalized vector, and the modulus of the vector is 1.
  • the threshold can be selected according to the extraction precision, and usually T 0 selects a value in the range [ 0 , 0.5].
  • the pixel point For the x-axis direction of the three-dimensional angiography image, it is determined whether a certain pixel point satisfies the following formula, and if satisfied, the pixel point is a ridge point in the x-axis direction:
  • (i, j, k) is the coordinates of a certain pixel point; (i-1, j, k) and (i+1, j, k) are two (i, j, k) in the x-axis direction Neighboring pixels; V x (i, j, k) , V x (i-1, j, k) , V x (i+1, j, k) are (i, j, k), (i -1, j, k), (i+1, j, k) components of the vector in the x-axis direction; V y (i, j, k) , V y (i-1, j, k) , V y (i+1, j, k) are the components of the vector at the (i, j, k), (i-1, j, k), (i+1, j, k) pixel points in the y-axis direction, respectively.
  • V z (i,j,k) , V z (i-1,j,k) , V z (i+1,j,k) are (i,j,k), (i-1,j, respectively , k), (i+1, j, k)
  • T 0 is the threshold.
  • the pixel point For the y-axis direction of the three-dimensional angiography image, it is determined whether a certain pixel point satisfies the following formula, and if satisfied, the pixel point is a ridge point in the y-axis direction:
  • (i, j, k) is the coordinates of a certain pixel point; (i, j-1, k) and (i, j+1, k) are two (i, j, k) in the y-axis direction Neighboring pixels; V x (i, j, k) , V x (i, j-1, k) , V x (i, j+1, k) are (i, j, k), (i , j-1, k), (i, j+1, k) the component of the vector in the x-axis direction; V y (i, j, k) , V y (i, j-1, k) , V y (i, j+1, k) are the components of the vector at the (i, j, k), (i, j-1, k), (i, j+1, k) pixel points in the y-axis direction, respectively.
  • V z (i, j, k) , V z (i, j-1, k) , V z (i, j+1, k) are (i, j, k), (i, j-1, respectively) , k), (i, j+1, k) the component of the vector in the z-axis direction at the pixel point; T 0 is the threshold.
  • the pixel point For the z-axis direction of the three-dimensional angiography image, it is determined whether a certain pixel point satisfies the following formula, and if satisfied, the pixel point is a ridge point in the z-axis direction:
  • (i, j, k) is the coordinates of a certain pixel point; (i, j, k-1) and (i, j, k+1) are two (i, j, k) in the z direction Neighboring pixels; V x (i, j, k) , V x (i, j, k-1) , V x (i, j, k+1) are (i, j, k), (i, respectively j, k-1), (i, j, k+1) the component of the vector at the x-axis direction; V y (i, j, k) , V y (i, j, k-1) , V y (i, j, k+1) are components of the vector at the (i, j, k), (i, j, k-1), (i, j, k+1) pixel points in the y-axis direction, respectively; V z (i,j,k)
  • the vector at each pixel is a normalized vector whose modulus is one.
  • the threshold can be selected according to the extraction precision, and usually T 0 selects a value in the range [ 0 , 0.5].
  • the ridge point extraction method in the three-dimensional simulation diagram shown in FIG. 5a is extracted by using the image gradient vector flow field based vascular ridge point extraction method described in the present application, and the ridge point extraction result is obtained. As shown in Figure 5b.
  • an embodiment of the present application further provides a blood vessel ridge extraction device based on an image gradient vector flow field, as described in the following embodiments. Since the principle of solving the problem of the device is similar to the method for extracting the vascular ridge point, the implementation of the device can be referred to the implementation of the method for extracting the vascular ridge point, and the repetition will not be repeated.
  • FIG. 6 is a vascular ridge point extracting device based on an image gradient vector flow field according to an embodiment of the present application.
  • the device can be implemented in a smart terminal, such as a mobile phone, a tablet computer, or the like by a logic circuit, or can implement functions of various components by software in a functional module manner, and run on the smart terminal.
  • the device includes: an enhancement module 601, configured to enhance a blood vessel target in the angiographic image;
  • a gradient vector flow field calculation module 602 configured to obtain a gradient vector flow field of the enhanced image by using the gradient vector flow field model
  • the ridge point detecting module 603 respectively calculates a cosine value of a vector at a pixel point in the enhanced image and a vector angle between two adjacent pixel points of the pixel along a coordinate axis according to the gradient vector flow field of the enhanced image. If the cosine value satisfies a threshold condition, the pixel point is a vascular ridge point in the direction of the coordinate axis.
  • the ridge point extracting device further includes: a culling module 604, configured to remove the isolated points from all the obtained vascular ridge points.
  • the culling module 604 is specifically configured to determine whether a ridge point has other ridge points in a ring circle composed of two concentric circles having a radius d and d+d 0 as a center, if not If there is, the ridge point is the center of the circle, and the ridge point of the circle range formed by the radius d is deleted, wherein d and d 0 are distance constants.
  • the enhancement module 601 enhances blood vessels in a three-dimensional angiographic image by a first multi-scale vascular enhancement function as follows:
  • v 0 (s) is the first multi-scale vascular enhancement function
  • R A , R B and S are measure functions
  • R A is used to distinguish between sheet and line structures
  • R B is used to distinguish between point structures and line structures
  • S is used to distinguish background pixels
  • ⁇ , ⁇ and c are threshold values.
  • ⁇ 1 , ⁇ 2 and ⁇ 3 are three eigenvalues of the Hessian matrix H, and satisfy
  • , D is the dimension of the image;
  • the vascular target in the two-dimensional angiography image is enhanced by a second multi-scale vascular enhancement function as follows:
  • v 0 '(s) is a second multi-scale vascular enhancement function
  • R B ' and S' are measure functions
  • R B ' is used to distinguish between point structures and line structures
  • S' is used to distinguish background pixels
  • ⁇ and c are threshold values
  • ⁇ 1 and ⁇ 2 are two of Hessian matrix H The feature value, and satisfies
  • , D is the dimension of the image.
  • the gradient vector flow field calculation module 602 is specifically configured to:
  • f x (x, y) and f y (x, y) are the values of the vessel edge function f(x, y) in the x and y directions of the enhanced two-dimensional angiography image
  • u( x, y) are the two components of the gradient vector flow field
  • V(x, y) (u(x, y), v(x, y))
  • (x, y) is enhanced
  • ⁇ is the control parameter
  • f x (x, y, z), f y (x, y, z) and f z (x, y, z) are the vascular edge functions of the enhanced three-dimensional angiography image f(x , y, z) values in the x, y, and z directions, (x, y, z) are the coordinates of the pixel points of the enhanced three-dimensional angiography image,
  • ⁇ is the control parameter.
  • ridge point detection module 603 For the ridge point detection module 603 to implement the ridge point detection process, refer to the above method embodiment, which is not described herein again.
  • the technical solution described in the above embodiments of the present application can effectively suppress the influence of background noise, improve the extraction efficiency of vascular ridge points in angiographic images, increase the number of detection of vascular target ridge points, and improve the detection accuracy of vascular ridge points, which is a follow-up vascular center.
  • Line extraction and vascular modeling provide the basis.
  • the embodiment of the present application further provides an electronic device, including a processor and a memory including a computer readable program, when executed, causing the processor to execute the image gradient vector based flow described in the above embodiment Field ridge point extraction method.
  • the embodiment of the present application further provides a computer readable program, wherein when the program is executed in an electronic device, the program causes a computer to execute an image gradient vector based flow field in the electronic device as described in the above embodiment Ridge extraction method.
  • the embodiment of the present application further provides a storage medium storing a computer readable program, wherein the computer readable program causes the computer to execute the image gradient vector flow field based ridge point extraction method described in the above embodiments in the electronic device.
  • portions of the application can be implemented in hardware, software, firmware, or a combination thereof.
  • multiple steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or combination of the following techniques well known in the art: having logic gates for implementing logic functions on data signals A discrete logic circuit of a circuit, an application specific integrated circuit with a suitable combination of logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and the like.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

L'invention concerne un procédé et un dispositif d'extraction d'un point de crête de vaisseau sur la base d'un champ de propagation de vecteur gradient d'image. Le procédé consiste à : agrandir une cible de vaisseau dans une image angiographique (101); obtenir un champ de propagation de vecteur gradient d'image améliorée à l'aide d'un modèle de champ de propagation de vecteur gradient (102); et calculer séparément des valeurs de cosinus d'angles inclus entre un vecteur au niveau d'un point de pixel de l'image améliorée et des vecteurs au niveau de deux points de pixel adjacents du point de pixel le long d'une direction d'axe de coordonnées selon le champ de propagation de vecteur gradient de l'image agrandie, le point de pixel étant un point de crête de vaisseau dans la direction d'axe de coordonnées si les valeurs de cosinus remplissent une condition de seuil (103). Le procédé peut supprimer efficacement l'influence du bruit de fond, améliorer l'efficacité d'extraction de points de crête de vaisseau dans une image angiographique, augmenter le nombre de points de crête cibles de vaisseau à détecter, et améliorer la précision de détection de points de crête de vaisseau, fournissant ainsi une base pour une extraction de ligne centrale vasculaire et une modélisation de vaisseau ultérieures.
PCT/CN2016/101753 2016-10-11 2016-10-11 Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image Ceased WO2018068195A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201680001077.8A CN106537451B (zh) 2016-10-11 2016-10-11 一种基于图像梯度矢量流场的血管脊点提取方法及装置
PCT/CN2016/101753 WO2018068195A1 (fr) 2016-10-11 2016-10-11 Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/101753 WO2018068195A1 (fr) 2016-10-11 2016-10-11 Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image

Publications (1)

Publication Number Publication Date
WO2018068195A1 true WO2018068195A1 (fr) 2018-04-19

Family

ID=58336003

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/101753 Ceased WO2018068195A1 (fr) 2016-10-11 2016-10-11 Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image

Country Status (2)

Country Link
CN (1) CN106537451B (fr)
WO (1) WO2018068195A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107330935B (zh) * 2017-05-24 2021-02-26 中国科学院深圳先进技术研究院 一种管状目标中心线的提取方法及装置
WO2018214053A1 (fr) * 2017-05-24 2018-11-29 中国科学院深圳先进技术研究院 Procédé et appareil d'extraction d'une ligne centrale cible tubulaire
CN107330895B (zh) * 2017-07-04 2020-05-19 上海联影医疗科技有限公司 医学图像的分割方法及装置
CN110490040B (zh) * 2019-05-30 2022-04-12 浙江理工大学 一种识别dsa冠状动脉图像中局部血管狭窄程度的方法

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001035339A2 (fr) * 1999-10-29 2001-05-17 Cnr Consiglio Nazionale Delle Ricerche Analyse automatique de sequence temporelle d'images anatomiques
US20080018646A1 (en) * 2006-06-30 2008-01-24 University Of Louisville Research Foundation Method and software for shape representation with curve skeletons
CN101127085A (zh) * 2006-07-28 2008-02-20 索尼株式会社 图像处理方法和图像处理设备
CN102184567A (zh) * 2011-05-04 2011-09-14 北京师范大学 基于球b样条曲线的三维血管模型构造方法
CN102346845A (zh) * 2011-09-15 2012-02-08 哈尔滨工程大学 基于多尺度二阶微分结构模型滤波形态响应直接提取手背静脉模式骨架的方法
CN102663761A (zh) * 2012-04-24 2012-09-12 长江勘测规划设计研究有限责任公司 用于影像地图的线状矢量与遥感影像自动配准方法
US20120308110A1 (en) * 2011-03-14 2012-12-06 Dongguk University, Industry-Academic Cooperation Foundation Automation Method For Computerized Tomography Image Analysis Using Automated Calculation Of Evaluation Index Of Degree Of Thoracic Deformation Based On Automatic Initialization, And Record Medium And Apparatus
CN102819823A (zh) * 2012-01-12 2012-12-12 北京理工大学 一种从造影图像中全自动跟踪提取血管的方法
CN103455999A (zh) * 2012-06-05 2013-12-18 北京工业大学 基于内超声图像序列的血管壁边缘自动检测方法
CN105976384A (zh) * 2016-05-16 2016-09-28 天津工业大学 基于GVF Snake模型的人体胸腹腔CT图像主动脉分割方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102903115B (zh) * 2012-10-12 2016-01-20 中国科学院深圳先进技术研究院 一种管状物体中心线的提取方法
CN103021022B (zh) * 2012-11-22 2016-03-09 北京理工大学 基于参数形变模型能量优化的血管重建方法
CN102982547B (zh) * 2012-11-29 2016-07-27 北京师范大学 自动初始化的局域活动轮廓模型心脑血管分割方法
CN105551041A (zh) * 2015-12-15 2016-05-04 中国科学院深圳先进技术研究院 普适的血管分割方法及系统

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001035339A2 (fr) * 1999-10-29 2001-05-17 Cnr Consiglio Nazionale Delle Ricerche Analyse automatique de sequence temporelle d'images anatomiques
US20080018646A1 (en) * 2006-06-30 2008-01-24 University Of Louisville Research Foundation Method and software for shape representation with curve skeletons
CN101127085A (zh) * 2006-07-28 2008-02-20 索尼株式会社 图像处理方法和图像处理设备
US20120308110A1 (en) * 2011-03-14 2012-12-06 Dongguk University, Industry-Academic Cooperation Foundation Automation Method For Computerized Tomography Image Analysis Using Automated Calculation Of Evaluation Index Of Degree Of Thoracic Deformation Based On Automatic Initialization, And Record Medium And Apparatus
CN102184567A (zh) * 2011-05-04 2011-09-14 北京师范大学 基于球b样条曲线的三维血管模型构造方法
CN102346845A (zh) * 2011-09-15 2012-02-08 哈尔滨工程大学 基于多尺度二阶微分结构模型滤波形态响应直接提取手背静脉模式骨架的方法
CN102819823A (zh) * 2012-01-12 2012-12-12 北京理工大学 一种从造影图像中全自动跟踪提取血管的方法
CN102663761A (zh) * 2012-04-24 2012-09-12 长江勘测规划设计研究有限责任公司 用于影像地图的线状矢量与遥感影像自动配准方法
CN103455999A (zh) * 2012-06-05 2013-12-18 北京工业大学 基于内超声图像序列的血管壁边缘自动检测方法
CN105976384A (zh) * 2016-05-16 2016-09-28 天津工业大学 基于GVF Snake模型的人体胸腹腔CT图像主动脉分割方法

Also Published As

Publication number Publication date
CN106537451A (zh) 2017-03-22
CN106537451B (zh) 2019-03-15

Similar Documents

Publication Publication Date Title
Lee et al. Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images
Shen et al. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy
Gao et al. Automatic segmentation of coronary tree in CT angiography images
Mukhopadhyay A segmentation framework of pulmonary nodules in lung CT images
Jin et al. Vascular tree segmentation in medical images using Hessian‐based multiscale filtering and level set method
CN111524139B (zh) 一种基于双边滤波器的角点检测方法和检测系统
Tian et al. A vessel active contour model for vascular segmentation
US7711167B2 (en) Fissure detection methods for lung lobe segmentation
Anand et al. An edge vector and edge map based boundary detection in medical images
WO2018068195A1 (fr) Procédé et dispositif d'extraction de point de crête de vaisseau basée sur un champ de propagation de vecteur gradient d'image
Merveille et al. Tubular structure filtering by ranking orientation responses of path operators
Niaz et al. Edge-based local and global energy active contour model driven by signed pressure force for image segmentation
Soltaninejad et al. Robust lung segmentation combining adaptive concave hulls with active contours
Cai et al. A phase-based active contour model for segmentation of breast ultrasound images
Kumar et al. Medical image edge detection using Gauss Gradient operator
Kumar et al. Edge detection of angiogram images using the classical image processing techniques
Jia et al. A novel lung nodules detection scheme based on vessel segmentation on CT images
Dvorak et al. Automated segmentation of brain tumour edema in flair mri using symmetry and thresholding
CN113012127A (zh) 基于胸部医学影像的心胸比测量方法
CN108416792B (zh) 基于活动轮廓模型的医学计算机断层扫描图像分割方法
CN118446979A (zh) 一种用于血管管径优化的血管精确定位方法和系统
Wang A bottleneck detection approach for separating overlapped cells
Hirayama et al. Extraction of GGO regions from chest CT images using deep learning
Aprovitola et al. Edge and junction detection improvement using the Canny algorithm with a fourth order accurate derivative filter
Khotanlou et al. Segmentation of uterine fibroid on mr images based on Chan–Vese level set method and shape prior model

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16918578

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 22.08.2019)

122 Ep: pct application non-entry in european phase

Ref document number: 16918578

Country of ref document: EP

Kind code of ref document: A1