Automatic carotid artery internal and external wall segmentation method based on shape prior and similarity constraint

Abstract

The invention relates to an automatic carotid artery internal and external wall segmentation method based on shape prior and similarity constraint. The segmentation method comprises the following steps: 1) detecting a carotid artery lumen through an SVM method to obtain a position where the lumen is; 2) segmenting an internal wall by using an anisotropic segmentation method; 3) learning a regression model in combination of the features of an external wall based on the segmented internal wall, estimating the approximate position of the external wall and constructing a probability model; 4) segmenting the external wall by using a similarity constraint algorithm. The invention provides the automatic carotid artery internal and external wall segmentation method based on the shape prior and similarity constraint, which adopts an automatic mode, effectively realizes the internal and external wall segmentation and is higher in accuracy.

Description

technical field [0001] The invention relates to the field of medical image segmentation, in particular to a method for segmenting the inner and outer walls of a carotid artery. Background technique [0002] Medical image segmentation is a process of dividing an image into several regions according to the similarity within the region and the difference between regions. The vascular image segmentation is to "extract" the vascular structure from XRA, MRI, CT and other medical images. Considering the tissue complexity, blurred image, and low contrast of the vascular image itself, the traditional image segmentation method is not suitable. [0003] At present, the blood vessel segmentation methods mainly include the level set method based on geometric deformation, the geodesic active contour model, the graph cut method based on the active contour model, and so on. Existing segmentation methods generally require users to provide background and foreground seed points as a priori, ...

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Problems solved by technology

[0004] In order to overcome the non-automatic way of the existing carotid artery segmentation method, the inability to realize the segmentation of the outer wall, and the lack of high accuracy, the present invention provides an automatic method, which effectively realizes the segmentation of the inner and outer walls, and has a higher accuracy based on shape prior and Automatic Segmentation of Inner and Outer Walls of Carotid Arteries Based on Similarity Constraints

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