Automatic segmentation of MR depicted carotid arterial boundary based on local priors and constrained global optimisation

• Published in: IET image processing 2019-02, Vol.13 (3), p.506-514
• Main Authors: Zhang, Jianhua, Teng, Zhongzhao, Guan, Qiu, He, Junli, Abutaleb, Wafa, Patterson, Andrew J, Graves, Martin J, Gillard, Jonathan, Chen, Shengyong
• Format: Article
• Language: English
• Subjects: appearance priors; biomedical MRI; blood vessels; carotid arterial boundary; carotid artery; carotid atherosclerotic disease diagnosis; diseases; flow artefact; Gaussian mixture models; Gaussian processes; geodesic star‐shape‐constrained graph cut; global optimisation; graph theory; image segmentation; image signal‐to‐noise ratio; lumen segmentation; magnetic resonance images; medical image processing; optimisation; outer wall boundaries segmentation; Research Article; shape priors; shape recognition; support vector machine; support vector machines
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5330

Segmentation of lumen (LB) and outer wall boundaries (OB) of carotid artery in magnetic resonance (MR) images is essential for carotid atherosclerotic disease diagnosis. However, the limited image signal-to-noise ratio, flow artefact, and varied lumen and outer wall become significant obstacles for automatic segmentation. A fully automatic framework is proposed for LB and OB segmentation in MR images. First, the lumen is identified by the support vector machine using a special strategy and LB is segmented by the geodesic star-shape-constrained graph cut. Then a novel global optimisation is developed to segment OB based on the graph cut, which consists of shape priors and appearance priors. The shape priors are learned from labelled shapes on LB and OB, while the appearance priors are modelled by Gaussian mixture models. A novel shape constraint is also designed as the constraint term. To evaluate author's method, extensive experiments are carried out from 160 MR images belonging to 16 patients. Experimental results demonstrate that the proposed method can yield high accuracy with fully automatic segmentation. Moreover, the advantages of the proposed method have been shown in terms of high flexibility and accuracy without user interactions in comparison with other methods.