Automatic segmentation of MR depicted carotid arterial boundary based on local priors and constrained global optimisation. Issue 3 (31st January 2019)

• Record Type: Journal Article
• Authors: Zhang, Jianhua; Teng, Zhongzhao; Guan, Qiu; He, Junli; Abutaleb, Wafa; Patterson, Andrew J.; Graves, Martin J.; Gillard, Jonathan; Chen, Shengyong
• Is Part Of: IET image processing. Volume 13:Issue 3(2019)
• Page Start: 506
• Page End: 514
• Publication Date: 2019-01-31
• Subjects: support vector machines -- medical image processing -- Gaussian processes -- image segmentation -- optimisation -- blood vessels -- biomedical MRI -- diseases -- graph theory -- shape recognition; carotid arterial boundary -- carotid artery -- magnetic resonance images -- carotid atherosclerotic disease diagnosis -- image signal‐to‐noise ratio -- flow artefact -- support vector machine -- geodesic star‐shape‐constrained graph cut -- shape priors -- appearance priors -- lumen segmentation -- global optimisation -- outer wall boundaries segmentation -- Gaussian mixture models; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5330
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.