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

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.

Inspec keywords: image segmentation; optimisation; diseases; support vector machines; graph theory; biomedical MRI; blood vessels; Gaussian processes; medical image processing; shape recognition

Other keywords: global optimisation; Gaussian mixture models; support vector machine; lumen segmentation; carotid artery; carotid arterial boundary; image signal-to-noise ratio; flow artefact; magnetic resonance images; outer wall boundaries segmentation; shape priors; appearance priors; geodesic star-shape-constrained graph cut; carotid atherosclerotic disease diagnosis

Subjects: Combinatorial mathematics; Optical, image and video signal processing; Knowledge engineering techniques; Combinatorial mathematics; Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Biomedical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation; Other topics in statistics; Optimisation techniques; Other topics in statistics; Optimisation techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5330
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5330
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading