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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 345968, 11 pages
Research Article

Ultrasound Common Carotid Artery Segmentation Based on Active Shape Model

1State Key Laboratory for Multispectral Information Processing Technologies, Institute for Pattern Recognition and Artificial Intelligence (IPRAI), Huazhong University of Science and Technology (HUST), Wuhan, Hubei 430074, China
2Department of Biomedical Engineering, College of Life Science and Technology, Image Processing and Intelligence Control Key Laboratory of Education of Ministry of China, Huazhong University of Science and Technology (HUST), Wuhan, Hubei 430074, China
3Biomedical Instrument Institute, Med-X Research Institute, Shanghai Jiaotong University, Shanghai 200030, China

Received 20 December 2012; Revised 29 January 2013; Accepted 31 January 2013

Academic Editor: Peng Feng

Copyright © 2013 Xin Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.