Hepatic vessel segmentation using variational level set combined with non-local robust statistics |
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| Institution: | 1. Beijing University of Chemical Technology, Beijing 100029, People''s Republic of China;2. Department of Hepatobiliary Surgery, Chinese PLA 309th Hospital, Beijing 100091, People''s Republic of China;3. Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People''s Republic of China;1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China;2. Digital Imaging Group of London, London, ON, Canada;3. Department of Medical Imaging, Western University, London, ON, Canada;1. Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea;2. Coreline Soft, Co., Ltd., Sung-Myung B/D 5F, World Cup buk-ro 6-gil 49, Mapo-Gu, Seoul 03991, South Korea;3. Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea;4. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea;1. School of Information Science and Engineering, Central South University, Changsha 410083, China;2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China;3. Department of Mathematics and Computer Science, École centrale de Lyon, Écully, France;4. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA |
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| Abstract: | Hepatic vessel segmentation is a challenging step in therapy guided by magnetic resonance imaging (MRI). This paper presents an improved variational level set method, which uses non-local robust statistics to suppress the influence of noise in MR images. The non-local robust statistics, which represent vascular features, are learned adaptively from seeds provided by users. K-means clustering in neighborhoods of seeds is utilized to exclude inappropriate seeds, which are obviously corrupted by noise. The neighborhoods of appropriate seeds are placed in an array to calculate the non-local robust statistics, and the variational level set formulation can be constructed. Bias correction is utilized in the level set formulation to reduce the influence of intensity inhomogeneity of MRI. Experiments were conducted over real MR images, and showed that the proposed method performed better on small hepatic vessel segmentation compared with other segmentation methods. |
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