Yongchang Zheng1, Danni Ai2, Jinrong Mu3, Weijian Cong3, Xuan Wang4, Haitao Zhao1, Jian Yang3. 1. Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China. 2. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China. danni@bit.edu.cn. 3. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China. 4. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Abstract
BACKGROUND: Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis. METHODS: This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction. RESULTS: The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system. CONCLUSIONS: Experimental results show that the proposed method is superior to eight other state of the art methods.
BACKGROUND: Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis. METHODS: This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction. RESULTS: The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system. CONCLUSIONS: Experimental results show that the proposed method is superior to eight other state of the art methods.
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