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Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection

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5 Author(s)
Xing Zhang ; Med. Image Process. Group, Chinese Acad. of Sci., Beijing, China ; Tian, Jie ; Kexin Deng ; Yongfang Wu
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In this letter, we present an approach for automatic liver segmentation from computed tomography (CT) scans that is based on a statistical shape model (SSM) integrated with an optimal-surface-detection strategy. The proposed method is a hybrid method that combines three steps. First, we use localization of the average liver shape model in a test CT volume via 3-D generalized Hough transform. Second, we use subspace initialization of the SSM through intensity and gradient profile. Third, we deform the shape model to adapt to liver contour through an optimal-surface-detection approach based on graph theory. The proposed method is evaluated on MICCAI 2007 liver-segmentation challenge datasets. The experiment results demonstrate availability of the proposed method.

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Biomedical Engineering, IEEE Transactions on  (Volume:57 ,  Issue: 10 )