Automatic Liver Segmentation from CT Scans Based on a Statistical Shape Model

被引：4

作者：

Zhang, Xing ^{[1
]}

Tian, Jie ^{[1
]}

Deng, Kexin ^{[1
]}

Wu, Yongfang ^{[1
]}

Li, Xiuli ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Med Image Proc Grp, Beijing 100190, Peoples R China

来源：

2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2010年

关键词：

D O I：

10.1109/IEMBS.2010.5626470

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

In this paper, we present an algorithm for automatic liver segmentation from CT scans which is based on a statistical shape model. The proposed method is a hybrid method that combines three steps: 1) Localization of the average liver shape model in a test CT volume via 3D generalized Hough transform; 2) Subspace initialization of the statistical shape model; 3) Deformation of 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.

引用

页码：5351 / 5354

页数：4

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