Automatic liver segmentation using a statistical shape model with optimal surface detection.
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.
Zhang
Xing
X
Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. xing.zhang@ia.ac.cn
Tian
Jie
J
Deng
Kexin
K
Wu
Yongfang
Y
Li
Xiuli
X
D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D014057:Tomography, X-Ray Computed / Q000379:methods*