Paper Info
Title
Automatic liver segmentation using a statistical shape model with optimal surface detection.
Abstract
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.
Year
DOI
Venue
2010
10.1109/TBME.2010.2056369
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
optimal surface detection,computerised tomography,intensity profile,principal component analysis (pca),liver segmentation,minimum s-t cut,generalized hough transform (ght),image segmentation,computed tomography,minimum s–t cut,statistical shape model (ssm),automatic liver segmentation,gradient profile,liver,graph theory,hough transforms,statistical shape model,medical image processing,3-d generalized hough transform,principal component analysis,ct scan,shape,robustness,testing
Computer vision,Pattern recognition,Subspace topology,Computer science,Segmentation,Computer-aided diagnosis,Hough transform,Image processing,Image segmentation,Artificial intelligence,Statistical model,Initialization
Journal
Volume
Issue
ISSN
57
10
1558-2531
Citations 
PageRank 
References 
42
1.77
5
Authors
5
Name
Order
Citations
PageRank
Xing Zhang11096.74
Jie Tian21475159.24
Kexin Deng3421.77
Yongfang Wu4421.77
Xiu-Li Li5886.24