title: Automatic liver segmentation from CT scans based on a statistical shape model.
abstract: 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.
authors.affiliation: Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Zhang
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
authors.forename: Kexin
authors.initials: K
authors.lastname: Deng
authors.forename: Yongfang
authors.initials: Y
authors.lastname: Wu
authors.forename: Xiuli
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001331:Automation; D004058:Diffusion; D006801:Humans; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*