title: [Liver CT image segmentation using statistical shape model based on statistical and specific information].
abstract: We propose an effective algorithm for accurate 3D segmentation of CT liver images based on statistical and specific information. We present a new intensity model which combines patient-specific intensity information of boundary with the statistical information for liver segmentation. Compared to the traditional methods, our approach not only produces excellent segmentation accuracy, but also increases the robustness.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. lichli1986@126.com
authors.forename: Chunli
authors.initials: C
authors.lastname: Li
authors.forename: Jiulou
authors.initials: J
authors.lastname: Zhang
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000000981:diagnostic imaging; D008113:Liver Neoplasms / Q000000981:diagnostic imaging; D015233:Models, Statistical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*