title: Liver vessel segmentation based on extreme learning machine.
abstract: Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.
authors.affiliation: Department of Biomedical and Information Engineering, Central South University, Changsha 410083, China.
authors.forename: Ye Zhan
authors.initials: YZ
authors.lastname: Zeng
authors.affiliation: Department of Biomedical and Information Engineering, Central South University, Changsha 410083, China; School of Information Science and Engineering, Central South University, Changsha 410083, China. Electronic address: zyq@csu.edu.cn.
authors.forename: Yu Qian
authors.initials: YQ
authors.lastname: Zhao
authors.affiliation: Department of Biomedical and Information Engineering, Central South University, Changsha 410083, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Liao
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors.forename: Bei Ji
authors.initials: BJ
authors.lastname: Zou
authors.affiliation: Department of Mathematics and Computer Science, École centrale de Lyon, Écully, France.
authors.forename: Xiao Fang
authors.initials: XF
authors.lastname: Wang
authors.affiliation: The Third Xiangya Hospital of Central South University, Changsha 410083, China. Electronic address: cjr.wangwei@vip.163.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D004058:Diffusion; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology*; D000069550:Machine Learning; D011860:Radiography, Abdominal; D011880:Radiotherapy Planning, Computer-Assisted; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed
keywords: CT; ELM; Liver vessels; Segmentation