title: Feature Learning Based Random Walk for Liver Segmentation.
abstract: Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods.
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
authors.forename: Yongchang
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Danni
authors.initials: D
authors.lastname: Ai
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Pan
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Yefei
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Likun
authors.initials: L
authors.lastname: Xia
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
authors.forename: Shunda
authors.initials: S
authors.lastname: Du
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
authors.forename: Xinting
authors.initials: X
authors.lastname: Sang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001331:Automation; D019992:Databases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000033:anatomy & histology*; D008103:Liver Cirrhosis / Q000175:diagnosis; D011336:Probability; D012015:Reference Standards