title: Automatic liver segmentation based on appearance and context information.
abstract: BACKGROUND
  Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis.
  
  
  METHODS
  This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.
  
  
  RESULTS
  The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.
  
  
  CONCLUSIONS
  Experimental results show that the proposed method is superior to eight other state of the art methods.
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, 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. danni@bit.edu.cn.
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: Jinrong
authors.initials: J
authors.lastname: Mu
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: Weijian
authors.initials: W
authors.lastname: Cong
authors.affiliation: Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
authors.forename: Haitao
authors.initials: H
authors.lastname: Zhao
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: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D011336:Probability; D014057:Tomography, X-Ray Computed