Evolution Model Based on Prior Information for Narrow Joint Segmentation

Xin Wang, Shuai Xu, Zhen Ye, Chao Zheng Zhou, Jing Qin

Research output: Contribution to journalArticlepeer-review


Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

Original languageEnglish
Pages (from-to)629-642
Number of pages14
JournalJournal of the Operations Research Society of China
Issue number4
StatePublished - Dec 1 2019

Bibliographical note

Publisher Copyright:
© 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.


  • CT
  • Feature prior
  • Hip joint
  • Image segmentation
  • Level set
  • Variational model

ASJC Scopus subject areas

  • General Mathematics
  • Management Science and Operations Research


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