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.
|Number of pages||14|
|Journal||Journal of the Operations Research Society of China|
|State||Published - Dec 1 2019|
Bibliographical notePublisher Copyright:
© 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.
- Feature prior
- Hip joint
- Image segmentation
- Level set
- Variational model
ASJC Scopus subject areas
- Mathematics (all)
- Management Science and Operations Research
- Applied Mathematics