Evolution Model Based on Prior Information for Narrow Joint Segmentation

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  • 1 Department of Mathematics, Shanghai University, Shanghai 200444, China;
    2 Shanghai Electric Central Research Institute, Shanghai 200070, China;
    3 Department of Mathematics, University of Kentucky, Lexington, KY 40506-0027, USA

Received date: 2019-05-08

  Revised date: 2019-06-05

  Online published: 2019-11-28

Supported by

This research was supported in part by the National Natural Science Foundation of China (Nos. 11771276, 11471208) and the capacity construction project of local universities in Shanghai (No. 18010500600). The research of Jing Qin was supported by the National Science Foundation of USA (No. DMS-1941197).

Abstract

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.

Cite this article

Xin Wang, Shuai Xu, Zhen Ye, Chao-Zheng Zhou, Jing Qin . Evolution Model Based on Prior Information for Narrow Joint Segmentation[J]. Journal of the Operations Research Society of China, 2019 , 7(4) : 629 -642 . DOI: 10.1007/s40305-019-00265-w

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