Evolution Model Based on Prior Information for Narrow Joint Segmentation

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

doi:10.1007/s40305-019-00265-w

Evolution Model Based on Prior Information for Narrow Joint Segmentation

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

Mathematics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	629-642
Number of pages	14
Journal	Journal of the Operations Research Society of China
Volume	7
Issue number	4
DOIs	https://doi.org/10.1007/s40305-019-00265-w
State	Published - 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.

Keywords

CT
Feature prior
Hip joint
Image segmentation
Level set
Variational model

ASJC Scopus subject areas

Mathematics (all)
Management Science and Operations Research
Applied Mathematics

Access to Document

10.1007/s40305-019-00265-w

Cite this

@article{2852a4cc9e4d4b3693e6870b205ec0f2,

title = "Evolution Model Based on Prior Information for Narrow Joint Segmentation",

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.",

keywords = "CT, Feature prior, Hip joint, Image segmentation, Level set, Variational model",

author = "Xin Wang and Shuai Xu and Zhen Ye and Zhou, {Chao Zheng} and Jing Qin",

note = "Publisher Copyright: {\textcopyright} 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.",

year = "2019",

month = dec,

day = "1",

doi = "10.1007/s40305-019-00265-w",

language = "English",

volume = "7",

pages = "629--642",

number = "4",

}

TY - JOUR

T1 - Evolution Model Based on Prior Information for Narrow Joint Segmentation

AU - Wang, Xin

AU - Xu, Shuai

AU - Ye, Zhen

AU - Zhou, Chao Zheng

AU - Qin, Jing

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

KW - CT

KW - Feature prior

KW - Hip joint

KW - Image segmentation

KW - Level set

KW - Variational model

UR - http://www.scopus.com/inward/record.url?scp=85073958034&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073958034&partnerID=8YFLogxK

U2 - 10.1007/s40305-019-00265-w

DO - 10.1007/s40305-019-00265-w

M3 - Article

AN - SCOPUS:85073958034

VL - 7

SP - 629

EP - 642

IS - 4

ER -

Evolution Model Based on Prior Information for Narrow Joint Segmentation

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this