A multi-label fusion based level set method for multiple sclerosis lesion segmentation.
10.7507/1001-5515.201808042
- Author:
Zhaoxuan GONG
1
,
2
;
Wei GUO
3
;
Guoxu ZHANG
4
;
Jia GUO
4
;
Zhenyu ZHU
3
;
Wenjun TAN
2
;
Guodong ZHANG
5
Author Information
1. School of Computer Science, Shenyang Aerospace University, Shenyang 110136, P.R.China
2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, P.R.China.
3. School of Computer Science, Shenyang Aerospace University, Shenyang 110136, P.R.China.
4. The General Hospital of Shenyang Military, Shenyang 110016, P.R.China.
5. .20180008@sau.edu.cn.
- Publication Type:Journal Article
- Keywords:
intensity prior information;
level set;
magnetic resonance image;
multi-label fusion;
multiple sclerosis lesion
- MeSH:
Algorithms;
Humans;
Magnetic Resonance Imaging;
Multiple Sclerosis;
diagnostic imaging
- From:
Journal of Biomedical Engineering
2019;36(3):453-459
- CountryChina
- Language:Chinese
-
Abstract:
A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.
