A multi-label fusion based level set method for multiple sclerosis lesion segmentation / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 453-459, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-774185
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Diagnostic Imaging
/
Magnetic Resonance Imaging
/
Multiple Sclerosis
Type of study:
Diagnostic study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2019
Type:
Article
Similar
MEDLINE
...
LILACS
LIS