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
em Zh
| WPRIM
| ID: wpr-774185
Biblioteca responsável:
WPRO
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.
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Base de dados:
WPRIM
Assunto principal:
Algoritmos
/
Diagnóstico por Imagem
/
Imageamento por Ressonância Magnética
/
Esclerose Múltipla
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2019
Tipo de documento:
Article