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.
Artículo
en Chino
| 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.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Algoritmos
/
Diagnóstico por Imagen
/
Imagen por Resonancia Magnética
/
Esclerosis Múltiple
Tipo de estudio:
Estudio diagnóstico
Límite:
Humanos
Idioma:
Chino
Revista:
Journal of Biomedical Engineering
Año:
2019
Tipo del documento:
Artículo
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