A new unsupervised algorithm for image segmentation based on an inhomogeneous Markov random field model / 南方医科大学学报
Journal of Southern Medical University
; (12): 1646-1648, 2007.
Article
em Zh
| WPRIM
| ID: wpr-281572
Biblioteca responsável:
WPRO
ABSTRACT
A new unsupervised algorithm for image segmentation is proposed using an inhomogeneous Markov random field (MRF) model, in which the parameter is estimated in fuzzy spel affinities. The proposed algorithm improved the accuracy of segmentation. Simulated brain MR image with different noise levels and clinical brain MR image were presented in the experiments. The results showed that the proposed algorithm was more powerful than conventional homogeneous MRF model-based ones and than the fuzzy c-means clustering algorithm as well.
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Algoritmos
/
Encéfalo
/
Imageamento por Ressonância Magnética
/
Interpretação de Imagem Assistida por Computador
/
Cadeias de Markov
/
Lógica Fuzzy
/
Métodos
Tipo de estudo:
Clinical_trials
/
Health_economic_evaluation
/
Prognostic_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Southern Medical University
Ano de publicação:
2007
Tipo de documento:
Article