A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models.
IEEE Trans Image Process
; 15(7): 1871-86, 2006 Jul.
Article
en En
| MEDLINE
| ID: mdl-16830909
ABSTRACT
Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Interpretación de Imagen Asistida por Computador
/
Aumento de la Imagen
/
Cadenas de Markov
/
Modelos Estadísticos
/
Almacenamiento y Recuperación de la Información
/
Imagenología Tridimensional
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Image Process
Asunto de la revista:
INFORMATICA MEDICA
Año:
2006
Tipo del documento:
Article
País de afiliación:
Estados Unidos