Level set method coupled with Energy Image features for brain MR image segmentation.
Biomed Tech (Berl)
; 59(3): 219-29, 2014 Jun.
Article
en En
| MEDLINE
| ID: mdl-24598830
ABSTRACT
Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods:
one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Encéfalo
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Reconocimiento de Normas Patrones Automatizadas
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Imagen por Resonancia Magnética
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Interpretación de Imagen Asistida por Computador
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Aumento de la Imagen
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Artefactos
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Biomed Tech (Berl)
Año:
2014
Tipo del documento:
Article