Validation of alternating Kernel mixture method: application to tissue segmentation of cortical and subcortical structures.
J Biomed Biotechnol
; 2008: 346129, 2008.
Article
en En
| MEDLINE
| ID: mdl-18695738
ABSTRACT
This paper describes the application of the alternating Kernel mixture (AKM) segmentation algorithm to high resolution MRI subvolumes acquired from a 1.5T scanner (hippocampus, n = 10 and prefrontal cortex, n = 9) and a 3T scanner (hippocampus, n = 10 and occipital lobe, n = 10). Segmentation of the subvolumes into cerebrospinal fluid, gray matter, and white matter tissue is validated by comparison with manual segmentation. When compared with other segmentation methods that use traditional Bayesian segmentation, AKM yields smaller errors (P < .005, exact Wilcoxon signed rank test) demonstrating the robustness and wide applicability of AKM across different structures. By generating multiple mixtures for each tissue compartment, AKM mimics the increased variation of manual segmentation in partial volumes due to the highly folded tissues. AKM's superior performance makes it useful for tissue segmentation of subcortical and cortical structures in large-scale neuroimaging studies.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Reconocimiento de Normas Patrones Automatizadas
/
Inteligencia Artificial
/
Imagen por Resonancia Magnética
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Interpretación de Imagen Asistida por Computador
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Corteza Cerebral
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Imagenología Tridimensional
/
Hipocampo
Tipo de estudio:
Diagnostic_studies
/
Evaluation_studies
Idioma:
En
Revista:
J Biomed Biotechnol
Asunto de la revista:
BIOTECNOLOGIA
/
MEDICINA
Año:
2008
Tipo del documento:
Article
País de afiliación:
Estados Unidos