Generalized multiresolution hierarchical shape models via automatic landmark clusterization.
Med Image Comput Comput Assist Interv
; 17(Pt 3): 1-8, 2014.
Article
en En
| MEDLINE
| ID: mdl-25320775
ABSTRACT
Point Distribution Models (PDM) are some of the most popular shape description techniques in medical imaging. However, to create an accurate shape model it is essential to have a representative sample of the underlying population, which is often challenging. This problem is particularly relevant as the dimensionality of the modeled structures increases, and becomes critical when dealing with complex 3D shapes. In this paper, we introduce a new generalized multiresolution hierarchical PDM (GMRH-PDM) able to efficiently address the high-dimension-low-sample-size challenge when modeling complex structures. Unlike previous approaches, our new and general framework extends hierarchical modeling to any type of structure (multi- and single-object shapes) allowing to describe efficiently the shape variability at different levels of resolution. Importantly, the configuration of the algorithm is automatized thanks to the new agglomerative landmark clustering method presented here. Our new and automatic GMRH-PDM framework performed significantly better than classical approaches, and as well as the state-of-the-art with the best manual configuration. Evaluations have been studied for two different cases, the right kidney, and a multi-object case composed of eight subcortical structures.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Encéfalo
/
Reconocimiento de Normas Patrones Automatizadas
/
Imagen por Resonancia Magnética
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Interpretación de Imagen Asistida por Computador
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Puntos Anatómicos de Referencia
/
Modelos Anatómicos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Año:
2014
Tipo del documento:
Article