Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation.
Med Image Comput Comput Assist Interv
; 9351: 703-710, 2015 Oct.
Article
em En
| MEDLINE
| ID: mdl-27754496
ABSTRACT
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Próstata
/
Somatotipos
/
Algoritmos
/
Interpretação de Imagem Assistida por Computador
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
/
Male
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article