Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation
;
(6): 264-255, 2006.
Artigo
em Chinês
| WPRIM
| ID: wpr-355400
ABSTRACT
This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors; second, the deformable model is constrained by both population-based and patient-specific shape statistics. At first, population-based shape statistics plays an leading role when the number of serial images is small, and gradually, patient-specific shape statistics plays a more and more important role after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Algoritmos
/
Simulação por Computador
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Diagnóstico por Imagem
/
Radiografia Torácica
/
Interpretação de Imagem Radiográfica Assistida por Computador
/
Intensificação de Imagem Radiográfica
/
Reprodutibilidade dos Testes
/
Interpretação Estatística de Dados
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Chinês
Revista:
Chinese Journal of Medical Instrumentation
Ano de publicação:
2006
Tipo de documento:
Artigo
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