RESUMO
We present algorithms for the automatic and precise segmentation of individual vertebras in CT Volume data. When a local surface evolution method such as the level set is applied to such a complex structure, global shape priors will not be sufficient to avoid the leakage and local minima problems, particularly if precise object boundary is desired. We propose a prior knowledge base that contains localized priors--a group of high-level features whose detection will augment the surface model and be the key to success. Base on this a set of context blockers are applied to prevent the leakages. Carefully designed initial surface when registered with the data helps avoid the local minimum problem. The results of segmentation well approximate the human delineated object boundaries. We also present the validation result of the segmentation of 150 vertebras.
Assuntos
Inteligência Artificial , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Técnica de Subtração , Vértebras Torácicas/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Humanos , Intensificação de Imagem Radiográfica/métodos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
In this paper we develop a multi-object prior shape model for use in curve evolution-based image segmentation. Our prior shape model is constructed from a family of shape distributions (cumulative distribution functions) of features related to the shape. Shape distribution-based object representations possess several desired properties, such as robustness, invariance, and good discriminative and generalizing properties. Further, our prior can capture information about the interaction between multiple objects. We incorporate this prior in a curve evolution formulation for shape estimation. We apply this methodology to problems in medical image segmentation.
