Neighbor-constrained segmentation with level set based 3-D deformable models

IEEE Transactions on Medical Imaging
Jing YangJames S Duncan

Abstract

A novel method for the segmentation of multiple objects from three-dimensional (3-D) medical images using interobject constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring shape and position relationships of a set of training images. In order to estimate the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. This method is useful in situations where there is limited interobject information as opposed to robust global atlases. In addition, we compare our level set representation of the object shape to the point distribution model. Results and validation from experim...Continue Reading

References

Jun 20, 2002·IEEE Transactions on Medical Imaging·Rhodri H DaviesChris J Taylor
Sep 2, 2004·IEEE Transactions on Medical Imaging·Jing YangJames S Duncan
Feb 6, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·T F Chan, L A Vese

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Citations

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