Neighbor-Constrained Segmentation with 3D Deformable Models

A novel method for the segmentation of multiple objects from 3D medical images using inter-object 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 positions and shapes of a set of training images. By estimating 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. We feel that this method is useful in situations wh...

• Type: Conference
• Year: 2003
• Where: IPMI
• Authors: Jing Yang, Lawrence H. Staib, James S. Duncan
• Keywords: IPMI 2003, Joint Probability Distributions, Medical Imaging, Multiple Objects, Neighbor Objects