Segmentation of neighboring organs in medical image with model competition

    Authors

    P. Yan; W. J. Shen; A. A. Kassim;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Keywords

    ACTIVE CONTOURS; SHAPE; PROPAGATION; Computer Science, Information Systems; Computer Science, Theory &; Methods; Radiology, Nuclear Medicine & Medical Imaging

    Abstract

    This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.

    Journal Title

    Medical Image Computing and Computer-Assisted Intervention - Miccai 2005, Pt 1

    Volume

    3749

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    270

    Last Page

    277

    WOS Identifier

    WOS:000233337000034

    ISSN

    0302-9743; 3-540-29327-2

    Share

    COinS