Segmentation of neighboring organs in medical image with model competition

Authors:
Pingkun Yan;Weijia Shen;Ashraf A. Kassim;Mubarak Shah
Affiliations:
Department of Electrical & Computer Engineering, National University of Singapore;Department of Electrical & Computer Engineering, National University of Singapore;Department of Electrical & Computer Engineering, National University of Singapore;School of Computer Science, University of Central Florida
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

Citing 6
Cited 2

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application

Computer Vision and Image Understanding
Geodesic Active Contours

International Journal of Computer Vision
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

IEEE Transactions on Information Technology in Biomedicine
Active contours without edges

IEEE Transactions on Image Processing

Modeling interaction for segmentation of neighboring structures

IEEE Transactions on Information Technology in Biomedicine
Simultaneous multi-object segmentation using local robust statistics and contour interaction

MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging

Quantified Score

Hi-index	0.01

Visualization

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.