Segmentation Of Neighboring Organs In Medical Image With Model Competition
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. © Springer-Verlag Berlin Heidelberg 2005.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of Pages
Article; Proceedings Paper
Source API URL
Yan, Pingkun; Shen, Weijia; and Kassim, Ashraf A., "Segmentation Of Neighboring Organs In Medical Image With Model Competition" (2005). Scopus Export 2000s. 3332.