학술논문
Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition
Document Type
Conference
Author
Source
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. Biomedical Imaging: Nano to Macro Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on. :141-144 2006
Subject
Language
ISSN
1945-7928
1945-8452
1945-8452
Abstract
Combining Bayesian network (BN) and Markov random field (MRF) models, this paper presents an effective supervised image segmentation algorithm. Representing information from different features, a Bayesian network generates the probability map for each pixel via the conditional PDF (probability density function) learned from a limited training data set. Considering the spatial relation and a priori knowledge of the image, MRF theory is used to generate a reasonable segmentation by minimizing the proposed energy functional. Applying this algorithm to multi-contrast MR image in vivo plaque composition measurement shows comparable results with expert manual segmentation.