Title :
Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition
Author :
Liu, Fei ; Xu, Dongxiang ; Yuan, Chun ; Kerwin, William
Author_Institution :
Dept. of Radiol., Washington Univ., Seattle, WA
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
Keywords :
Markov processes; belief networks; biomedical MRI; image segmentation; medical image processing; probability; Bayesian network; Markov random field model; conditional probability density function; in vivo plaque composition; multicontrast MR image; supervised image segmentation; Bayesian methods; Biomedical imaging; Image converters; Image segmentation; In vivo; Markov random fields; Pixel; Probability density function; Radiology; Training data;
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-9576-X
DOI :
10.1109/ISBI.2006.1624872