Paper Info
Title
Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions.
Abstract
Nowadays, multi-source image acquisition attracts an increasing interest in many fields, such as multi-modal medical image segmentation. Such acquisition aims at considering complementary information to perform image segmentation, since the same scene has been observed by various types of images. However, strong dependence often exists between multi-source images. This dependence should be taken into account when we try to extract joint information for precisely making a decision. In order to statistically model this dependence between multiple sources, we propose a novel multi-source fusion method based on the Gaussian copula. The proposed fusion model is integrated in a statistical framework with the hidden Markov field inference in order to delineate a target volume from multi-source images. Estimation of parameters of the models and segmentation of the images are jointly performed by an iterative algorithm based on Gibbs sampling. Experiments are performed on multi-sequence MRI to segment tumors. The results show that the proposed method based on the Gaussian copula is effective to accomplish multi-source image segmentation.
Year
DOI
Venue
2017
10.1109/TIP.2017.2685345
IEEE Trans. Image Processing
Keywords
Field
DocType
Hidden Markov models,Image segmentation,Tumors,Magnetic resonance imaging,Bayes methods,Fuses,Probabilistic logic
Computer vision,Scale-space segmentation,Pattern recognition,Copula (linguistics),Segmentation,Copula (probability theory),Segmentation-based object categorization,Image segmentation,Artificial intelligence,Hidden Markov model,Mathematics,Gibbs sampling
Journal
Volume
Issue
ISSN
26
7
1057-7149
Citations 
PageRank 
References 
2
0.36
25
Authors
3
Name
Order
Citations
PageRank
Jérôme Lapuyade-Lahorgue122.39
Jing-Hao Xue239346.48
Ruan Su355953.00