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Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions

Lapuyade-Lahorgue, J; Xue, J-H; Ruan, S; (2017) Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions. IEEE Transactions on Image Processing , 26 (7) pp. 3187-3195. 10.1109/TIP.2017.2685345. Green open access

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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.

Type: Article
Title: Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIP.2017.2685345
Publisher version: http://doi.org/10.1109/TIP.2017.2685345
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Hidden Markov models, Image segmentation, Tumors, Magnetic resonance imaging, Bayes methods, Fuses, Probabilistic logic
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1557403
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