A hidden Markov Model for image fusion and their joint segmentation in medical image computing

Details A hidden Markov Model for image fusion and their joint segmentation in medical image computing A hidden Markov Model for image fusion and their joint segmentation in medical image computing

2004-03-31

arxiv.org/abs/physics/0403150v1

Physics

Data Analysis, Statistics and Probability

submitted to MICCAI, St. Malo, France

Scientific paper

In this work we propose a Bayesian framework for fully automated image fusion and their joint segmentation. More specifically, we consider the case where we have observed images of the same object through different image processes or through different spectral bands. The objective of this work is then to propose a coherent approach to combine these data sets and obtain a segmented image which can be considered as the fusion result of these observations. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently, with common hidden classification label variables which are modeled by the Potts Markov Random Field. We propose an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.

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