A Hidden Markov model for Bayesian data fusion of multivariate signals

TL;DR

This paper introduces a Bayesian Hidden Markov Model framework for fusing multivariate imaging data to produce a unified segmented image, leveraging MCMC algorithms for implementation.

Contribution

It presents a novel Bayesian approach using HMM and Potts Markov Random Field for data fusion and segmentation of multivariate images.

Findings

Effective data fusion demonstrated through simulations

The method produces coherent segmented images from multiple sources

MCMC implementation successfully applied to real imaging data

Abstract

In this work we propose a Bayesian framework for data fusion of multivariate signals which arises in imaging systems. More specifically, we consider the case where we have observed two images of the same object through two different imaging processes. The objective of this work is then to propose a coherent approach to combine these data sets to obtain a segmented image which can be considered as the fusion result of these two images. 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 is modeled by the Potts Markov Random Field. We propose then an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.