Image registration using Markov random coefficient and geometric transformation fields

Image registration is central to different applications such as medical analysis, biomedical systems, and image guidance. In this paper we propose a new algorithm for multimodal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on coefficient and geometric fields. These coefficients, which represent the local intensity polynomial transformations, as the local geometric transformations, are modeled as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both fields, making the registration between the images possible. © 2008 Elsevier Ltd. All rights reserved.

Bayesian estimation; Elastic registration; Geometric transformation; Intensity transformation; Markov random fields; Multi-modal image registration; Rigid registration Bayesian estimation; Elastic registration; Geometric transformation; Intensity transformation; Markov random fields; Multi-modal image registration; Rigid registration; Bayesian networks; Fourier transforms; Hidden Markov models; Medical imaging; Parameter estimation; Image registration

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