Image registration using markov random coefficient fields Conference Paper

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Overview

abstract

• Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multi-modal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on linear intensity transformation functions. The coefficients of these transformations are represented 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 the parameters that control the affine transformation of one of the images and the coefficient fields of the intensity transformations for each pixel. © 2008 Springer-Verlag Berlin Heidelberg.

authors

• Alba Cadena Francisco Alfonso
• Arce Santana Edgar Román

publication date

• 2008-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• Bayesian Estimation; Image Registration; Intensity Transformation Function; Markov Random Fields Bayesian networks; Bioinformatics; Image registration; Linear transformations; Markov processes; Mathematical transformations; Medical imaging; Affine transformations; Bayesian estimations; Bayesian formulation; Linear intensity transformations; Markov Random Fields; Multimodal image registration; Probabilistic approaches; Transformation functions; Image analysis

Identity

Digital Object Identifier (DOI)

• 10.1007/978-3-540-78275-9_27

Additional Document Info

start page

• 306

end page

• 317

volume

• 4958 LNCS