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Non-rigid 2D-3D Medical Image Registration Using Markov Random Fields
Enzo Ferrante and Nikos Paragios
Center for Visual Computing, Ecole Centrale de Paris, Franceenzo.ferrante@ecp.fr
nikos.paragios@ecp.fr
http://cvc.centrale-ponts.fr/
Abstract. The aim of this paper is to propose a novel mapping algorithm between 2D images and a 3D volume seeking simultaneously a linear plane transformation and an in-plane dense deformation. We adopt a metric free locally over-parametrized graphical model that combines linear and deformable parameters within a coupled formulation on a 5-dimensional space. Image similarity is encoded in singleton terms, while geometric linear consistency of the solution (common/single plane) and in-plane deformations smoothness are modeled in a pair-wise term. The robustness of the method and its promising results with respect to the state of the art demonstrate the extreme potential of this approach.
Keywords: 2D-3D registration, medical imaging, markov random fields, discrete optimization
LNCS 8151, p. 163 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013