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. © 2013 Springer-Verlag.
CITATION STYLE
Ferrante, E., & Paragios, N. (2013). Non-rigid 2D-3D medical image registration using Markov random fields. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8151 LNCS, pp. 163–170). https://doi.org/10.1007/978-3-642-40760-4_21
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