Markov Random Field Optimization for Intensity-based 2D-3D Registration

Abstract : We propose a Markov Random Field (MRF) formulation for the intensity-based N-view 2D-3D registration problem. The transformation aligning the 3D volume to the 2D views is estimated by iterative updates obtained by discrete optimization of the proposed MRF model. We employ a pairwise MRF model with a fully connected graph in which the nodes represent the parameter updates and the edges encode the image similarity costs resulting from variations of the values of adjacent nodes. A label space refinement strategy is employed to achieve sub-millimeter accuracy. The evaluation on real and synthetic data and comparison to state-of-the-art method demonstrates the potential of our approach.
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Contributor : Enzo Ferrante <>
Submitted on : Thursday, September 5, 2013 - 1:29:33 PM
Last modification on : Tuesday, February 5, 2019 - 1:52:14 PM


  • HAL Id : hal-00858402, version 1



Darko Zikic, Ben Glocker, Oliver Kutter, Martin Groher, Nikos Komodakis, et al.. Markov Random Field Optimization for Intensity-based 2D-3D Registration. SPIE 2010, Medical Imaging 2010: Image Processing, 2010, United States. ⟨hal-00858402⟩



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