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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Communication dans un congrès
SPIE 2010, Medical Imaging 2010: Image Processing, 2010, United States. 2010
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https://hal.archives-ouvertes.fr/hal-00858402
Contributeur : Enzo Ferrante <>
Soumis le : jeudi 5 septembre 2013 - 13:29:33
Dernière modification le : mardi 5 février 2019 - 13:52:14

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  • HAL Id : hal-00858402, version 1

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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. 2010. 〈hal-00858402〉

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