12 March 2010 Markov random field optimization for intensity-based 2D-3D registration
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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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Darko Zikic, Ben Glocker, Oliver Kutter, Martin Groher, Nikos Komodakis, Ali Khamene, Nikos Paragios, Nassir Navab, "Markov random field optimization for intensity-based 2D-3D registration", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762334 (12 March 2010); doi: 10.1117/12.837232; https://doi.org/10.1117/12.837232

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