Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

D. Zikic, B. Glocker, O. Kutter, M. Groher, N. Komodakis, A. Kamen, N. Paragios, N. Navab
Markov Random Field Optimization for Intensity-based 2D-3D Registration
SPIE Medical Imaging, San Diego, California, USA, 13-18 February 2010 (bib)

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 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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Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

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