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Proceedings Paper

Markov random field optimization for intensity-based 2D-3D registration
Author(s): Darko Zikic; Ben Glocker; Oliver Kutter; Martin Groher; Nikos Komodakis; Ali Khamene; Nikos Paragios; Nassir Navab
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Paper 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.

Paper Details

Date Published: 13 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762334 (13 March 2010); doi: 10.1117/12.837232
Show Author Affiliations
Darko Zikic, Technische Univ. München (Germany)
Ben Glocker, Technische Univ. München (Germany)
Oliver Kutter, Technische Univ. München (Germany)
Martin Groher, Technische Univ. München (Germany)
Nikos Komodakis, Univ. of Crete (Greece)
Ali Khamene, Siemens Corp. Research (United States)
Nikos Paragios, Lab. MAS, Ecole Centrale Paris (France)
Equipe GALEN, INRIA Saclay (France)
Nassir Navab, Technische Univ. München (Germany)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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