Markov random field optimization for intensity-based 2D-3D registration

Darko Zikic; Ben Glocker; Oliver Kutter; Martin Groher; Nikos Komodakis; Ali Khamene; Nikos Paragios; Nassir Navab

doi:10.1117/12.837232

12 March 2010 Markov random field optimization for intensity-based 2D-3D registration

Darko Zikic, Ben Glocker, Oliver Kutter, Martin Groher, Nikos Komodakis, Ali Khamene, Nikos Paragios, Nassir Navab

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 762334 (2010) https://doi.org/10.1117/12.837232
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

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

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