Linear intensity-based image registration by Markov random fields and discrete optimization

We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises from the fact that optimizing the energy associated with this problem requires a high-order MRF model. Currently, methods for optimizing such high-order models are less general, easy to use, and efficient, than methods for the popular second-order models. Therefore, we propose an approximation to the original energy by an MRF with tractable second-order terms. The approximation at a certain point p in the parameter space is the normalized sum of evaluations of the original energy at projections of p to two-dimensional subspaces. We demonstrate the quality of the proposed approximation by computing the correlation with the original energy, and show that registration can be performed by discrete optimization of the approximated energy in an iteration loop. A search space refinement strategy is employed over ite...

• Type: Journal
• Year: 2010
• Where: MIA
• Authors: Darko Zikic, Ben Glocker, Oliver Kutter, Martin Groher, Nikos Komodakis, Ali Kamen, Nikos Paragios, Nassir Navab
• Keywords: Intensity-based Registration, Markov Random Fields, MIA 2010, Original Energy