Markov Random Field Optimization for Intensity-based 2D-3D Registration

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.     «

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 str...     »