An Energy-Based Framework for Dense 3D Registration of Volumetric Brain Images

In this paper we describe a new method for medical image registration. The registration is formulated as a minimization problem involving robust estimators. We propose an e cient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to re ne the estimation in speci ed areas. Furthermore we introduce a methodology to constrain the registration with landmarks such as anatomical structures. The performances of this method are objectively evaluated on simulated data and its bene ts are demonstrated on a large database of real acquisitions. keywords Registration, atlas matching, medical imaging, local constraints, incremental optical ow, multigrid minimization, robust estimators.