Hierarchical multimodal image registration based on adaptive local mutual information

Authors:
Dante De Nigris;Laurence Mercier;Rolando Del Maestro;D. Louis Collins;Tal Arbel
Affiliations:
McGill University, Centre for Intelligent Machines;McGill University, Dept. of Biomedical Engineering;Montreal Neurological Institute and Hospital, McGill University;McGill University, Dept. of Biomedical Engineering;McGill University, Centre for Intelligent Machines
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

We propose a new, adaptive local measure based on gradient orientation similarity for the purposes of multimodal image registration. We embed this metric into a hierarchical registration framework, where we show that registration robustness and accuracy can be improved by adapting both the similarity metric and the pixel selection strategy to the Gaussian blurring scale and to the modalities being registered. A computationally efficient estimation of gradient orientations is proposed based on patch-wise rigidity.We have applied our method to both rigid and nonrigidmultimodal registration taskswith differentmodalities.Our approach outperforms mutual information (MI) and previously proposed local approximations of MI for multimodal (e.g. CT/MRI) brain image registration tasks. Furthermore, it shows significant improvements in terms of mTRE over standard methods in the highly challenging clinical context of registering pre-operative brain MRI to intra-operative US images.