An adaptive multiscale similarity measure for non-rigid registration

Abstract

Popular intensity-based similarity measures such as (normalized) mutual information estimate statistics over the entire image, neglecting spatial relationships and local image properties. In this work, we present an adaptive multiscale image similarity measure for non-rigid registration which combines image statistics at multiple scales for a multiscale representation of regional image similarities. We validated the proposed similarity measure on simulated and clinical MR brain datasets. Results show that our approach achieves higher registration accuracy and robustness than conventional global measures or their local variations at a single scale. © 2014 Springer International Publishing.