Locally-adaptive similarity metric for deformable medical image registration

More and more researchers are beginning to use multiple dis-similarity metrics or image features for medical image reg-istration. In most of these approaches, however, weights for ranking the relative importance between the selected metrics are empirically tuned and fixed for the entire image domain. Different parts of a medical image, however, may contain sig-nificantly different appearance properties such that a metric may only be applicable in certain image regions but less so in other regions. In this paper, we propose to adapt this weight-ing to generate a locally-adaptive set of dissimilarity met-rics such that the overall metric set encourages proper spa-tial alignment. Using contextual information or via a learning procedure, our approach generates a vector weight map that determines, at each spatial location, the relative importance of each constituent of the overall metric. Our approach was eval-uated on 2 datasets of 15 computed tomography (CT) lung images and 40 brain magnetic resonance images (MRI). Ex-periments show that our approach of using a locally-adaptive set of dissimilarity metrics gives superior results when com-pared against its non-region specific variant. 1