Point similarity measures for non-rigid registration of multi-modal data

High-dimensional non-rigid registration of multi-modal data requires similarity measures with two important properties: multi-modality and locality. Unfortunately all commonly used multi-modal similarity measures are inherently global and cannot operate on small image regions. In this paper, we propose a new class of multi-modal similarity measures, which are constructed from information of the whole images but can be applied pointwise. Due to their capability of measuring correspondence for individual image points we call them point similarity measures. Point similarity measures can be derived from global measures and enable detailed relative comparison of local image correspondence. We present a set of multi-modal point similarity measures based on joint intensity distribution and test them as an integral part of non-rigid multi-modal registration system. The comparison results show that segmentation-based measure, which models the joint distribution as a sum of intensity classes, performs best. When intensity classes do not exist or cannot be accurately modeled, each intensity pair can be treated as a separate class, which results in a more general measure, suitable for various non-rigid registration tasks.