Image registration: convex weighting functions for histogram-based similarity measures

Recently the entropy-similarity measure has been introduced for the registration of image pairs prior to subtraction in medical imaging e.g. digital subtraction angiography (DSA). The registration is based on motion-vector fields estimated with a template-matching techniques. The entropy is calculated via weighted grey-value histograms of the difference-image template and measures the degree of histogram dispersion in case of misregistration. In this paper, a generalization of the underlying concept is presented. We prove that any strictly convex function can be used as histogram-weighting function leading to a suitable similarity measure. The quality of the histogram-based measures is compared to other frequently used similarity measures. As a result the energy-similarity measure turns out to be the most suitable measure for template matching. The success of the registration will be demonstrated with a geometrically distorted pair of images taken of the abdomen.