Multi-modal Image Registration by Quantitative-Qualitative Measure of Mutual Information (Q-MI)

Abstract

This paper presents a novel measure of image similarity, called quantitative-qualitative measure of mutual information (Q-MI), for multi-modal image registration. Conventional information measure, i.e., Shannon’s entropy, is a quantitative measure of information, since it only considers probabilities, not utilities of events. Actually, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Therefore, it is important to consider both quantitative and qualitative (i.e., utility) information simultaneously for image registration. To achieve this, salient voxels such as white matter (WM) voxels near to brain cortex will be assigned higher utilities than the WM voxels inside the large WM regions, according to the regional saliency values calculated from scale-space map of brain image. Thus, voxels with higher utilities will contribute more in measuring the mutual information of two images under registration. We use this novel measure of mutual information (Q-MI) for registration of multi-modality brain images, and find that the successful rate of our registration method is much higher than that of conventional mutual information registration method.