Tsallis and Renyi's embedded entropy based mutual information for multimodal image registration

Abstract

In this paper, an embedded entropy based image registration scheme has been proposed. Here, Tsallis and Renyi's entropy have been embedded to form a new entropic measure. This parametrized entropy has been used to determine the weighted mutual information (MI) for the CT and MR brain images. The embedded mutual information has been maximized to obtain registration. This notion of embedded mutual information has also been validated in feature space registration. The mutual information with respect to the registration parameter has been found to be a nonlinear curve. It has been found that the feature space registration resulted in higher value mutual information and hence registration process could be smoother. We have used Simulated Annealing algorithm to determine the maximum of this embedded mutual information and hence register the images.

Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach

“…Particularly, it has been extensively and successfully used to measure the intensity relationships in image processing [6] [7]. During the last decade, many researchers have enhanced the accuracy of image registration based on MI, such as Tsallis and Renyi's entropies [8], Jensen-Renyi's entropy [9], hybrid EMPCA-Scott approach [10], and self-similarity α-MI (SeSaMI) [11]. However, the notion of MI alone still has a well-known drawback, i.e., it ignores spatial information [12].…”