Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem
Keyvan Kasiri, David A. Clausi, Paul Fieguth · Vision Letters · 2015
Registration of multi-modal images has been a challenging task due to the complex intensity relationship between images. The standard multi-modal approach tends to use sophisticated similarity measures, such as mutual information, to assess the accuracy of the alignment. Employing such measures imply the increase in the computational time and complexity, and makes it highly difficult for the optimization process to converge. The presented registration method works based on structural representations of images captured from different modalities, in order to convert the multimodal problem into a mono-modal one. Two different representation methods are presented. One is based on a combination of phase congruency and gradient information of the input images, and the other utilizes a modified version of entropy images in a patch-based manner. Sample results are illustrated based on experiments performed on brain images from different modalities.