2006 MULTIMODALITY IMAGE REGISTRATION

Abstract

Image registration has been a major area of research in the last few decades. It is a complex problem that involves handling large data sets. The relation between the real object and the image is very intricate and encompasses the object’s shape, surface properties, position, and illumination. In this thesis, a general solution to this problem was devised that can be applied to multimodality and within-modality image registration. The approach followed here is such that various factors such as illumination do not affect the algorithm. The algorithm considers the intensity of the images as raw data for the registration. The method presented in this research applies mutual information (MI) to measure the information redundancy. The redundancy is calculated between the image intensities of corresponding voxels in both images. If the images are aligned with each other closely, then the MI is assumed to be maximal. Maximization of MI is a powerful criterion for image registration. The result of the image registration algorithm is validated for computed tomography and magnetic resonance imaging by comparing with a sub-pixel registration based on intensity. Image registration is obtained automatically, as well as manually, without following any preprocessing steps such as segmentation and feature selection. ii DEDICATION To my parents for making it all possible iii ACKNOWLEDGMENTS The following contributed to the writer’s understanding and supported the research with helpful suggestions and information. Professor Murat M. Tanik, Committee Chair, for assisting in the understanding of entropy and mutual information Drs. Gary Grimes and John Hartman, committee members, for helpful suggestion