The great challenge in image registration is to devise computationally efficient algorithms for aligning images
so that their details overlap accurately. The first problem addressed in this thesis is multimodality
medical image registration, which we formulate as an optimization problem in the information-theoretic setting.
We introduce a viable and practical image registration method by maximizing a generalized entropic
dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The
feasibility of the proposed image registration approach is demonstrated through extensive experiments.
The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic
framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton
method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between
the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy
and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in
such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration
accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance
data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein,
we extend our nonrigid registration approach to align diffusion tensor images for multiple components by
enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration
algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image
registration indicate the feasibility of the proposed approach and a much better performance compared to
the affine registration method based on mutual information, not only in terms of registration accuracy in the
presence of geometric distortions but also in terms of robustness in the presence of Rician noise
