Abstract: This thesis investigates the employment of different entropic measures, including Rényi entropy, in the context of image registration. Specifically, we focus on the entropy estimation problem for image registration and provide theoretical and experimental comparisons of two important entropy estimators: the plug-in estimator and minimal entropic graphs. We further develop an image registration framework based on the graph-theoretic estimator. Within this framework, we address practical and theoretical issues such as the incorporation of spatial information, the efficient and fast search of the optimum alignment, and the employment of previously aligned image pairs. These analyses yield fast, robust and accurate multi-modal affine registration algorithms applicable to different medical problems. Next, we investigate the nonrigid registration problem and provide a novel fast entropy-based nonrigid registration algorithm. Finally, we discuss a scientific application, the normalization of the human cerebral cortex based on patterns of functional response, and investigate an algorithm that employs a correlation-based entropic measure.