The work covered in this thesis deals with the problem of automatically registering 3D images acquired from different medical imaging modalities. The approach taken is to develop generic measures of image registration derived from the co-occurence of values in the two images. The development of statistical alignment measures is reviewed. The registration problem is then expressed in terms of entropy and developed using tools from information theory. The problem of the optimization of the registration process in the different types of algorithms is identified as important and the power of Genetic Algorithms is applied. The application of image registration techniques, implemented during this thesis, in complex situations is evaluated. The cases of patients with brain ischemia and brain tumour residual disease are elaborated. This is accomplished with the formation of Groupwares where the tacit knowledge, owned by the individual specialists that take part in the collaboration, is exposed and made explicit in the process of the evaluation of the findings, provided by the fused images. This is performed in a high performance computer network that has been developed between the Department of Medicine and the University Hospital. / Η παρούσα εργασία ασχολείται με το πρόβλημα της αυτοματοποιημένης προσαρμογής και σύντηξης τρισδιάστατων απεικονίσεων από διαφορετικές ιατρικές απεικονιστικές μεθοδολογίες.