The accumulation of several data coming from medical images and signals, expert knowledge and databases is becoming very common for the study of a given pathology. The aggregation of all this information is mentally performed by a clinician, and generally allows for a better medical decision in clinical studies. We propose in this article a fusion method that models this aggregation process. This method is a three step scheme, that first transforms all the available information in a common theoretical frame, then aggregates these data using their redundancy and their conflicts, and finally computes a new information synthesizing all the initial knowledge. We first introduce in this article the fusion scheme and its theoretical aspects, and we particularly focus on the three steps of the process. We then detail the software implementation of this concept, achieved in collaboration with SEGAMI Corporation Inc. We finally apply this concept to a real clinical problem, the study of Alzheimer's disease using MR and SPECT images, and we show very encouraging preliminary results.