Disease evolution visualization through historized versions of medical images

Medical images are fundamental in medical processes particularly in the disease surveillance which is a practice that enables to monitor the evolution of patients´ states. This practice cannot be understood and described only by a current image but requires the observation of image sequences in order to follow up the evolution of the disease from one human body location to another. This work aims to model a data warehouse where images and their related sequences are gathered and analyzed for decision making purposes such as disease evolution surveillance. The images´ features are gathered as intrinsic features representing both the content-based and the description-based descriptors combined to the experts´ annotations. We take into account the various modalities of images with the related temporal relationships which describe the sequence, and the conventional dimensions interfering for the target analysis.