Image reference databases (IRDBs) are a recurrent research topic in medical imaging. Most IRDBs are designed to help experienced physicians in diagnostic tasks and require that users have prior extensive knowledge of the field for their use to be fruitful. Therefore, the educational potential of such image collections cannot be exploited thoroughly. In this paper we propose an image-indexing method to extend the functionalities of an existing medical IRDB and allow for its use in educational applications, as well as in computer-assisted diagnosis. Our method, based on the Kahrunen-Leève transform, has been used to develop a content-based search engine for tomographic image databases on which we are presently experimenting and which we aim to integrate into a working radiological IRDB installed at the University of Florence. Results achieved in our preliminary tests are also reported.