The impact of content-based access to medical images is frequently reported but existing systems are designed for only a particular modality or context of diagnosis. Contrarily, our concept of image retrieval in medical applications (IRMA) aims at a general structure for semantic content analysis that is suitable for numerous applications in case-based reasoning or evidence-based medicine. Within IRMA, stepwise processing results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer) incorporating medical expert knowledge. At the scheme layer, medical images are represented by a hierarchical structure of ellipses (blobs) describing image regions. Hence, image retrieval transforms to graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements. The central database holds program sources, process-ing schemes, images, features, and blob trees; the scheduler balances distributed computing by addressing daemons running on all connected workstations; and the web server provides graphical user interfaces for data entry and retrieval..