In this paper, we present a Case Based Reasoning (CBR) system for the retrieval of medical cases made up of a series of images with contextual information (such as the patient age, sex and medical history). Indeed, medical experts generally need varied sources of information (which might be incomplete) to diagnose a pathology. Consequently, we derive a retrieval framework from decision trees, ... [Show full abstract] which are well suited to process heterogeneous and incomplete information. To be integrated in the system, images are indexed by their digital content. The method is evaluated on a classified diabetic retinopathy database. On this database, results are promising: the retrieval sensitivity reaches 79.5% for a window of 5 cases, which is almost twice as good as the retrieval of single images alone. As a comparison, the retrieval sensitivity is 52.3% for a standard multimodal case retrieval using a linear combination of heterogeneous distances.