Concept Based Intermedia Medical Indexing. Application on CLEF Medical Image with UMLS Metathesaurus

Extended Abstract Content Based Medical Image Retrieval (CBMIR) has reached a very challenging threshold, related to the gap between low-level medical image features and the semantic highly specialized medical information and knowledge; to the important context-dependence of the query and navigation; and the wide distribution of the medical data and knowledge. Answers to questions concerning semantic descrip-tors, medical image analysis and report fusion and indexing, context-sensitive navigation and querying are thus still missing today. The medical imaging community has become increasingly aware of the potential benefit of using the new technologies in medical image analysis and retrieval , relating to diagnosis and prognosis assistance, evidence-based medicine and medical case-based reasoning. Besides the growing amount of medical data produced everyday, medical image retrieval systems have good potential in clinical decision making process, where it can be beneficial to find other images of the same modality, of the same anatomic region, and of the same disease. Hence, CBMIR systems can assist doctors in diagnosis by retrieving images with known pathologies that are similar to a patient's image(s). In teaching and research, visual retrieval methods could help researchers, lecturers, and student find relevant images from large repositories. Visual features not only allow the retrieval of cases with patients having similar diagnoses but also cases with visual similarity but different diagnoses. Current Content Based Image Retrieval (CBIR) systems generally use primitive features such as color or texture, or logical features such as object and their relationships to represent images. We believe that the lack of medical knowledge, explains the poor results obtained in the medical domain. More specifically, the description of an image by low-level or medium-level features seems not sufficient