Medical images contains valuable information that is not explicit and readable for the machine. For instance, an image may contain information about the anatomy and abnormal structures. However, this kind of information can only be interpreted by a medical domain expert. This paper proposes SMITag, a collaborative semantic annotation tool for medical images that combines features of a DICOM Viewer together with a social network, so that the consensus of domain experts makes easier semantic enrichment tasks, sorting and image retrieval.
