Abstract
Medical social networking sites have enabled multimedia content sharing in large volumes by allowing physicians and patients to upload their medical images. Moreover, it is necessary to employ new techniques to effectively handle and benefit from them. This huge volume of images needs to formulate new types of queries that pose complex questions to medical social network data-bases. Content-based image retrieval (CBIR) stills an active and efficient research topic to manipulate medical images. To palliate this situation, we propose in this paper the integration of a content-based medical image retrieval method through a medical social network, based on an efficient fusion of low-level visual image features (color, shape and texture features) which offers an efficient and flexible precision. A clear application of our CBIR system consists of providing stored images that are visually similar to a new (undiagnosed) one, allowing specialist and patients to check past exam diagnoses from comments and other physicians' annotations, and to establish, therefore, a new diagnostic or to prepare a new report of an image's exam. Experiments show that the proposed medical image retrieval scheme achieves better performance and accuracy in retrieving images. However, we need also to verify whether our approach is considered by the specialists as a potential aid in a real environment. To do so, we evaluate our methodology's impact in the user's decision, inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed methodology presented a high acceptance regarding the specialists' interests in the clinical practice domain and can improve the decision-making process during analysis.