Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, a...

Medical image content-based retrieval entails several possible scenarios. One scenario relates to retrieving based on image landmarks. In this scenario, quantitative image primitives are extracted from the image content, in an extensive pre-processing phase, following which these quantities serve as metadata in the archive, for any future search. A second scenario is one in which image-to-image...

In this paper, we propose an integrated approach for medical image retrieval. In particular, we present a series of experiments in medical image retrieval task. There are three main goals for our participation of this task. First, we will test traditional well-known weighting models used in text retrieval domain, such as BM25, TFIDF and Language Model (LM), for context-based image retrieval. Se...

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 se...

The digital image is probably the most important tools in the medicine since this is used for the diagnosis of the diseases, for drug treatment responses and also manages the diseases of patients, having very few side effects and an effective cost-effective relationship. With the wide growth of medical images database, the statistical analysis of medical images is becoming a big challenge. CBIR...