Content-based Image Retrieval for Clinical Applications: An Overview of Current Approaches and Challenges

Digital medical imaging has become one of the most important tools for medical diagnosis. However, the ongoing evolution in both storage and modality devices has had as consequence that enormous amounts of imaging data are being produced. The existence of large sets of data coupled with the limited query capabilities provided by the standard tools and protocols poses problems for radiologists and has shifted the research focus from data availability towards data accessibility. Content-based image retrieval (CBIR) systems have been heralded as a solution that is able to cope with the increasingly larger volumes of information present in medical repositories and assist radiologists with decision support. While generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce, developments are happening at a fast pace and several systems have been implemented with some degree of success. Based on the literature available, we provide an overview of such CBIR systems, architecture and implementation techniques, with an emphasis on systems oriented towards usage in a clinical domain. We conclude with an analysis of some of the challenges that still need to be overcome in order to bring this technology to a medical audience.