Content-Based Image Retrieval in Medicine: Retrospective Assessment, State of the Art, and Future Directions

Content-Based Image Retrieval in Medicine: Retrospective Assessment, State of the Art, and Future Directions

L. Rodney Long (National Library of Medicine, NIH, USA), Sameer Antani (National Library of Medicine, NIH, USA), Thomas M. Deserno (RWTH Aachen University, Germany) and George R. Thoma (National Library of Medicine, NIH, USA)
DOI: 10.4018/jhisi.2009010101
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Abstract

Content-based image retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practical medical problems is a goal yet to be realized. Furthermore, we highlight “gaps” between desired CBIR system functionality and what has been achieved to date, present for illustration a comparative analysis of four state-of-the-art CBIR implementations using the gap approach, and suggest that high-priority gaps to be overcome lie in CBIR interfaces and functionality that better serve the clinical and biomedical research communities.

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