Content-Based Image Retrieval for Advancing Medical Diagnostics, Treatment and Education

Author:

Long L. Rodney1,Antani Sameer1,Thoma George R.1,Deserno Thomas M.2

Affiliation:

1. National Library of Medicine (NIH), USA

2. RWTH Aachen University, Germany

Abstract

Content-Based Image Retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large medical 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 a comparative analysis of four state-of-the-art CBIR implementations using the gap approach for illustration, and suggest that high-priority gaps to be overcome lie in CBIR interfaces and functionality that better serve the clinical and biomedical research communities.

Publisher

IGI Global

Reference28 articles.

1. Interfacing Global and Local CBIR Systems for Medical Image Retrieval

2. Antani, S., Long, L. R., & Thoma, G. (2008). Bridging the gap: Enabling CBIR in medical applications. Proceedings of the 21st International Symposium on Computer-Based Medical Systems (CBMS 2008), (pp. 4-6). University of Jyväskylä, Finland.

3. CBIR Workshop Panel. (2007). Content-based image retrieval for biomedical image archives: Achievements, problems, and prospects. Medical Image Computing and Computer Assisted Intervention (MICCAI 2007). Retrieved from http://www.eng.tau.ac.il/~hayit/MICCAI_CBIR_workshop/

4. CBIR Workshop Panel. (2008). Content-based image retrieval: Major challenges for medical applications. SPIE Medical Imaging 2008. Retrieved from http://archive.nlm.nih.gov/spiemi08/CBIRpanel.php

5. Image retrieval

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