Content-Based Image Retrieval for Large Biomedical Image Archives

Content-Based Image Retrieval (CBIR) has been a topic of research interest for nearly a decade. Approaches to date use image features for describing content. A survey of the literature shows that progress has been limited to prototype systems that make gross assumptions and approximations. Additionally, research attention has been largely focused on stock image collections. Advances in medical imaging have led to growth in large image collections. At the Lister Hill National Center for Biomedical Communication, an R&D division of the National Library of Medicine, we are conducting research on CBIR for biomedical images. We maintain an archive of over 17,000 digitized x-rays of the cervical and lumbar spine from the second National Health and Nutrition Examination Survey (NHANES II). In addition, we are developing an archive of a large number of digitized 35 mm color slides of the uterine cervix. Our research focuses on developing techniques for hybrid text/image query-retrieval from the survey text and image data. In this paper we present the challenges in developing CBIR of biomedical images and results from our research efforts.