S. Antani, L.R. Long, G.R. Thoma, and D.J. Lee (USA)
CBIR, Shape, Similarity, Vertebra, Digitized x-ray
Efficient content-based image retrieval (CBIR) of biomed ical images is a challenging problem. Feature represen tation algorithms used in indexing medical images on the pathology of interest have to address conflicting goals of reducing feature dimensionality while retaining important and often subtle biomedical features. At the Lister Hill Na tional Center for Biomedical Communications, an intramu ral R&D division of the U.S. National Library of Medicine, we are developing CBIR for digitized images of a collec tion of 17,000 cervical and lumbar spine x-rays taken as a part of the second National Health and Nutrition Examina tion Survey (NHANES II). The vertebra shape effectively describes various pathologies identified by medical experts as being consistently and reliably found in the image col lection. A suitable shape algorithm must represent shapes in a low dimension, be invariant to rotation, translation, and scale transforms, and retain relevant pathology. Ad ditionally, supported similarity algorithms must be useful to the intended target community, viz. medical researchers, physicians, etc. This paper describes our research in the development of such a method and a comparison with the state of the art from the literature.
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