Abstract

E#cient content-based image retrieval (CBIR) of biomedical images is a challenging problem. Feature representation 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 National Center for Biomedical Communications, an intramural R&D division of the U.S. National Library of Medicine, we are developing CBIR prototype for digitized images of a collection of 17,000 cervical and lumbar spine X-rays taken as a part of the second National Health and Nutrition Examination Survey (NHANES II). The vertebra shape e#ectively describes various pathologies identified by medical experts as being consistently and reliably found in the image collection. A suitable shape algorithm must represent shapes in low dimension, be invariant to rotation, translation, and scale transforms, and retain relevant pathology. Additionally, supported similarity algorithms must be useful in retrieving images that are relevant to the queries posed by the intended target community, viz. medical researchers, physicians, etc. This paper describes an evaluation of two popular shape similarity methods from the literature on a set of 250 vertebra boundary shapes. The polygon approximation method achieved a performance score of 55.94% and bettered the Fourier descriptor algorithm which had a performance score of 46.96%.

Citations