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Evaluation of Shape Indexing Methods for Content-Based Retrieval of X-Ray Images
Efficient content-based image retrieval of biomedical images is a challenging problem of growing research interest. 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, a R&D division of the National Library of Medicine, we are developing a content-based image retrieval system for digitized images of a collection of 17,000 cervical and lumbar x-rays taken as a part of the second National Health and Nutrition Examination Survey (NHANES II). Shape is the only feature that effectively describes various pathologies identified by medical experts as being consistently and reliably found in the image collection. In order to determine if the state of the art in shape representation methods is suitable for this application, we have evaluated representative algorithms selected from the literature. The algorithms were tested on a subset of 250 vertebral shapes. In this paper we present the requirements of an ideal algorithm, define the evaluation criteria, and present the results and our analysis of the evaluation. We observe that while the shape methods perform well on visual inspection of the overall shape boundaries, they fall short in meeting the needs of determining similarity between the vertebral shapes based on the pathology.