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Size-invariant Descriptors for Detecting Regions of Abnormal Growth in Cervical Vertebrae

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Stanley RJ, Antani S, Long LR, Thoma GR, Gupta K, Das M
Comput Med Imaging Graph. 2008 Jan;32(1):44-52. Epub 2007 Oct 22
Abstract: 

Digitized spinal X-ray images exhibiting specific pathological conditions such as osteophytes can be retrieved from large databases using Content Based Image Retrieval (CBIR) techniques. For efficient image retrieval, it is important that the pathological features of interest be detected with high accuracy. In this study, new size-invariant features were investigated for the detection of anterior osteophytes, including claw and traction in cervical vertebrae. Using a K-means clustering and nearest neighbor classification approach, average correct classification rates of 85.80%, 86.04% and 84.44% were obtained for claw, traction and anterior osteophytes, respectively.

Stanley RJ, Antani S, Long LR, Thoma GR, Gupta K, Das M. Size-invariant Descriptors for Detecting Regions of Abnormal Growth in Cervical Vertebrae Comput Med Imaging Graph. 2008 Jan;32(1):44-52. Epub 2007 Oct 22