Size-Invariant Descriptors for Detecting Regions of Abnormal Growth in Cervical Vertebrae
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.
R. J. Stanley et al., "Size-Invariant Descriptors for Detecting Regions of Abnormal Growth in Cervical Vertebrae," Computerized Medical Imaging and Graphics, vol. 32, no. 1, pp. 44-52, Elsevier, Jan 2008.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2007.09.002
Electrical and Computer Engineering
Lister Hill National Center for Biomedical Communications
National Institutes of Health (U.S.)
National Library of Medicine
Keywords and Phrases
CBIR; K-Means Clustering; NHANES; Cervical Spine; Claw; Image Processing; Nearest Neighbor Classification; Osteoarthritis; Osteophyte; Traction; X-Ray
International Standard Serial Number (ISSN)
Article - Journal
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