Image Analysis Techniques for Characterizing Disc Space Narrowing in Cervical Vertebrae Interfaces
Image analysis techniques are introduced for evaluating disc space narrowing of cervical vertebrae interfaces from X-ray images. Four scale-invariant, distance transform-based features are presented for characterizing the spacing between adjacent vertebrae. K-means and self-organizing map clustering techniques are applied to estimate the degree of disc space narrowing using a four grade (0-3) scoring system, where 0 and 3 represent normal spacing and significant narrowing, respectively. For a data set of 294 vertebrae interfaces, experimental results yield average correct grade assignment of greater than 82.10% for each of the four grades using a one grade window around the correct grade.
P. Chamarthy et al., "Image Analysis Techniques for Characterizing Disc Space Narrowing in Cervical Vertebrae Interfaces," Computerized Medical Imaging and Graphics, vol. 28, no. 1-2, pp. 39-50, Elsevier, Jan 2004.
The definitive version is available at http://dx.doi.org/10.1016/j.compmedimag.2003.10.001
Electrical and Computer Engineering
Keywords and Phrases
K-means; Cervical Spine Disorders; Degenerative Disk Disease; Disc Space Narrowing; Image Processing; Self-organizing Maps; Cervical vertebrae; Self-organizing maps
International Standard Serial Number (ISSN)
Article - Journal
© 2004 Elsevier, All rights reserved.