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Student Internships

Image Analysis Techniques for Characterizing Disc Space Narrowing in Cervical Vertebrae Interfaces

Pavan Chamarthya, R. Joe Stanleya, Gregory Cizekb
Rodney Longc, Sameer Antanic, George Thomac
aDepartment of Electrical and Computer Engineering,
University of Missouri-Rolla, Rolla, MO
bExcel Imaging, St. Louis, MO
cCommunications Engineering Branch,
National Library of Medicine, Bethesda, MD

Abstract

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.

Keywords: degenerative disk disease, disc space narrowing, image processing, cervical spine disorders, K-means, self organizing maps.

Please contact Sameer Antani for a full reprint.

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URL: http://archive.nlm.nih.gov/pubs/antani/discSpace/discSpace.php
Last updated February 13, 2004

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