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Abstract

Degenerative changes to the cervical spine can be accompanied by neck pain, which can result from narrowing of the intervertebral disc space and growth of osteophytes. In a lateral x-ray image of the cervical spine, degenerative changes are characterized by vertebral bodies that have indistinct boundaries and limited spacing between vertebrae. In this paper, we present a machine learning approach to detect and localize degenerative changes in lateral x-ray images of the cervical spine. Starting from a user-supplied set of points in the center of each vertebral body, we fit a central spline, from which a region of interest is extracted and image features are computed. A Random Forest classifier labels regions as degenerative change or normal. Leave-one-out cross-validation studies performed on a dataset of 103 patients demonstrates performance of above 95% accuracy.

Publication Type:	Article
Additional Information:	Jebri, B., Phillips, M.L., Knapp, K., Appelboam, A., Reuben, A. & Slabaugh, G.G. "Detection of degenerative change in lateral projection cervical spine x-ray images." Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 9414, p. 941404. (Mar 20, 2015) DOI: http://dx.doi.org/10.1117/12.2082515 Copyright 2015 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.
Publisher Keywords:	Cervical Spine, Degenerative Change, Spondylosis, Random Forest
Subjects:	R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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