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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 276589, 8 pages
Research Article

A Machine Learning Approach for Specification of Spinal Cord Injuries Using Fractional Anisotropy Values Obtained from Diffusion Tensor Images

Department of Nanobiomedical Science, Dankook University, Cheonan 330-714, Republic of Korea

Received 30 July 2013; Revised 5 November 2013; Accepted 27 November 2013; Published 21 January 2014

Academic Editor: Rong Chen

Copyright © 2014 Bunheang Tay et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Diffusion Tensor Imaging (DTI) uses in vivo images that describe extracellular structures by measuring the diffusion of water molecules. These images capture axonal movement and orientation using echo-planar imaging and provide critical information for evaluating lesions and structural damage in the central nervous system. This information can be used for prediction of Spinal Cord Injuries (SCIs) and for assessment of patients who are recovering from such injuries. In this paper, we propose a classification scheme for identifying healthy individuals and patients. In the proposed scheme, a dataset is first constructed from DTI images, after which the constructed dataset undergoes feature selection and classification. The experiment results show that the proposed scheme aids in the diagnosis of SCIs.