%0 Journal Article
%T A Machine Learning Approach for Specification of Spinal Cord Injuries Using Fractional Anisotropy Values Obtained from Diffusion Tensor Images
%A Bunheang Tay
%A Jung Keun Hyun
%A Sejong Oh
%J Computational and Mathematical Methods in Medicine
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/276589
%X 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. 1. Introduction The Spinal Cord (SC) is a major pathway for motor and sensory signals traveling between the brain and the peripheral nervous system. The SC, along with the brain, comprises the central nervous system. It is tubular in shape and contains white matter (spinal tracks) and gray matter (neuronal cell bodies). When a Spinal Cord Injury (SCI) occurs, the spinal tracks, which convey sensory, motor, and autonomic signals between the brain and organs, are disrupted. An SCI may cause patients to become paralyzed or stop organs from functioning properly. The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) motor and sensory scores enable us to assess SCI patients more precisely. These scores were developed by the American Spinal Injury Association (ASIA) in 1982 and updated several times [1¨C3]. They are correlated with functional status, and they are essential to arriving at a prognosis for SCI patients in clinical rehabilitation units [4]. However, clinical assessment based on ISNCSCI scores has limitations for accurate diagnosis of SCIs. It includes unclear information when there are concomitant injuries in other organs, and it is also somewhat subjective as it relies on information relayed by the patient. Conventional Magnetic Resonance Imaging (MRI) is widely used to diagnose SCI. MRI is a medical imaging technology that produces high-quality images of organs and tissues at the macroscopic level. It utilizes a black and white contrast image to differentiate between soft and hard tissues. Diffusion Tensor Imaging (DTI) is an advanced technology that utilizes echo-planar images obtained from MRI. It maps the diffusion of water molecules in the brain and SC tissue according to their tissue structure and architecture
%U http://www.hindawi.com/journals/cmmm/2014/276589/