Using Support Vector Machines with Tract-Based Spatial Statistics for Automated Classification of Tourette Syndrome Children
Wen Hongwei1,2; Yue Liu3,4; Jieqiong Wang1,2; Jishui Zhang3,4; Yun Peng3,4; Huiguang He1,2; He HG(何晖光)
2016-03
Conference NameSPIE Medical Imaging 2016: Computer-Aided Diagnosis
Source PublicationProceedings of SPIE
Volume9785
Pages97852Q-97852Q-9
Conference Date2016-02
Conference PlaceSan Diego, USA
AbstractTourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with 
KeywordTourette Syndrome Dti Tract-based Spatial Statistics Relieff Svm
Subject Area模式识别与智能系统
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14687
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Corresponding AuthorHe HG(何晖光)
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China
4.Beijing key Lab of Magnetic Imaging Device and Technique, Beijing Children’s Hospital, Capital Medical University, Beijing, China
Recommended Citation
GB/T 7714
Wen Hongwei,Yue Liu,Jieqiong Wang,et al. Using Support Vector Machines with Tract-Based Spatial Statistics for Automated Classification of Tourette Syndrome Children[C],2016:97852Q-97852Q-9.
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