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Abstract #3194

Information Extraction from Raw DTI Data Using Texture Based Analysis: A Preliminary Study of Classification and Regression

Che-Wei Chang1, 2, Chien-Chang Ho1, Jyh-Horng Chen1, 2

1Electrical Engineering, National Taiwan University, Taipei, Taiwan; 2Interdisciplinary MRI/MRS Lab, National Taiwan University, Taipei, Taiwan

This study presents a texture based analysis, Local Binary Pattern on Three Orthogonal Planes (LBP-TOP), to extract effective features from raw DTI data. Examples of sex classification and age estimation were used to demonstrate the performance of this method. A total 204 subject downloaded from NKI/Rockland Samples were used to evaluate those approaches. Our results show that the best sex classification accuracy is 0.81, and the best age estimation mean average error is 6.32 years. We demonstrated that LBP-TOP is capable of extracting effective information from DTI and could be a good candidate for classifying or evaluating neurological diseases based on raw DTI data.