Classification of diffusion tensor images for the early detection of Alzheimer's disease |
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Authors: | Wook Lee Byungkyu Park Kyungsook Han |
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Institution: | 1. School of Computer Science and Engineering, Inha University, Incheon, Republic of Korea;2. Institute for Information and Electronics Research, Inha University, Incheon, Republic of Korea |
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Abstract: | Early detection of Alzheimer's disease (AD) is important since treatments are more efficacious when used at the beginning of the disease. Despite significant advances in diagnostic methods for AD, there is no single diagnostic method for AD with high accuracy. We developed a support vector machine (SVM) model that classifies mild cognitive impairment (MCI) and normal control subjects using probabilistic tractography and tract-based spatial statistics of diffusion tensor imaging (DTI) data. MCI is an intermediate state between normal aging and AD, so finding MCI is important for an early diagnosis of AD. The key features of DTI data we identified through extensive analysis include the fractional anisotropy (FA) values of selected voxels, their average FA value, and the volume of fiber pathways from a pre-defined seed region. In particular, the volume of the fiber pathways to thalamus is the most powerful single feature in classifying MCI and normal subjects regardless of the age of the subjects. The best performance achieved by the SVM model in a 10-fold cross validation and in independent testing was sensitivity of 100%, specificity of 100% and accuracy of 100%. |
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Keywords: | Mild cognitive impairment Diffusion tensor imaging Tractography Tract-based spatial statistics Fiber pathways |
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