FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES

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DEMİRHAN A., Nir T. M., Zavaliangos-Petropulu A., Jack C. R., Weiner M. W., Bernstein M. A., ...Daha Fazla

IEEE 12th International Symposium on Biomedical Imaging, New York, Amerika Birleşik Devletleri, 16 - 19 Nisan 2015, ss.126-130 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/isbi.2015.7163832
  • Basıldığı Şehir: New York
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.126-130
  • Anahtar Kelimeler: diffusion tensor imaging, fractional anisotropy, Alzheimer's disease, voxel-based analysis, support vector machines, MILD COGNITIVE IMPAIRMENT, MRI, CLASSIFICATION, PREDICTION
  • Gazi Üniversitesi Adresli: Evet

Özet

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.