• Title/Summary/Keyword: VBM features

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Convergence performance comparison using combination of ML-SVM, PCA, VBM and GMM for detection of AD (알츠하이머 병의 검출을 위한 ML-SVM, PCA, VBM, GMM을 결합한 융합적 성능 비교)

  • Alam, Saurar;Kwon, Goo-Rak
    • Journal of the Korea Convergence Society
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    • v.7 no.4
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    • pp.1-7
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    • 2016
  • Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.