Deformation Based Features for Alzheimer’s Disease Detection with Linear SVM

  • Alexandre Savio
  • Manuel Grańa
  • Jorge Villanúa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)


Detection of Alzheimer’s disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel SVM reach accuracies comparable to previous best results.


Support Vector Machine Feature Vector Mild Cognitive Impairment Magnetic Resonance Image Data Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Savio, A., García-Sebastián, M., Hernández, C., Graña, M., Villanúa, J.: Classification results of artificial neural networks for alzheimer’s disease detection. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 641–648. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Savio, A., García-Sebastián, M., Graña, M., Villanúa, J.: Results of an Adaboost Approach on Alzheimer’s Disease Detection on MRI. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 114–123. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Ashburner, J., Friston, K.J.: Voxel-Based Morphometry: The Methods. Neuroimage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  4. 4.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with Cross-Correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis 12(1), 26–41 (2008)CrossRefGoogle Scholar
  5. 5.
    Bossa, M., Zacur, E., Olmos, S.: Tensor-based morphometry with stationary velocity field diffeomorphic registration: Application to ADNI. NeuroImage 51(3), 956–969 (2010)CrossRefGoogle Scholar
  6. 6.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software (2001),
  7. 7.
    Chyzhyk, D., Graña, M., Savio, A., Maiora, J.: Hybrid Dendritic Computing with Kernel-LICA applied to Alzheimer’s disease detection in MRI. Neurocomputing, (2011) (accepted )Google Scholar
  8. 8.
    Chyzhyk, D., Savio, A.: Feature extraction from structural MRI images based on VBM: data from OASIS database. Technical Report GIC-UPV-EHU-RR-2010-10-14, Grupo de Inteligencia Computacional UPV/EHU (2010)Google Scholar
  9. 9.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. In: Routledge Academic, 2nd edn. (1988)Google Scholar
  10. 10.
    Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.-S., Niethammer, M., Dubois, B., Lehericy, S., Garnero, L., Eustache, F., Colliot, O.: Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. NeuroImage 47(4), 1476–1486 (2009)CrossRefGoogle Scholar
  11. 11.
    Lepore, N., Brun, C., Chou, Y.Y., Chiang, M.C., Dutton, R.A., Hayashi, K.M., Luders, E., Lopez, O.L., Aizenstein, H.J., Toga, A.W., Becker, J.T., Thompson, P.M.: Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors. IEEE Transactions on Medical Imaging 27(1), 129–141 (2008)CrossRefGoogle Scholar
  12. 12.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  13. 13.
    Maritz, J.S.: Distribution-Free Statistical Methods, 2nd edn. Chapman and Hall, Boca Raton ( April 1995)zbMATHGoogle Scholar
  14. 14.
    Plant, C., Teipel, S.J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., Bokde, A.W., Hampel, H., Ewers, M.: Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage 50(1), 162–174 (2010)CrossRefGoogle Scholar
  15. 15.
    Savio, A., García-Sebastián, M., Chyzhyk, D., Hernández, C., Graña, M., Sistiaga, A., Lopez de Munain, A., Villanúa, J.: Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI. Computers in Biology and Medicine (2011) (accepted with revisions)Google Scholar
  16. 16.
    Teipel, S.J., Born, C., Ewers, M., Bokde, A.L.W., Reiser, M.F., Möller, H.-J., Hampel, H.: Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. NeuroImage 38(1), 13–24 (2007)CrossRefGoogle Scholar
  17. 17.
    Wozniak, M., Zmyslony, M.: Designing Fusers on the Basis of Discriminants – Evolutionary and Neural Methods of Training. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 590–597. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Manuel Grańa
    • 1
  • Jorge Villanúa
    • 2
  1. 1.Grupo de Inteligencia ComputacionalSpain
  2. 2.Osatek, Hospital Donostia PaseoSan SebastiánSpain

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