On the Use of Morphometry Based Features for Alzheimer’s Disease Detection on MRI

  • Maite García-Sebastián
  • Alexandre Savio
  • Manuel Graña
  • Jorge Villanúa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)


We have studied feature extraction processes for the detection of Alzheimer’s disease on brain Magnetic Resonance Imaging (MRI) based on Voxel-based morphometry (VBM). The clusters of voxel locations detected by the VBM were applied to select the voxel intensity values upon which the classification features were computed. We have explored the use of the data from the original MRI volumes and the GM segmentation volumes. In this paper, we apply the Support Vector Machine (SVM) algorithm to perform classification of patients with mild Alzheimer’s disease vs. control subjects. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies.


Support Vector Machine Radial Basis Function Kernel Voxel Intensity Feature Extraction Process Magnetic Resonance Image Volume 
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.
    Alvarez, I., Lopez, M., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Segovia, F., Puntonet, C.G.: Automatic classification system for the diagnosis of alzheimer disease using Component-Based SVM aggregations. In: 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly (ICONIP 2008) (2008)Google Scholar
  2. 2.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry: The methods. Neuroimage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  3. 3.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery  2(2), 167, 121 (1998)CrossRefGoogle Scholar
  4. 4.
    Busatto, G.F., Garrido, G.E.J., Almeida, O.P., Castro, C.C., Camargo, C.H.P., Cid, C.G., Buchpiguel, C.A., Furuie, S., Bottino, C.M.: A voxel-based morphometry study of temporal lobe gray matter reductions in alzheimer’s disease. Neurobiology of Aging 24(2), 221–231 (2003)CrossRefGoogle Scholar
  5. 5.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001),
  6. 6.
    Davatzikos, C., Fan, Y., Wu, X., Shen, D., Resnick, S.M.: Detection of prodromal alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging 29(4), 514–523 (2008)CrossRefGoogle Scholar
  7. 7.
    Fan, Y., Shen, D., Davatzikos, C.: Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM, pp. 1–8 (2005)Google Scholar
  8. 8.
    Fotenos, A.F., Snyder, A.Z., Girton, L.E., Morris, J.C., Buckner, R.L.: Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64(6), 1032–1039 (2005)CrossRefGoogle Scholar
  9. 9.
    Frisoni, G.B., Testa, C., Zorzan, A., Sabattoli, F., Beltramello, A., Soininen, H., Laakso, M.P.: Detection of grey matter loss in mild alzheimer’s disease with voxel based morphometry. Journal of Neurology, Neurosurgery & Psychiatry 73(6), 657–664 (2002)CrossRefGoogle Scholar
  10. 10.
    Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr., C.R., Ashburner, J., Frackowiak, R.S.J.: Automatic classification of MR scans in alzheimer’s disease. Brain 131(3), 681 (2008)CrossRefGoogle Scholar
  11. 11.
    Lao, Z., Shen, D., Xue, Z., Karacali, B., Resnick, S.M., Davatzikos, C.: Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 21(1), 46–57 (2004)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M., Carter, C.S., Stenger, V.A., Davis, S., Aizenstein, H., Becker, J.T.: Discriminative MR image feature analysis for automatic schizophrenia and alzheimer’s disease classification. LNCS, pp. 393–401. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    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) PMID: 17714011CrossRefGoogle Scholar
  14. 14.
    Ramirez, J., Gorriz, J.M., Lopez, M., Salas-Gonzalez, D., Alvarez, I., Segovia, F., Puntonet, C.G.: Early detection of the alzheimer disease combining feature selection and kernel machines. In: 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly (ICONIP 2008) (2008)Google Scholar
  15. 15.
    Salas-Gonzalez, D., Gorriz, J.M., Ramirez, J., Lopez, M., Alvarez, I., Segovia, F., Puntonet, C.G.: Computer aided diagnosis of alzheimer disease using support vector machines and classification trees. In: 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly (ICONIP 2008) (2008)Google Scholar
  16. 16.
    Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N., Fox, N.C.: Mapping the evolution of regional atrophy in alzheimer’s disease: Unbiased analysis of fluid-registered serial MRI. Proceedings of the National Academy of Sciences 99(7), 4703 (2002)CrossRefGoogle Scholar
  17. 17.
    Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maite García-Sebastián
    • 1
  • Alexandre Savio
    • 1
  • Manuel Graña
    • 1
  • Jorge Villanúa
    • 2
  1. 1.Grupo de Inteligencia ComputacionalSpain
  2. 2.Osatek, Hospital Donostia Paseo Dr. Beguiristain 109San SebastiánSpain

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