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Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis

  • J. Ramírez
  • R. Chaves
  • J. M. Górriz
  • I. Álvarez
  • M. López
  • D. Salas-Gonzalez
  • F. Segovia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. As the number of AD patients has increased, its early diagnosis has received more attention for both social and medical reasons. Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper evaluates different pattern classifiers including k-nearest neighbor (kNN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis (CAD) system for improving the early detection of the AD. The proposed system, yielding a 98.7% AD diagnosis accuracy, reports clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.

Keywords

Support Vector Machine Alzheimer Disease Feedforward Neural Network Coronal Slice Normalize Mean Square Error 
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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References

  1. 1.
    Petrella, J.R., Coleman, R.E., Doraiswamy, P.M.: Neuroimaging and early diagnosis of Alzheimer disease: A look to the future. Radiology 226, 315–336 (2003)CrossRefGoogle Scholar
  2. 2.
    Holman, B.L., Johnson, K.A., Gerada, B., Carvaiho, P.A., Sathn, A.: The scintigraphic appearance of Alzheimer’s disease: a prospective study using Tc-99m HMPAO SPECT. Journal of Nuclear Medicine 33(2), 181–185 (1992)Google Scholar
  3. 3.
    Ramírez, J., Górriz, J.M., López, M., Salas-Gonzalez, D., Álvarez, I., Segovia, F., Puntonet, C.G.: Early detection of the Alzheimer disease combining feature selection and kernel machines. In: ICONIP 2008 Proceedings. LNCS, Springer, Heidelberg (2008)Google Scholar
  4. 4.
    McCulloch, W.S., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Mathematical Biophysics 5, 115–133 (1943)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Rosenblatt, R.: Principles of Neurodynamics. Spartan Books, New York (1962)zbMATHGoogle Scholar
  6. 6.
    Friston, K.J., Ashburner, J., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, London (2007)CrossRefGoogle Scholar
  7. 7.
    Salas-González, D., Górriz, J.M., Ramírez, J., Lassl, A., Puntonet, C.G.: Improved Gauss-Newton optimization methods in affine registration of SPECT brain images. IET Electronics Letters 44(22), 1291–1292 (2008)CrossRefGoogle Scholar
  8. 8.
    Saxena, P., Pavel, D.G., Quintana, J.C., Horwitz, B.: An automatic threshold-based scaling method for enhancing the usefulness of Tc-HMPAO SPECT in the diagnosis of Alzheimers disease. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 623–630. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Stoeckel, J., Malandain, G., Migneco, O., Koulibaly, P.M., Robert, P., Ayache, N., Darcourt, J.: Classification of SPECT images of normal subjects versus images of Alzheimer’s disease patients. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 666–674. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • J. Ramírez
    • 1
  • R. Chaves
    • 1
  • J. M. Górriz
    • 1
  • I. Álvarez
    • 1
  • M. López
    • 1
  • D. Salas-Gonzalez
    • 1
  • F. Segovia
    • 1
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaSpain

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