Effective Diagnosis of Alzheimer’s Disease by Means of Distance Metric Learning and Random Forest

  • R. Chaves
  • J. Ramírez
  • J. M. Górriz
  • I. Illán
  • F. Segovia
  • A. Olivares
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

In this paper we present a novel classification method of SPECT images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer’s Disease (AD). The system combines firstly template-based normalized mean square error (NMSE) features of tridimensional Regions of Interest (ROIs) t-test selected with secondly Kernel Principal Components Analysis (KPCA) to find the main features. Thirdly, aiming to separate examples from different classes (Controls and ATD) by a Large Margin Nearest Neighbors technique (LMNN), distance metric learning methods namely Mahalanobis and Euclidean distances are used. Moreover, the proposed system evaluates Random Forests (RF) classifier, yielding a 98.97% AD diagnosis accuracy, which reports clear improvements over existing techniques, for instance the Principal Component Analysis(PCA) or Normalized Minimum Squared Error (NMSE) evaluated with RF.

Keywords

SPECT Brain Imaging Alzheimer’s disease Distance Metric Learning Kernel Principal Components Analysis Random Forest Support Vector Machines 

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References

  1. 1.
    Evans, D., Funkenstein, H., Albert, M., Scherr, P., Cook, N., Chown, M., Hebert, L., Hennekens, C., Taylor, J.: Prevalence of Alzheimer’s disease in a Community Population of older persons. Journal of the American Medical Association 262(18), 2551 (1989)CrossRefGoogle Scholar
  2. 2.
    Petrella, J.R., Coleman, R.E., Doraiswamy, P.M.: Neuroimaging and Early Diagnosis of Alzheimer’s Disease: A Look to the Future. Radiology 226, 315–336 (2003)CrossRefGoogle Scholar
  3. 3.
    English, R.J., Childs, J.: SPECT: Single-Photon Emission Computed Tomography: A Primer. Society of Nuclear Medicine (1996)Google Scholar
  4. 4.
    Fung, G., Stoeckel, J.: SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowledge and Information Systems 11(2), 243–258 (2007)CrossRefGoogle Scholar
  5. 5.
    Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. Journal of Machine Learning Research 10, 207–244 (2009)MATHGoogle Scholar
  6. 6.
    Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    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 alzheimer#146s 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
  8. 8.
    Chaves, R., Ramírez, J., Górriz, J.M., López, M., Salas-Gonzalez, D., Alvarez, I., Segovia, F.: SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neuroscience Letters 461, 293–297 (2009)CrossRefGoogle Scholar
  9. 9.
    Andersen, A.H., Gash, D.M., Avison, M.J.: Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Journal of Magnetic Resonance Imaging 17, 795–815 (1999)CrossRefGoogle Scholar
  10. 10.
    López, M., Ramírez, J., Górriz, J.M., Alvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R.: SVM-based CAD system for early detection of the Alzheimer’s disease using kernel PCA and LDA. Neuroscience Letters 464(3), 233–238 (2009)CrossRefGoogle Scholar
  11. 11.
    Chatpatanasiri, R., Korsrilabutr, T., Tangchanachaianan, P., Kijsirikul, B.: A new kernelization framework for Mahalanobis distance learning algorithms. Neurocomputing 73, 1570–1579 (2010)CrossRefGoogle Scholar
  12. 12.
    Xiang, S., Nie, F., Zhang, C.: Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition 41, 3600–3612 (2008)CrossRefMATHGoogle Scholar
  13. 13.
    Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large Margin Methods for Structured and Interdependent Output Variables. Journal of Machine Learning Research 6, 1453–1484 (2005)MathSciNetMATHGoogle Scholar
  14. 14.
    Ramírez, J., Górriz, J.M., Chaves, R., López, M., Salas-Gonzalez, D., Alvarez, I., Segovia, F.: SPECT image classification using random forests. Electronic Letters 45(12) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Chaves
    • 1
  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • I. Illán
    • 1
  • F. Segovia
    • 1
  • A. Olivares
    • 1
  1. 1.University of GranadaGranadaSpain

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