Bootstrapped Dendritic Classifiers in MRI Analysis for Alzheimer’s Disease Recognition

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 246)


This paper presents an intelligent approach to classification analysis of Alzheimer disease patients. Bootstrap technique is chosen to get rid of weak point of Dendritic Classifiers (DC), which is low Specificity and improve the Accuracy at all. Dendritic Classifiers (BDC) is an ensemble of weak DC trained combining their output by majority voting. Weak DCs are trained on bootstrapped samples of the train data setting varying the depth by limit number of trees and varying number of dendrites. The classification accuracies of the combined LICA-DC, Kernel LICA-DC and BDC are compared. The experimental on T1-weighted Magnetic Resonance Imaging (MRI) images indicate that the developed method can significantly improve classification results.


Lattice Computing Alzheimer’s Disease Dendritic Classifiers 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  2. 2.
    Barandiaran, I., Paloc, C., Graña, M.: Real-time optical markerless tracking for augmented reality applications. Journal of Real-Time Image Processing 5(2), 129–138 (2010)CrossRefGoogle Scholar
  3. 3.
    Barmpoutis, A., Ritter, G.X.: Orthonormal basis lattice neural networks. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 331–336 (2006)Google Scholar
  4. 4.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Chyzhyk, D., Graña, M., Savio, A., Maiora, J.: Hybrid dendritic computing with kernel-lica applied to Alzheimer’s disease detection in MRI. Neurocomputing 75(1), 72–77 (2012)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    García-Sebastián, M., Savio, A., Graña, M., Villanúa, J.: On the use of morphometry based features for Alzheimer’s disease detection on MRI. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009, Part I. LNCS, vol. 5517, pp. 957–964. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Ritter, G.X., Schmalz, M.S.: Learning in lattice neural networks that employ dendritic computing. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 7–13 (2006)Google Scholar
  11. 11.
    Ritter, G.X., Urcid, G.: Lattice algebra approach to single-neuron computation. IEEE Transactions on Neural Networks 14(2), 282–295 (2003)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Savio, A., Garcia-Sebastian, M., Chyzhyk, D., Hernandez, C., Graña, M., Sistiaga, A., Lopez de Munain, A., Villanua, J.: Neurocognitive disorder detection based on feature vectors extracted from vbm analysis of structural MRI. Computers in Biology and Medicine 41, 600–610 (2011)CrossRefGoogle Scholar
  13. 13.
    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, Part II. LNCS, vol. 5602, pp. 114–123. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Termenon, M., Graña, M.: A two stage sequential ensemble applied to the classification of Alzheimer’s disease based on mri features. Neural Processing Letters 35(1), 1–12 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Grupo de Inteligencia ComputacionalUPV/EHUSpain

Personalised recommendations