Advertisement

An Ensemble of Classifiers Guided by the AAL Brain Atlas for Alzheimer’s Disease Detection

  • Alexandre Savio
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)

Abstract

Detection of Alzheimer’s disease based on Magnetic Resonance Imaging (MRI) still is one of the most sought goals in the neuroscientific community. Here, we evaluate a ensemble of classifiers each independently trained with disjoint data extracted from a partition of the brain data volumes performed according to the 116 regions of the Anatomical Automatic Labeling (AAL) brain atlas. Grey-matter probability values from 416 subjects (316 controls and 100 patients) of the OASIS database are estimated, partitioned into AAL regions, and summary statistics per region are computed to create the feature sets. Our objective is to discriminate between control subjects and Alzheimer’s disease patients. For validation we performed a leave-one-out process. Elementary classifiers are linear Support Vector Machines (SVM) with model parameter estimated by grid search. The ensemble is composed of one SVM per AAL region, and we test 6 different methods to make the collective decision. The best performance achieved with this approach is 83.6% accuracy, 91.0% sensitivity, 81.3% specificity and 0.86 of area under the ROC curve. Most discriminant regions for some of the collective decision methods are also provided.

Keywords

Support Vector Machine Magnetic Resonance Image Data Linear Support Vector Machine FMRIB Software Library Ensemble Decision 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chyzhyk, D., Graña, M., et al.: Hybrid dendritic computing with kernel-LICA applied to Alzheimer’s disease detection in MRI. Neurocomputing 75(1), 72–77 (2012)CrossRefGoogle Scholar
  2. 2.
    Davatzikos, C., Fan, Y., et al.: Detection of prodromal alzheimer’s disease via pattern classification of MRI. Neurobiology of Aging 29(4), 514–523 (2008); PMID: 17174012 PMCID: 2323584Google Scholar
  3. 3.
    Chen, T., Rangarajan, A., et al.: CAVIAR: Classification via aggregated regression and its application in classifying OASIS brain database. In: Proceedings / IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1337–1340 (April 2010)Google Scholar
  4. 4.
    Liu, M., Zhang, D., et al.: Ensemble sparse classification of alzheimer’s disease. NeuroImage 60(2), 1106–1116 (2012); PMID: 22270352Google Scholar
  5. 5.
    Varol, E., Gaonkar, B., et al.: Feature ranking based nested support vector machine ensemble for medical image classification. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 146–149 (May 2012)Google Scholar
  6. 6.
    Pedregosa, F., Varoquaux, G., et al.: Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, 2825–2830 (2011)MathSciNetGoogle Scholar
  7. 7.
    Marcus, D.S., Wang, T.H., et al.: 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: 17714011Google Scholar
  8. 8.
    Talairach, J., Tournoux, P.: Co-Planar Stereotaxic Atlas of the Human Brain: 3-D Proportional System: An Approach to Cerebral Imaging. Thieme (January 1988)Google Scholar
  9. 9.
    Smith, S.M., Jenkinson, M., et al.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(suppl. 1), S208–S219 (2004); PMID: 15501092Google Scholar
  10. 10.
    Lepore, N., Brun, C., et al.: Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors. IEEE Transactions on Medical Imaging 27(1), 129–141 (1827); PMID: 18270068Google Scholar
  11. 11.
    Zhang, Y., Brady, M., et al.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001); PMID: 11293691Google Scholar
  12. 12.
    Tzourio-Mazoyer, N., Landeau, B., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002); PMID: 11771995Google Scholar
  13. 13.
    Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (September 1998)Google Scholar
  14. 14.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  15. 15.
    Faraggi, D., Reiser, B.: Estimation of the area under the ROC curve. Statistics in Medicine 21(20), 3093–3106 (2002)CrossRefGoogle Scholar
  16. 16.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  17. 17.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  18. 18.
    Savio, A., Graña, M.: Deformation based feature selection for computer aided diagnosis of alzheimer’s disease. Expert Systems with Applications 40(5), 1619–1628 (2013)CrossRefGoogle Scholar
  19. 19.
    Savio, A., García-Sebastián, M.T., et al.: Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI. Computers in Biology and Medicine 41(8), 600–610 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Manuel Graña
    • 1
  1. 1.Grupo de Inteligencia Computacional (GIC)Universidad del País Vasco (UPV/EHU)San SebastiánSpain

Personalised recommendations