Results of an Adaboost Approach on Alzheimer’s Disease Detection on MRI

  • Alexandre Savio
  • Maite García-Sebastián
  • Manuel Graña
  • Jorge Villanúa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)


In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide the feature extraction processes for the detection of Alzheimer’s disease on brain Magnetic Resonance Imaging (MRI). The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed over the data from the original MRI volumes and from the GM segmentation volumes, using the VBM clusters as voxel selection masks. We use the Support Vector Machine (SVM) algorithm to perform classification of patients with mild Alzheimer’s disease vs. control subjects. We have also considered combinations of isolated cluster based classifiers and an Adaboost strategy applied to the SVM built on the feature vectors. 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. Results are moderately encouraging, as we can obtain up to 85% accuracy with the Adaboost strategy in a 10-fold cross-validation.


Support Vector Machine Feature Vector Radial Basis Function Kernel Voxel Intensity Feature Extraction Process 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Maite García-Sebastián
    • 1
  • Manuel Graña
    • 1
  • Jorge Villanúa
    • 2
  1. 1.Grupo de Inteligencia ComputacionalSpain
  2. 2.OsatekSan SebastiánSpain

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