Independent Component Analysis-Based Classification of Alzheimer’s Disease from Segmented MRI Data

  • L. Khedher
  • J. Ramírez
  • J. M. Górriz
  • A. Brahim
  • I. A. Illán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)


An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance to improve diagnosis techniques, to better understand this neurodegenerative process and to develop effective treatments. In this work, a novel classification method based on independent component analysis (ICA) and supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification task. The ICA-based method is composed of three step. First, MRI are normalized and segmented by the Statistical Parametric Mapping (SPM8) software. After that, average image of normal (NC), mild cognitive impairment (MCI) or AD subjects are computed. Then, FastICA is applied to these different average images for extracting a set of independent components (IC) which symbolized each class characteristics. Finally, each brain image from the database was projected onto the space spanned by this independent components basis for feature extraction, a support vector machine (SVM) is used to manage the classification task. A 87.5% accuracy in identifying AD from NC, with 90.4% specificity and 84.6% sensitivity is obtained. According to the experimental results, we can see that this proposed method can successfully differentiate AD, MCI and NC subjects. So, it is suitable for automatic classification of sMRI images.


Alzheimer’s disease Mild cognitive impairment Magnetic resonance imaging Computer aided diagnosis Independent component analysis Support vector machine Supervised learning 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • L. Khedher
    • 1
  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • A. Brahim
    • 1
  • I. A. Illán
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain

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