, Volume 51, Issue 2, pp 73–83 | Cite as

Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI

  • Benoît Magnin
  • Lilia Mesrob
  • Serge Kinkingnéhun
  • Mélanie Pélégrini-IssacEmail author
  • Olivier Colliot
  • Marie Sarazin
  • Bruno Dubois
  • Stéphane Lehéricy
  • Habib Benali
Diagnostic Neuroradiology



We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer’s disease (AD) and elderly control subjects.

Materials and methods

We studied 16 patients with AD [mean age ± standard deviation (SD) = 74.1 ± 5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3 ± 5.0 years, MMSE = 28.5 ± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results.


We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%).


Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.


Alzheimer’s disease Diagnosis Magnetic resonance image Support vector machine Sensitivity Specificity 


Conflict of interest statement

S. Kinkingnéhun has a financial relationship with e(ye)BRAIN. B. Dubois and H. Benali consult for e(ye)BRAIN.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Benoît Magnin
    • 1
    • 2
    • 3
    • 4
  • Lilia Mesrob
    • 2
    • 3
    • 4
  • Serge Kinkingnéhun
    • 2
    • 3
    • 4
    • 5
  • Mélanie Pélégrini-Issac
    • 1
    • 3
    • 4
    Email author
  • Olivier Colliot
    • 4
    • 6
  • Marie Sarazin
    • 2
    • 3
    • 4
    • 7
  • Bruno Dubois
    • 2
    • 3
    • 4
    • 7
  • Stéphane Lehéricy
    • 2
    • 3
    • 4
    • 8
    • 9
  • Habib Benali
    • 1
    • 3
    • 4
    • 10
  1. 1.UMR-S 678, InsermParisFrance
  2. 2.UMR-S 610, InsermParisFrance
  3. 3.Faculté de médecine Pitié-SalpêtrièreUMPC Univ Paris 06ParisFrance
  4. 4.IFR 49Gif-sur-YvetteFrance
  5. 5.e(ye)BRAINVitry-sur-SeineFrance
  6. 6.UPR 640 LENA, CNRSParisFrance
  7. 7.Department of NeurologyPitié-Salpêtrière HospitalParisFrance
  8. 8.Center for NeuroImaging Research–CENIRUMPC Univ Paris 06ParisFrance
  9. 9.Department of NeuroradiologyPitié-Salpêtrière HospitalParisFrance
  10. 10.UNF/CRIUGM, Université de MontréalMontréalCanada

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