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Multimedia Tools and Applications

, Volume 74, Issue 4, pp 1249–1266 | Cite as

Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features

  • Olfa Ben AhmedEmail author
  • Jenny Benois-Pineau
  • Michèle Allard
  • Chokri Ben Amar
  • Gwénaëlle Catheline
  • for the Alzheimer’s Disease Neuroimaging Initiative
Article

Abstract

Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer’s disease (AD) diagnosis. This is a normal “knowledge diffusion” process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: “Bordeaux dataset”. The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and “Bordeaux dataset” respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.

Keywords

Content based visual indexing Visual features Circular Harmonic Functions descriptors SVM Bag-of-Visual-Words Late fusion Hippocampus CSF 

Notes

Acknowledgments

This research is supported by the Franco-Tunisian program, the LaBRI, University of Bordeaux 1 and university of Bordeaux 2. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Olfa Ben Ahmed
    • 1
    Email author
  • Jenny Benois-Pineau
    • 1
  • Michèle Allard
    • 1
    • 2
  • Chokri Ben Amar
    • 2
  • Gwénaëlle Catheline
    • 1
    • 2
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Laboratoire Bordelais de Recherche en Informatique, LaBRIUniversity of BordeauxBordeauxFrance
  2. 2.The Aquitaine Institute for Cognitive and Integrative NeuroscienceUniversity of BordeauxBordeauxFrance

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