Ensemble Tree Learning Techniques for Magnetic Resonance Image Analysis

  • Javier RamírezEmail author
  • Juan M. Górriz
  • Andrés Ortiz
  • Pablo Padilla
  • Francisco J. Martínez-Murcia
  • for the Alzheimer Disease Neuroimaging Initiative
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 45)


This paper shows a comparative study of boosting and bagging algorithms for magnetic resonance image (MRI) analysis and classification and the early detection of Alzheimer’s disease (AD). The methods evaluated are based on a feature extraction process estimating first-order statistics from gray matter (GM) segmented MRI for a number of subcortical structures, and a learning process of an ensemble of decision trees. Several experiments were conducted in order to compare the performance of the generalization ability of the ensemble learning algorithms for different complexity classification tasks. The generalization error converges to a limit as the number of trees in the ensemble becomes large for boosting and bagging. It depends on the strength of the individual trees in the forest and the correlation between them. Bagging outperforms boosting algorithms in terms of classification error and convergence rate. The improvement of bagging over boosting techniques increases with the complexity of the classification task. Thus, bagging is better suited for discrimination of mild cognitive impairment (MCI) from healthy controls or AD subjects than boosting techniques.


Mild Cognitive Impairment Classification Task Receiver Operating Characteristic Clinical Dementia Rating Ensemble Learning Method 
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.



This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( 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 California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier Ramírez
    • 1
    Email author
  • Juan M. Górriz
    • 1
  • Andrés Ortiz
    • 1
  • Pablo Padilla
    • 1
  • Francisco J. Martínez-Murcia
    • 1
  • for the Alzheimer Disease Neuroimaging Initiative
  1. 1.Department of Signal TheoryTelematics and Communications University of GranadaGranadaSpain

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