Classification of Structural MRI for Detecting Alzheimer’s Disease


  • Ayşe Demirhan



Alzheimer’s Disease, neuroimaging, structural MRI, multivariate analysis, image classification, machine learning techniques


Alzheimer’s Disease (AD) is a pathological form of dementia that degenerates brain structures. AD affects millions of elderly people over the world and the number of people with AD doubles every year. Detecting AD years before the effects of disease using structural magnetic resonance imaging (MRI) of the brain is possible. Neuroimaging features that are extracted from the structural brain MRI can be used to predict AD by revealing disease related patterns. Machine learning techniques can detect AD and predict conversions from mild cognitive impairment (MCI) to AD automatically and successfully by using these neuroimaging features. In this study common structural brain measures such as volumes and thickness of anatomical structures that are obtained from The Open Access Series of Imaging Studies (OASIS) and made publicly available by are analysed. State-of-the-art machine learning techniques, namely support vector machines (SVM), k-nearest neighbour (kNN) algorithm and backpropagation neural network (BP-NN) are employed to discriminate AD and mild AD from healthy controls. Training hyperparameters of the classifiers are tuned using classification accuracy which is obtained with 5-fold cross validation. Prediction performance of the techniques are compared using accuracy, sensitivity and specificity. Results of the system revealed that AD can be distinguished from the healthy controls successfully using multivariate morphological features and machine learning tools. According to the performed experiments SVM is the most successful classifier for detecting AD with classification accuracies up to 82%.


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C. R. Jack Jr., M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. J. Britson, J. L. Whitwell, C. Ward, A. M. Dale, J. P. Felmlee, J. L. Gunter, D. L. G. Hill, R. Killiany, N. Schuff, S. Fox-Bosetti, C. Lin, C. Studholme, C. S. DeCarli, G. Krueger, H. A. Ward, G. J. Metzger, K. T. Scott, R. Mallozzi, D. Blezek, J. Levy, J. P. Debbins, A. S. Fleisher, M. Albert, R. Green, G. Bartzokis, G. Glover, J. Mugler, M. W. Weiner, “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685-691, 2008.

R. C. Petersen, “Mild cognitive impairment as a diagnostic entity,” Journal of Internal Medicine, vol. 256, no. 3, pp. 183-194, 2004.

P. M. Thompson, K. M. Hayashi, G. De Zubicaray, A. L. Janke, S. E. Rose, J. Semple, D. Herman, M. S. Hong, S. S. Dittmer, D. M. Doddrell, A. W. Toga, “Dynamics of gray matter loss in Alzheimer's disease,” Journal of Neuroscience, vol. 23, no. 3, pp. 994-1005, 2003.

A. Demirhan, T. M. Nir, A. Zavaliangos-Petropulu, C. R. Jack Jr., W. M. Weiner, M. A. Bernstein, P. M. Thompson, N. Jahanshad, “Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images,” in Proceedings - International Symposium on Biomedical Imaging, 2015, art. no. 7163832, pp. 126-130.

G. B. Frisoni, N. C. Fox, C. R. Jack Jr., P. Scheltens, P. M. Thompson, “The clinical use of structural MRI in Alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, pp. 67-77, 2010.

M. R. Sabuncu, E. Konukoglu, “Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study,” Neuroinformatics, 13 (1), pp. 31-46, 2015.

B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehéricy, H. Benali, “Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI,” Neuroradiology, vol. 51, no. 2, pp. 73-83, 2009.

C. A. Cocosco, A. P. Zijdenbos, A. C. Evans, “A fully automatic and robust brain MRI tissue classification method,” Medical Image Analysis, vol. 7, no. 4, pp. 513-527, Dec. 2003.

N. Amoroso, R. Errico, R. Bellotti, “PRISMA-CAD: Fully automated method for Computer-Aided Diagnosis of Dementia based on structural MRI data,” in Proc MICCAI Workshop Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, 2014, pp. 16–23.

D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, & R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498–1507, Sep. 2007.

R. Casanova, F.-C. Hsu, & M. A. Espeland, Alzheimer’s Disease Neuroimaging Initiative, “Classification of Structural MRI Images in Alzheimer’s Disease from the Perspective of ill-Posed Problems,” PLoS One, vol. 7, no. 10, e44877, 2012.

J. P. Vert, K. Tsuda, B. Schölkopf, “A primer on kernel methods,” in Kernel Methods in Computational Biology, 2nd ed., Cambridge, MA: MIT Press, 2004.

J. Ramírez, J. M. Górriz, D. Salas-Gonzalez, A. Romero, M. López, I. Álvarez, M. Gómez-Río, “Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features,” Information Sciences, vol. 237, no. 10, pp. 59-72, Jul. 2013.

A. Demirhan, Y. A. Kılıç, İ. Güler, “Artificial Intelligence Applications in Medicine,” Turkish Journal of Intensive Care Medicine, vol. 9, no. 1, pp. 31-41, 2010.

Y. Zhang, Z. Dong, L. Wu, S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049-10053, Aug. 2011.

C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,” Department of Computer Science, National Taiwan University, Taipei, Taiwan, Tech. Rep., 2003.




How to Cite

Demirhan, A. (2016). Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering, 195–198. Issue-146973



Research Article