Journal of Medical Systems

, Volume 36, Issue 2, pp 995–1000 | Cite as

Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images

  • Mohammad Reza Daliri
Original Paper


In this paper we present an automated method for diagnosing Alzheimer disease (AD) from brain MR images. The approach uses the scale-invariant feature transforms (SIFT) extracted from different slices in MR images for both healthy subjects and subjects with Alzheimer disease. These features are then clustered in a group of features which they can be used to transform a full 3-dimensional image from a subject to a histogram of these features. A feature selection strategy was used to select those bins from these histograms that contribute most in classifying the two groups. This was done by ranking the features using the Fisher’s discriminant ratio and a feature subset selection strategy using the genetic algorithm. These selected bins of the histograms are then used for the classification of healthy/patient subjects from MR images. Support vector machines with different kernels were applied to the data for the discrimination of the two groups, namely healthy subjects and patients diagnosed by AD. The results indicate that the proposed method can be used for diagnose of AD from MR images with the accuracy of %86 for the subjects aged from 60 to 80 years old and with mild AD.


Diagnosis system Alzheimer disease SIFT features MRI SVM 


  1. 1.
    Villain, N., Desgranges, B., Viader, F., De la Sayettte, V., Mezenge, F., Landeau, B., Baron, J.-C., Eustache, F., and Chetelat, G., Relationships between Hippocampal Atrophy, White Matter Disruption, and Grey Matter Hypometabolism in Alzheimer’s disease. J Neurosci 28(24):6174–6181, 2008.CrossRefGoogle Scholar
  2. 2.
    Qiu, A., Fennema-Notestine, C., Dale, A. M., and Miller, M. I., Regional shape abnormalities in mild cognitive impairment and Alzheimer’s Disease. Neuroimage 45(3):656–661, 2009.CrossRefGoogle Scholar
  3. 3.
    Ishii, K., Kawachi, T., Sasaki, H., Kono, A. K., Fukuda, T., Kojima, Y., and Mori, E., Voxel-based morphometric comparison between early- and late-onset mild Alzheimer’ disease and assessment of diagnostic performance of Z score images. Am J Neuroradiol 26:333–340, 2005.Google Scholar
  4. 4.
    Toews, M., Wells, W. M., Collins, D. L., and Arbel, T., Feature-based morphometry: discovering group-related anatomical patterns. Neuroimage 49(3):2318–2327, 2010.CrossRefGoogle Scholar
  5. 5.
    Harris, C., Stephens, M., A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference. pp. 147–151, 1988.Google Scholar
  6. 6.
    Rohr, K., On 3D differential operators for detecting point landmarks. Image Vis Comput 15(3):219–233, 1997.CrossRefGoogle Scholar
  7. 7.
    Lowe, D. G., Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110, 2004.CrossRefGoogle Scholar
  8. 8.
    MacKay, D., Information theory, inference and learning algorithms. Cambridge University Press, 2003.Google Scholar
  9. 9.
    Duda, R. O., Hart, P. E., Stork, D. G., Pattern classification. John Wiley & Sons, 2001.Google Scholar
  10. 10.
    Theodoridis, S., Koutroumbas, K., Pattern recognition, 4th Edition. Academic Press, 2009.Google Scholar
  11. 11.
    Webb, A. R., Statistical pattern recognition, 2nd Edition. John Wiley & Sons, 2002.Google Scholar
  12. 12.
    Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, 1989.Google Scholar
  13. 13.
    Burges, C. J. C., A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167, 1998.CrossRefGoogle Scholar
  14. 14.
    Vapnik, V. N., The nature of statistical learning theory. Springer, 1995.Google Scholar
  15. 15.
    Hsu, C.-W., and Lin, C.-J., A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425, 2002.CrossRefGoogle Scholar
  16. 16.
    Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., and Buckner, R., Open access series of imaging studies (oasis): Cross-sectional MRI data in young, Middle aged, nondemented and demented older adults. J Cogn Neurosci 19:1498–1507, 2007.CrossRefGoogle Scholar
  17. 17.
    Talairach, J., and Tournoux, P., Co-planar stereotaxic atlas of the human brain. Thieme, New York, 1988.Google Scholar
  18. 18.
    Buckner, R., Head, D., Parker, J., Fotenos, A., Marcus, D., Morris, J., and Snyder, A., A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated Atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23:724–738, 2004.CrossRefGoogle Scholar
  19. 19.
    Evgeniou, T., Pontil, M., and Elisseeff, A., Leave one out error, stability, and generalization of voting combinations of classifiers. J Mach Learn 55(1):71–97, 2004.MATHCrossRefGoogle Scholar
  20. 20.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Biomedical Engineering Department, Faculty of Electrical EngineeringIran University of Science and Technology (IUST)TehranIran

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