Classification of Brain MR Images using Texture Feature Extraction

IJCSEC Front Page

Alzheimer’s disease (AD), is a degenerative disease which leads to memory loss and problems with thinking and behaviour.AD is a type of dementia which accounts for an estimated 60% to 80% of cases. Accurate diagnosis depends on the identification of discriminative features of AD. Recently, different feature extraction methods are used for the classification of AD. In this paper, we proposed a classification framework to select features, which are extracted using Gray-Level Co-occurrence Matrix (GLCM) method to distinguish between the AD and the Normal Control (NC). In order to evaluate the proposed method, we have performed evaluations on the MRI acquiring from the OASIS database. The proposed method yields an average testing accuracy of 75.71% which indicates that the proposed method can differentiate AD and NC satisfactorily.

Keywords: Alzheimer’s Disease classification, feature extraction, GLCM.


  1. Alzheimer’s association, “Basics of Alzheimer’s Disease”, [Online]. Available: [Accessed: Nov. 15, 2016],Oct 2016.
  2. Kim, T. and J. Paik, “Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization”,IEEE Trans. Consumer Electr.,DOI: 10.1109/ TCE.2008.4711238, pp: 1803-1810, 2008.
  3. Sengee, N. and H. Choi, “Brightness Preserving Weight Clustering Histogram Equalization”, IEEE Trans. Consumer Electr.,DOI: 10.1109/ TCE.2008.4637624, pp: 1329-1337,2008.
  4. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., terHaarRomeny, B., Zimmerman, J.B. and Zuiderveld, K., “Adaptive histogram equalization and its variations”. Computer vision, graphics, and image processing, 39(3), pp.355-36, Sep. 1987.
  5. D A. Clausi, “An analysis of co-occurrence texture statistics as a function of grey level Quantization”, Can. J. Remote Sensing, Vol. 28, No. 1, pp 45-62. 2002.
  6. S.S. Keerthi, C.J. Lin, “Asymptotic behaviour of Support Vector Machines with Gaussian Kernel”, Neural Computation,15(7), 1667-1689, 2003.
  7. E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R.E. Johnston, K. Muller, M. P. Braeuning and S. M. Pizer, “Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms,” Journal of Digit Imaging , Vol. 11, No. 4, pp. 193-200,1998.
  8. R.M. Haralick, K. Shanmugam, I. Dinstein, “Textural Features for Image Classification”,IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 6, pp 610–621, 1973.
  9. Bino Sebastian V, A. Unnikrishnan and Kannan Balakrishnan, “Grey Level Co-occurrence Matrices: Generalization and Some New Features”, International Journal of Computer Science,Engineering and Information Technology,Vol. 2, No.2, 2012.
  10. L. Soh and C. Tsatsoulis, “Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices”, IEEE Transactions on Geoscience and Remote Sensing,Vol. 37 , No. 2, pp 780-795, 1999.
  11. F. I. Alam, R. U. Faruqui,“Optimized Calculations of Haralick Texture Features”, European Journal of Scientific Research, Vol. 50 No. 4, pp 543-553, 2011.
  12. C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S. M. Resnick, “Detection of prodromal Alzheimer’s disease via pattern classification of MRI,” Neurobiology of Aging, vol. 29, pp. 514–523, 2008.
  13. J. Zhang, B. Yan, X. Huang, P. Yang, and C. Huang, “The diagnosis of Alzheimer’s disease based on voxel-based morphometry and support vector machine” ,Proceedings of Fourth International Conference on Natural Computation (ICNC2008), vol. 2, Jinan, Shandong province, China, pp. 197–201, 2008.
  14. D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and 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, pp. 1498–1507, 2007.