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Reseach Article

MRI based Techniques for Detection of Alzheimer: A Survey

by Ruaa Adeeb Abdulmunem Al-falluji
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 5
Year of Publication: 2017
Authors: Ruaa Adeeb Abdulmunem Al-falluji
10.5120/ijca2017912929

Ruaa Adeeb Abdulmunem Al-falluji . MRI based Techniques for Detection of Alzheimer: A Survey. International Journal of Computer Applications. 159, 5 ( Feb 2017), 20-24. DOI=10.5120/ijca2017912929

@article{ 10.5120/ijca2017912929,
author = { Ruaa Adeeb Abdulmunem Al-falluji },
title = { MRI based Techniques for Detection of Alzheimer: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 5 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number5/26997-2017912929/ },
doi = { 10.5120/ijca2017912929 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:24.223768+05:30
%A Ruaa Adeeb Abdulmunem Al-falluji
%T MRI based Techniques for Detection of Alzheimer: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 5
%P 20-24
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Alzheimer’s disease(AD) is a neurological disease. It affects memory of the patient. The livelihood of the people that are diagnosed with AD. Magnetic resonance imaging (MRI) is one of the most commonly used imaging modality for the diagnosis of Alzheimer’s. Different features and classifiers that are used Computer Aided Diagnosis (CAD) for diagnosis of Alzheimer’s are presented.

References
  1. P. Morgado, “Automated Diagnosis of Alzheimer’s Disease using PET Images”, MSc thesis at Electrical and Computer Engineering Dep., Higher technical institute, Technical University of Lisbon, September 2012.
  2. C. P. Ferri, R. Sousa, E. Albanense, W. s. Ribeiro, and M. Honyashiki, “World Alzheimer Report 2009,” 2009.
  3. A. Wimo and M. Prince, “World Alzheimer Report 2010: The global economic impact of dementia,” September 2010.
  4. A. Association, “2012 Alzheimer’s disease facts and figures,” Alzheimer’s and Dementia: The Jthenal of the Alzheimer’s Association, vol. 8, no. 2, pp. 131–168, 2012.
  5. http://emedicine.medscape.com/article/336281-overview
  6. A .Hyvarinen “Fast and robust fixed-point algorithms for independent component analysis”. Ieee Transactions on Neural Networks. 1999; 10:626–634.
  7. Yang, Wenlu, et al. "Independent component analysis-based classification of Alzheimer's disease MRI data." Journal of Alzheimer's disease 24.4 (2011): 775-783.
  8. P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min and W. Worek, “Overview of the Face Recognition Grand Challenge,” in Computer vision and pattern recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 947-954.
  9. D. Srinivasulu Asadi, Ch. DV Subba Rao and V. Saik-rishna “A Comparative Study of Face Recognition with Principal Component Analysis and Cross-Correlation Technique,” International Journal of Computer Applica-tions Vol. 10, 2010.
  10. Fermı´n Segovia, Christine Bastin, Eric Salmon, Juan Manuel Go´ rriz, Javier Ramı´rez, Christophe Phillips “Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer’s Disease” PLOS ONE, www.plosone.org, February 2014, Volume 9, Issue 2, e88687.
  11. 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis” I.A. Illán, J.M. Górriz,J. Ramírez, D. Salas-Gonzalez, M.M. López, F. Segovia, R. Chaves, M. Gómez-Rio c, C.G. Puntonet, Information Sciences 181 903–916, 2011.
  12. M. Bartlett, J. Movellan, T. Sejnowski, “Face recognition by independent component analysis”, IEEETransactions on Neural Networks 13 (6) (2002) 1450–1464.
  13. “Independent component analysis of electroencephalographic data in: Advances in Neural Information Processing Systems” S. Makeig, A.J. Bell, T. ping Jung, T.J. Sejnowski, vol. 8, MIT, 1996, pp. 145–151.
  14. Herrera, Luis Javier, et al. "Classification of MRI Images for Alzheimer's Disease Detection." Social Computing (SocialCom), 2013 International Conference on. IEEE, 2013.
  15. Haralick RM, Shanmugam K, Dinstein I. Textural features for.image classification. IEEE Trans. Syst., Man, Cybern. 1973; 6:610-21.
  16. Rajeesh, Jayapathy. "Discrimination of Alzheimer’s disease using hippocampus texture features from MRI." Asian Biomedicine (Research Reviews and News) 6.1 (2012).
  17. Zhu, Xiaofeng, et al. "Multi-view classification for identification of Alzheimer’s disease." International Workshop on Machine Learning in Medical Imaging. Springer International Publishing, 2015.
  18. J. Llonen, J. K. Kamarainen and H. Kalviainen, “Efficient computation of Gabor features,” Research Report 100, Lappeenranta University of Technology, Department of Information Technology, 2005.
  19. Ahmed, Olfa Ben, et al. "Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features." Multimedia Tools and Applications 74.4 (2015): 1249-1266.
  20. Athitsos, V., Alon, J., Sclaroff, S.: Efficient nearest neighbor classification using a cascade of approximate similarity measures. In: CVPR ’05, pp. 486–493. IEEE Computer Society, Washington, DC, USA (2005)
  21. Athitsos, V., Sclaroff, S.: Boosting nearest neighbor classifiers for multiclass recognition. In: CVPR ’05, IEEE Computer Society, Washington, DC, USA (2005)
  22. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
  23. Peng, J., Heisterkamp, D.R., Dai, H.K.: LDA/SVM driven nearest neighbor classification. In: CVPR ’01, p. 58. IEEE Computer Society, Los Alamitos, CA, USA (2001)
  24. Zhang, H., Berg, A.C., Maire, M., Svm-knn, J.M.: Discriminative nearest neighbor classification for visual category recognition. In: CVPR ’06, pp. 2126–2136. IEEE Computer Society, Los Alamitos, CA, USA (2006)
  25. Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
  26. Rish, Irina. "An empirical study of the naive Bayes classifier," IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
  27. P. Doll´ar, V. Rabaud, G. Cottrell, and S. Belongie, ―Be-havior recognition via sparse spatio-temporal features‖, In VS-PETS, 2005.
  28. J. C. Niebles, H. Wang, and L. Fei-Fei, ―Unsupervised learning of human action categories using spatial-temporal words‖, In BMVC, 2006.
  29. Gallant, S.I., 1993. Neural Network Learning and Expert Systems. M.I.T. Press, London, pp 365.
  30. B.-B. Chae, T. Huang, X. Zhuang, Y. Zhao, J. Sklansky, “Piecewise linear classifiers using binary tree structure and genetic algorithm,” Pattern Recognition, Vol.29, No.11, pp.1905-1917, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Alzheimer Magnetic Resonance Imaging (MRI) Independent Component Analysis(ICA) Principal Component Analysis(PCA) Gray Level CoOccurance Matrix(GLCM) Gabor Filter.