Independent Component Analysis-Based Classification of Alzheimer’s Disease from Segmented MRI Data

  • L. Khedher
  • J. Ramírez
  • J. M. Górriz
  • A. Brahim
  • I. A. Illán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)

Abstract

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance to improve diagnosis techniques, to better understand this neurodegenerative process and to develop effective treatments. In this work, a novel classification method based on independent component analysis (ICA) and supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification task. The ICA-based method is composed of three step. First, MRI are normalized and segmented by the Statistical Parametric Mapping (SPM8) software. After that, average image of normal (NC), mild cognitive impairment (MCI) or AD subjects are computed. Then, FastICA is applied to these different average images for extracting a set of independent components (IC) which symbolized each class characteristics. Finally, each brain image from the database was projected onto the space spanned by this independent components basis for feature extraction, a support vector machine (SVM) is used to manage the classification task. A 87.5% accuracy in identifying AD from NC, with 90.4% specificity and 84.6% sensitivity is obtained. According to the experimental results, we can see that this proposed method can successfully differentiate AD, MCI and NC subjects. So, it is suitable for automatic classification of sMRI images.

Keywords

Alzheimer’s disease Mild cognitive impairment Magnetic resonance imaging Computer aided diagnosis Independent component analysis Support vector machine Supervised learning 

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References

  1. 1.
    Alzheimer’s Association, Alzheimer’s News (2013), http://www.alz.org/news and events facts and figures report.asp
  2. 2.
    Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56, 766–781 (2011)CrossRefGoogle Scholar
  3. 3.
    Illán, I.A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M.M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.G.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences 181, 903–916 (2011)CrossRefGoogle Scholar
  4. 4.
    Illán, I.A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M.M., Segovia, F., Padilla, P., Puntonet, C.G.: Projecting independent components of SPECT images for computer aided diagnosis of Alzheimers disease. Pattern Recognition Letters 31, 1342–1347 (2010)CrossRefGoogle Scholar
  5. 5.
    Duin, R.P.W.: Classifiers in almost empty spaces. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp. 1–7 (2000)Google Scholar
  6. 6.
    Magnin, B., Mesrob, L., Kinkingnehun, S., Pelegrini-Issac, M., Calliot, O., Sarazin, M., Dubais, B., Lehericy, S., Benali, H.: Support vector machine-based classification of alzheimer’s disease from whole-brain anatomical mri. Neuroradiology 51, 73–83 (2009)CrossRefGoogle Scholar
  7. 7.
    Jaramillo, D., Rojas, I., Valenzuela, O., Garcia, I., Prieto, A.: Advanced systems in medical decision-making using intelligent computing. Application to magnetic resonance imaging. In: International Joint Conference on Neural Networks (IJCNN) (2012)Google Scholar
  8. 8.
    Padilla, P., Lopez, M., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D., Alvarez, I.: NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans. Med. Imaging 31, 207–216 (2012)CrossRefGoogle Scholar
  9. 9.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, Pittsburgh (1992)Google Scholar
  10. 10.
    Martinez-Murcia, F.J., Grriz, J.M., Ramrez, J., Moreno-Caballero, M., Gomez-Rio, M.: Parkinson’s Progression Markers Initiative. Parametrization of textural patterns in 123i-ioflupane imaging for the automatic detection of parkinsonism. Medical Physics 41, 012502 (2013)Google Scholar
  11. 11.
    Khedher, L., Ramrez, J., Grriz, J.M., Brahim, A., Segovia, F.: Early diagnosis of Alzheimer’s disease based on Partial Least Squares, Principal Component Analysis and Support Vector Machine using segmented MRI images. Neurocomputing 151, 139–150 (2015)CrossRefGoogle Scholar
  12. 12.
    Chaves, R., Ramrez, J., Grriz, J.M., Lpez, M., Salas-Gonzalez, D., Alvarez, I., Segovia, F.: SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci. Lett. 461, 293–297 (2009)CrossRefGoogle Scholar
  13. 13.
    Ashburner, J., Friston, K.: Human Brain Function (2003)Google Scholar
  14. 14.
    Psychiatry SBMGD, Vbm toolboxes. University of Jena (2013), http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf
  15. 15.
    Ashburner, J., Barnes, G., Chen, C., Daunizeau, J., Flandin, G., Friston, K.: SPM8 manual. In: Functional Imaging Laboratory. Institute of Neurology, London (2012)Google Scholar
  16. 16.
    Stoeckel, J., Ayache, N., Malandain, G., Malick Koulibaly, P., Ebmeier, K.P., Darcourt, J.: Automatic classification of spect images of Alzheimer’s disease patients and control subjects. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 654–662. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Stoeckel, J., Malandain, G., Migneco, O., Malick Koulibaly, P., Robert, P., Ayache, N., Darcourt, J.: Classification of SPECT images of normal subjects versus images of alzheimer’s disease patients. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 666–674. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Khedher, L., Ramrez, J., Grriz, J.M., Brahim, A.: Automatic classification of segmented MRI data combining Independent Component Analysis and Support Vector Machines. In: Innovation in Medicine and Healthcare, InMed, vol. 207. Lecture notes in IOS Press (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • L. Khedher
    • 1
  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • A. Brahim
    • 1
  • I. A. Illán
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain

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