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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alzheimer’s Association, Alzheimer’s News (2013), http://www.alz.org/news and events facts and figures report.asp
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Ashburner, J., Friston, K.: Human Brain Function (2003)
Psychiatry SBMGD, Vbm toolboxes. University of Jena (2013), http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf
Ashburner, J., Barnes, G., Chen, C., Daunizeau, J., Flandin, G., Friston, K.: SPM8 manual. In: Functional Imaging Laboratory. Institute of Neurology, London (2012)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Khedher, L., Ramírez, J., Górriz, J.M., Brahim, A., Illán, I.A. (2015). Independent Component Analysis-Based Classification of Alzheimer’s Disease from Segmented MRI Data. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-18914-7_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18913-0
Online ISBN: 978-3-319-18914-7
eBook Packages: Computer ScienceComputer Science (R0)