Abstract
This paper proposes a novel method of automatic classification of magnetic resonance images based on independent component analysis (ICA). The ICA-based method is composed of three steps. First, all magnetic resonance imaging (MRI) scans are aligned and normalized by statistical parametric mapping. Then FastICA is applied to the preprocessed images for extracting specific neuroimaging components as potential classifying feature. Finally, the separated independent coefficients are fed into a classifying machine that discriminates among Alzheimer’s patients, and mild cognitive impairment, and control subjects. In this study, the MRI data is selected from the Alzheimer’s Disease Neuroimaging Initiative databases. The experimental results show that our method can successfully differentiate subjects with Alzheimer’s disease and mild cognitive impairment from normal controls.
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.
Similar content being viewed by others
References
Jack, C.R., Bernstein, M.A., et al.: The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)
Walhovd, K.B., Fjell, A.M., et al.: Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease. AJNR Am. J. Neuroradiol. (2010)
Jack, C.R., Petersen, R.C., Obrien, P.C., Tangalos, E.G.: MR-based hippocampal volumetry in the diagnosis of Alzheimers-disease. Neurology 42(1), 183–188 (1992)
Ashburner, J., Friston, K.J.: Voxel-based morphometry - The methods. Neuroimage 11(6), 805–821 (2000)
Magnin, B., Mesrob, L., et al.: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2), 73–83 (2009)
Chupin, M., Gerardin, E., et al.: Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6), 579–587 (2009)
Zhou, L., Lieby, P., et al.: Hippocampal shape analysis for Alzheimer’s disease using an efficient hypothesis test and regularized discriminative deformation. Hippocampus 19(6), 533–540 (2009)
Braak, H., Braak, E.: Staging of Alzheimer-related cortical destruction. Int. Psychogeriatr 9(suppl. 1), 257–261 (1997); discussion 69-72
Giesel, F.L., Thomann, P.A., et al.: Comparison of manual direct and automated indirect measurement of hippocampus using magnetic resonance imaging. European Journal of Radiology (66), 268–273 (2008)
Hua, X., Leow, A.D., et al.: Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 43(3), 458–469 (2008)
Xu, L., Pearlson, G., Calhoun, V.D.: Joint source based morphometry identifies linked gray and white matter group differences. Neuroimage 44(3), 777–789 (2009)
Xu, L., Groth, K.M., et al.: Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum. Brain Mapp. 30(3), 711–724 (2009)
Kloppel, S., Stonnington, C.M., et al.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(Pt. 3), 681–690 (2008)
McKeown, M.J., Sejnowski, T.J.: Independent component analysis of fMRI data: Examining the assumptions. Human Brain Mapping 6(5-6), 368–372 (1998)
Marcus, D.S., et al.: Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Talairach, J., Tournoux, P.: Co-planar Stereotaxic Atlas of the Human Brain. Thieme Medical, New York (1988)
Calhoun, V.D., Adali, T., et al.: Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Human Brain Mapping 13(1), 43–53 (2001)
Cortes, C., Vapnik, V.: SUPPORT-VECTOR NETWORKS. Machine Learning 20(3), 273–297 (1995)
Gutman, B., Wang, Y., et al.: Disease classification with hippocampal shape invariants. Hippocampus 19(6), 572–578 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, W., Chen, X., Xie, H., Huang, X. (2010). ICA-Based Automatic Classification of Magnetic Resonance Images from ADNI Data. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-15615-1_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15614-4
Online ISBN: 978-3-642-15615-1
eBook Packages: Computer ScienceComputer Science (R0)