Abstract
Due to the unknown pathogenesis and pathologies of Alzhei-mer’s Disease(AD) that it brings about the serious of social problems, it is urgent to find appropriate technology for early detection of the Alzheimer’s Disease. As a kind of function imaging, FDG-PET images can display lesions distribution of AD through the glucose metabolism in brain, directly reflect lesions of specific areas and the metabolic features, to diagnose and identify AD. In the paper, we propose a novel method combining Independent Component Analysis(ICA) and voxel of interest in PET images for automatic classification of AD vs healthy controls(HC) in ADNI database. The method includes four steps: preprocessing, feature extraction using ICA, selection of voxel of interest, and classification of AD vs healthy controls using Support Vector Machine(SVM). The experimental results show that the proposed method based on ICA is able to obtain the averaged accuracy of 86.78%. In addition, we selected different number of independent component for classification, achieving the average accuracy of classification results with the biggest difference only 1.47%. According to the experimental results, we can see that this method can successfully distinguish AD from healthy controls, so it is suitable for automatic classification of PET 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
Gomez-ISLA, T., Spires, T., De Calignon, A., Hyman, B.T.: Neuropathology of Alzheimer’s Disease. In: Hankbook of Clinical Neurology, Dementias, vol. 89, pp. 234–243 (2008)
Dartigues, J.F.: Altheimer’s disease:a global challenge for the 21st century. Lancel Neurol. 8, 1023–1083 (2009)
Jack Jr., C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Krueger, G., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W.: The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691 (2008)
Sperling, R.: Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer’s disease. Imaging and the Aging Brain 1097, 146–155 (2007)
Walhovd, K.B., Fjell, A.M., Brewer, J., McEvoy, L.K., Fennema-Notestine, C., Hagler Jr., D.J., Jennings, R.G., Karow, D., Dale, A.M.: Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease. AJNR Am. J. Neuroradiol. (2010)
Mosconi, L.: Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. Eur. J. Nucl. Med. Mol. Imaging 32, 486–510 (2005)
Lopez, M., Ramirez, J., Gorriz, J.M., Alvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R., Padilla, P., Gomez-Rio, M.: The Alzheimer’s Disease Neuroimaging Initiative. Principal Component Analysis-Based Techniques and Supervised Classification Schemes for the Early Detection of Alzheimer’s Disease 74, 1260–1271 (2011)
Gorriz, J.M., Ramirez, J., Lassl, A., Salas-Gonzalez, D., Lang, E.W., Puntonet, C.G., Alvarez, I., Lopez, M., Gomez-Rio, M.: Automatic computer aided diagnosis tool using component-based SVM. In: IEEE Nuclear Science Symposium Conference Record, Medical Imaging Conference, pp. 4392–4395 (2008)
Salas-Gonzalez, D., Gorriz, J.M., Ramirez, J., Lopez, M., Illan, I.A., Puntonet, C.G., Gomez-Rio, M.: Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease using moments and support vector machines. Neuroscience Letters 461, 60–64 (2009)
Ramirez, J., Gorriz, J., Salas-Gonzalez, D., Romero, A., Lopez, M., Alvarez, I., Gomez-Rio, M.: Computer-aided diagnosis of alzheimer’s type dementia combining support vector machines and discriminant set of features. Information Sciences 05, 012 (2009)
Friston, K.J., Ashburner, J., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press (2007)
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)
Fink, F., Worle, K., Guber, P., Tome, A.M., Gorriz, J.M., Puntonet, C.G., Lang, E.W.: ICA analysis of retina images for glaucoma classification. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4664–4667 (2008)
Górriz, J.M., Puntonet, C.G., Salmerón, M., Rojas Ruiz, F.: Hybridizing Genetic Algorithms with ICA in Higher Dimension. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 414–421. Springer, Heidelberg (2004)
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)
Kloppel, S., Stonnington, C.M., et al.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(Pt 3), 681–689 (2008)
Ashburner, J.: Computational anatomy with the SPM software. Magnetic Resonance Imaging 27, 1163–1174 (2009)
Calhoun, V., Pekar, J.: When and where are components independent? On the applicability of spatial and temporal ICA to functional MRI data. Hum. Brain Mapp. (2000)
Calhoun, V., et al.: A Method for Making Group Inferences Using Independent Component Analysis of Functional MRI Data: Exploring the Visual System (2001)
Calhoun, V.D., et al.: ICA of Functional MRI Data: An Overview. In: 4th International Symposium on Independent Component Analysis and Blind Signal Seperation (ICA 2003) (2003)
Li, Y.-O., Adal, T.l., Calhoun, V.D.: Estimating the Number of Independent Components for Functional Magnetic Resonance Imaging Data. Hum. Brain Mapp. 28, 1251–1266 (2007)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification (2009)
Yuan, Y., Gu, Z.X., Wei, W.S.: Fluorodeoxyglucose-Positron-Emission Tomography, Single-Photon Emission Tomography, and Structural MR Imaging for Prediction of Rapid Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis. American Journal of Neuroradiology 30(2), 404–410 (2009)
Calhoun, V., Adali, T., Pearlson, G., Pekar, J.: Group ICA of Functional MRI Data: Separability, Stationarity, and Inference (2007)
Lancaster, J.L., Rainey, L.H., Summerlin, J.L., Freitas, C.S., Fox, P.T., Evans, A.C., Toga, A.W., Mazziotta, J.C.: Automated labeling of the human brain: A preliminary report on the development and evaluation of a forwardtransform method. Human Brain Mapping 5(4), 238–242 (1997)
Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping 10, 120–131 (2000)
Illan, I.A., Gorriz, J.M., Ramrez, J., Salas-Gonzalez, D., Lopez, M.M., Segovia, F., et al.: 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences 181(4), 903–916 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wenlu, Y., Fangyu, H., Xinyun, C., Xudong, H. (2011). ICA-Based Automatic Classification of PET Images from ADNI Database. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)