Abstract
FDG-PET imaging offers the potential for an image-based automated identification of different dementia syndromes. However, various global and local FDG-PET image features have their limitations in characterizing the patterns of this disease. In this paper, we propose an automated approach to identifying the patients with suspected Alzheimer’s disease, patients with frontotemporal dementia and normal controls based on the jointly using a group of global features and three groups of local features extracted from parametric FDG-PET images. In this approach, we employ the genetic algorithm to select the features that have best discriminatory ability, and use the AdaBoost technique to adaptively combine four feature groups in constructing a strong classifier. We compared our approach to other classification methods in 154 clinical FDG-PET studies. Our results show that, with the complementary use of the selected global and local features, the proposed approach can substantially improve the accuracy of FDG-PET imaging-based dementia identification.
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 Disease International: World Alzheimer Report 2009 Executive Summary (2009)
Devous, M.D.: Functional brain imaging in the dementias: role in early detection, differential diagnosis, and longitudinal studies. European Journal of Nuclear Medicine and Molecular Imaging 29, 1685–1696 (2002)
Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12, 189–198 (1975)
Stoeckel, J., Fung, G.: SVM Feature Selection for Classification of SPECT Images of Alzheimer’s Disease using Spatial Information. In: The Fifth IEEE International Conference on Data Mining (ICDM 2005), Houston, Texas, USA (2005)
Silverman, D.H.S.: Brain 18F-FDG PET in the Diagnosis of Neurodegenerative Dementias: Comparison with Perfusion SPECT and with Clinical Evaluations Lacking Nuclear Imaging. The Journal of Nuclear Medicine 45, 594–607 (2004)
Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: Alzheimer’s disease and models of computation: Imaging, classification, and neural models. Journal of Alzheimers Disease 7, 187–199 (2005)
Pagani, M., Kovalev, V.A., Lundqvist, R., Jacobsson, H., Larsson, S.A., Thurfjell, L.: A new approach for improving diagnostic accuracy in Alzheimer’s disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset. European Journal of Nuclear Medicine and Molecular Imaging 30, 1481–1488 (2003)
Nagao, M., Sugawara, Y., Ikeda, M., Fukuhara, R., Hokoishi, K., Murase, K., Mochizuki, T., Miki, H., Kikuchi, T.: Heterogeneity of cerebral blood flow in frontotemporal lobar degeneration and Alzheimer’s disease. European Journal of Nuclear Medicine and Molecular Imaging 31, 162–168 (2004)
Lingfeng, W., Bewley, M., Eberl, S., Fulham, M., Dagan, F.: Classification of dementia from FDG-PET parametric images using data mining. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 412–415 (2008)
Jolliffe, I.T.: Principal Component Analysis. Springer, NY (2002)
Xia, Y., Wen, L., Eberl, S., Fulham, M., Feng, D.: Genetic algorithm-based PCA eigenvector selection and weighting for automated identification of dementia using FDG-PET imaging. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2008, pp. 4812–4815 (2008)
Yin, X.-C., Liu, C.-P., Han, Z.: Feature combination using boosting. Pattern Recognition Letters 26, 2195–2205 (2005)
Eberl, S., Anayat, A.R., Fulton, R.R., et al.: Evaluation of two population-based input functions for quantitative neurological FDG PET studies. European Journal of Nuclear Medicine 24, 299–304 (1997)
Hutchins, G.D., Holden, J.E., Koeppe, R.A., Halama, J.R., Gatley, S.J., Nickles, R.J.: Alternative Approach to Single-scan Estimation of Cerebral Glucose Metabolic Rate Using Glucose Analogs with Particular Application to Ischemia. Journal of Cerebral Blood Flow and Metabolism 4, 35–40 (1984)
Frackowiak, R.S.J., Friston, K.J., Frith, C.D., Dolan, R.J., Price, C.J., Zeki, S., Ashburner, J., Penny, W.: Human Brain Function. Elsevier Academic Press, Amsterdam (2004)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage 15, 273–289 (2002)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)
Houck, C.R., Joines, J.A., Kay, M.G.: A Genetic Algorithm for Function Optimization: A Matlab Implementation (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xia, Y., Zhang, Z., Wen, L., Dong, P., Feng, D.D. (2012). GA and AdaBoost-Based Feature Selection and Combination for Automated Identification of Dementia Using FDG-PET Imaging. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)