Abstract
Alzheimer’s disease (AD) is a progressively neuro-degenerative disorder characterized by symptoms such as memory loss and cognitive degeneration. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish AD’s patients from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a SVM classifier was trained for AD classification. With the proposed framework, the classification accuracy is improved from 73.08% or 76.92%, by only using volumetric features or shape features, to 92.31% by using three kinds of volume features and two kinds of shape features.
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
Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., Martin, R.L.: A new clinical scale for the staging of dementia. The British Journal of Psychiatry, 566–572 (1982)
Folstein, M., 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(3), 189–198 (1975)
Schott, J.M., Price, S.L., Frost, C., Whitwell, J.L., Rossor, M.N., Fox, N.C.: Measuring atrophy in Alzheimer diseases: a serial MRI study over 6 and 12 months. Neurology 65, 119–124 (2005)
Pruessner, J.C., Collins, D.L., Pruessner, M., Evans, A.C.: Age and gender predict volume decline in the anterior and posterior hippocampus. Jornal of Neuroscience 21(1), 194–200 (2001)
Nestor, S., Rupsingh, R., Accomazzi, V., Borrie, M., Smith, M., Wells, J., Bartha, R.: Changes in brain ventricle volume associated with mild cognitive impairment and alzheimer disease in subjects participating in the alzheimer’s disease neuroimaging initiative. Alzheimer’s and Dementia 3, S114 (2007)
Jack Jr, C.R., Shiung, M.M., Gunter, J.L., O’Brien, P.C., Weigand, S.D., Knopman, D.S.: Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600 (2004)
Talairach, J., Tournoux, P.: Co-Planar Stereotaxic Atlas of a Human Brain: Dimensional Proprotional System: An Approach to Cerebral Imaging. Georg Thieme Verlag, New York (1988)
Mazziotta, J.C., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K.: A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). The Royal Society 356(1412), 1293–1322 (2001)
Fritzsche, K.H., von Wangenheim, A., Abdala, D.D., Meinzer, H.P.: A computational method for the estimation of atrophic changes in Alzheimer’s disease and mild cognitive impairment. Computerized Medical Imaging and Graphics 32, 294–303 (2008)
UCL Institute of Neurology. Statistical Parametric Mapping (2005), http://www.fil.ion.ucl.ac.uk/spm/
Wang, J., Ekin, A., de Haan, G.: Shape Analysis of brain ventricles for improved classification of alzheimer’s patients. In: Proceedinds of ICIP, pp. 2252–2255 (2008)
Wang, J., de Haan, G., Unay, D., Soldea, O., Ekin, A.: Voxel-based discriminant map classification on brain ventricles for Alzheimer’s disease. Medical Imaging 7259 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, JD. et al. (2009). Combination of Multiple Features in Support Vector Machine with Principal Component Analysis in Application for Alzheimer’s Disease Diagnosis. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-10684-2_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
eBook Packages: Computer ScienceComputer Science (R0)