Abstract
Alzheimer’s disease (AD) is a progressively neuro-degenerative disorder. 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, a MRI-based classification framework is proposed to differentiate between AD’s patients and 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 Back-propagation artificial neural network (ANN) classifier was trained for AD classification. With the proposed framework, the classification accuracy is reached to 88.27% by only using volumetric features and shape features. And, the result achieved up to 92.17% by using volumetric features and shape features with the PCA.
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References
Vemuri, P., Wiste, H.J., Weigand, S.D., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W., Knopman, D.S., Petersen, R.C., Jack Jr., C.R.: MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology 73(4), 294–301 (2009)
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)
Talairach, J., Tournoux, P.: Co-Planar Stereotaxic Atlas of a Human Brain. In: Dimensional Proprotional System: An Approach to Cerebral Imaging. Georg Thieme Verlag (1988)
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)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)
Wang, J., de Haan, G., Unay, D., Soldea, O., Ekin, A.: Voxel-based discriminant map classification on brain ventricles for Alzheimer’s disease. In: Medical Imaging, vol. 7259 (2009)
de la Escalera, A., Moreno, L.E., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Transactions on Industrial Electronics 44(6), 848–859 (1997)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics. Springer, New York (2002)
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Yang, ST., Lee, JD., Huang, CH., Wang, JJ., Hsu, WC., Wai, YY. (2010). Computer-Aided Diagnosis of Alzheimer’s Disease Using Multiple Features with Artificial Neural Network . In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_72
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DOI: https://doi.org/10.1007/978-3-642-15246-7_72
Publisher Name: Springer, Berlin, Heidelberg
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