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Automatic Classification of Initial Categories of Alzheimer's Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods

Authors: Ahsan Bin Tufail, Ali Abidi, Adil Masood Siddiqui, Muhammad Shahzad Younis

Abstract:

An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.

Keywords: Biomedical image processing, classification algorithms, feature extraction, statistical learning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084608

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References:


[1] Li S, Shi F, Pu F, Li X, Jiang T, Xie S and Wang Y, "Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods," AJNR Am J Neuroradiol, vol. 28, no. 7, pp. 1339-1345, August 2007.
[2] Zhang D, Wang Y, Zhou L, Yuan H, Shen D and the Alzheimer-s Disease Neuroimaging Initiative, "Multimodal classification of Alzheimer-s disease and mild cognitive impairment," NeuroImage, vol. 55, no. 3, pp. 856-867, April 2011.
[3] An-Tao Du, Norbert Schuff, Joel H. Kramer, Howard J. Rosen, Maria Luisa Gorno-Tempini, Katherine Rankin, Bruce L. Miller and Michael W. Weiner, "Different regional patterns of cortical thinning in Alzheimer-s disease and frontotemporal dementia," Brain, vol. 130, no. 4, pp. 1159-1166, March 2007.
[4] Gerardin E, Chetelat G, Chupin M, Cuingnet R, Desgranges B, Kim HS, Niethammer M, Dubois B, Lehericy S, Garnero L, Eustache F, Colliot O and the Alzheimer-s Disease Neuroimaging Initiative, "Multidimensional classification of hippocampal shape features discriminates Alzheimer-s disease and mild cognitive impairment from normal aging," NeuroImage, vol. 47, no. 4, pp. 1476-1486, October 2009.
[5] Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova, Amity E. Green, Christina Avedissian, Sarah K. Madsen, Neelroop Parikshak, Xue Hua, Arthur W. Toga, Clifford R. Jack, Michael W. Weiner, Paul M. Thompson and the Alzheimer-s Disease Neuroimaging Initiative, "Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer-s disease mild cognitive impairment, and elderly controls," NeuroImage, vol. 43, no. 1, pp. 59-68, October 2008.
[6] Chupin M, Gerardin E, Cuingnet R, Boutet C, Lemieux L, Lehericy S, Benali H, Garnero L, Colliot O and the Alzheimer-s Disease Neuroimaging Initiative, "Fully Automatic Hippocampus Segmentation and Classification in Alzheimer-s Disease and Mild Cognitive Impairment Applied on Data From ADNI," Hippocampus, vol. 19, no. 6, pp. 579-587, June 2009.
[7] Magnin B, Mesrob L, Kinkingnehun S, Pelegrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehericy S and Benali H, "Support vector machine-based classification of Alzheimer-s disease from whole-brain anatomical MRI," Neuroradiology, vol. 51, no. 2, pp. 73-83, February 2009.
[8] Fan Y, Resnick SM, Wu X and Davatzikos C, "Structural and functional biomarkers of prodromal Alzheimer-s disease: A high-dimensional pattern classification study," NeuroImage, vol. 41, no. 2, pp. 277-285, June 2008.
[9] Lao Z, Shen D, Xue Z, Karacali B, Resnick SM and Davatzikos C, "Morphological classification of brains via high-dimensional shape transformations and machine learning methods," NeuroImage, vol. 21, no. 1, pp. 46-57, January 2004.
[10] Sandeep Chaplot, L.M. Patnaik and N.R. Jagannathan, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network," Biomedical Signal Processing and Control, vol. 1, no. 1, pp. 86-92, September 2010.
[11] Stoeckel, J. and Fung, G., "SVM feature selection for classification of SPECT images of Alzheimer-s disease using spatial information," Knowl Inf Syst, vol. 11, no. 2, pp. 243-258, September 2006.
[12] I. Alvarez, J.M. Gorriz, J. Ramirez, D. Salas-Gonzalez, M. Lopez, C.G. Puntonet and F. Segovia, "Alzheimer-s Diagnosis Using Eigenbrains and Support Vector Machines," Electronics Letters, vol. 45, no. 7, pp. 342- 343, March 2009.
[13] M. Lopez, J. Ramirez, J.M. Gorriz, D. Salas-Gonzalez, I. Alvarez, F. Segovia and C.G. Puntonet, "Automatic tool for Alzheimer-s disease diagnosis using PCA and Bayesian classification rules," Electronics Letters, vol. 45, no. 8, pp. 389-391, April 2009.
[14] J. Shane Kippenhan, Warren W. Barker, Shlomo Pascal, Joachim Nagel and Ranjan Duara, "Evaluation of a Neural-Network Classifier for PET Scans of Normal and Alzheimer-s Disease Subjects," The Journal of Nuclear Medicine, vol. 33, no. 8, pp. 1459-1467, August 1992.
[15] Lehmann C, Koenig T, Jelic V, Prichep L, John RE, Wahlund LO, Dodge Y and Dierks T, "Application and comparison of classification algorithms for recognition of Alzheimer-s disease in electrical brain activity (EEG)," Journal of Neuroscience Methods, vol. 161, no. 2, pp. 342-350, April 2007.
[16] Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, Habert MO, Chupin M, Benali H, Colliot O and the Alzheimer-s Disease Neuroimaging Initiative, "Automatic classification of patients with Alzheimer-s disease from structural MRI: A comparison of ten methods using the ADNI database," NeuroImage, vol. 56, no. 2, pp. 766-781, May 2011.
[17] S.R. Amendolia, G. Cossu, M.L. Ganadu, B. Golosio, G.L. Masala and G.M. Mura, "A comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia screening," Chemometrics and Intelligent Laboratory Systems, vol. 69, no. 1-2, pp. 13-20, November 2003.
[18] Daniel S. Marcus, Tracy H. Wang, Jamie Parker, John G. Csernansky, John C. Morris and Randy L. Buckner, "Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults," Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498-1507, 2007.
[19] Lai Xu, Jingyu Liu, Tulay Adali and Vince D. Calhoun, "Source based morphometry using structural MRI phase images to identify sources of gray matter and white matter relative differences in schizophrenia versus controls," in ICASSP, 2008.
[20] Virginia C. Klema and Alan J. Laub, "The Singular Value Decomposition: Its Computation and some Applications," IEEE Transactions on Automatic Control, vol. 25, no. 2, pp. 164-176, April 1980.
[21] Aapo Hyvarinen, "A family of fixed-point algorithms for independent component analysis," in ICASSP, 1997.
[22] Xindong Wu, Vipin Kumar, J. Ross, Quinlan Joydeep, Ghosh Qiang Yang, Hiroshi Motoda, Geoffrey J. Mclachlan, Angus Ng, Bing Liu, Philip S. Yu, Dan Steinberg, X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang and H. Motoda , "Top 10 algorithms in data mining," Knowl Inf Syst, vol. 14, no. 1, pp. 1-37, December 2007.
[23] Paul Honeine and Cedric Richard, "Preimage Problem in Kernel-Based Machine Learning," IEEE Signal Processing Magazine, pp. 77-88, March 2011.
[24] Glenn Fung and Olvi L. Mangasarian, "Proximal Support Vector Machine Classifiers," Knowledge discovery and data mining (KDD), pp. 77-86, 2001.
[25] Xiaojing Long and Chris Wyatt, "An automatic unsupervised classification of MR images in Alzheimer-s disease," in Computer Vision and Pattern Recognition (CVPR), June 2010, pp. 2910-2917.
[26] Martin T. Hagan and Mohammad B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989-993, November 1994.
[27] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Representations by Back-Propagating Errors," Nature, vol. 323, pp. 533-536, October 1986.