Automatic Classification of Initial Categories of Alzheimer's Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods
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.
[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.
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Benali H, Garnero L, Colliot O and the Alzheimer-s Disease
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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.
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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.
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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.
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Puntonet and F. Segovia, "Alzheimer-s Diagnosis Using Eigenbrains and
Support Vector Machines," Electronics Letters, vol. 45, no. 7, pp. 342-
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diagnosis using PCA and Bayesian classification rules," Electronics
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and Ranjan Duara, "Evaluation of a Neural-Network Classifier for PET
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[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.
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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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:64211", author = "Ahsan Bin Tufail and Ali Abidi and Adil Masood Siddiqui and Muhammad Shahzad Younis", title = "Automatic Classification of Initial Categories of Alzheimer's Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods", 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.", volume = "6", number = "12", pages = "738-5", }
