volume | PIER Journals

Abstract

In this paper we proposed a novel classification system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC). The method employed the magnetic resonance imaging (MRI) data of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. First, all these three dimensional (3D) MRI images were preprocessed with atlasregistered normalization. Then, gray matter images were extracted and the 3D images were undersampled. Afterwards, principle component analysis was applied for feature extraction. In total, 20 principal components (PC) were extracted from 3D MRI data using singular value decomposition (SVD) algorithm, and 2 PCs were extracted from additional information (consisting of demographics, clinical examination, and derived anatomic volumes) using alternating least squares (ALS). On the basic of the 22 features, we constructed a kernel support vector machine decision tree (kSVM-DT). The error penalty parameter C and kernel parameter σ were determined by Particle Swarm Optimization (PSO). The weights ω and biases b were still obtained by quadratic programming method. 5-fold cross validation was employed to obtain the out-of-sample estimate. The results show that the proposed kSVM-DT achieves 80% classification accuracy, better than 74% of the method without kernel. Besides, the PSO exceeds the random selection method in choosing the parameters of the classifier. The computation time to predict a new patient is only 0.022s.

1. Hahn, K., et al. "Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease --- Revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence," NeuroImage, Vol. 81, 96-109, 2013.
doi:10.1016/j.neuroimage.2013.05.011

2. Brookmeyer, R., et al. "Forecasting the global burden of Alzheimer's disease," Alzheimers Dement, Vol. 3, No. 3, 186-191, 2007.
doi:10.1016/j.jalz.2007.04.381

3. Chen, X., W. Yang, and X. Huang, "ICA-based classification of MCI vs HC," 2011 Seventh International Conference on Natural Computation (ICNC), Vol. 3, 1658-1662, 2011.
doi:10.1109/ICNC.2011.6022275

4. Kubota, T., Y. Ushijima, and T. Nishimura, "A region-of-interest (ROI) template for three-dimensional stereotactic surface projection (3D-SSP) images: Initial application to analysis of Alzheimer disease and mild cognitive impairment ," International Congress Series, Vol. 1290, 128-134, 2006.
doi:10.1016/j.ics.2005.11.104

5. Pennanen, C., et al. "Hippocampus and entorhinal cortex in mild cognitive impairment and early AD," Neurobiology of Aging, Vol. 25, No. 3, 303-310, 2004.
doi:10.1016/S0197-4580(03)00084-8

6. Lee, W., B. Park, and K. Han, "Classification of diffusion tensor images for the early detection of Alzheimer's disease," Computers in Biology and Medicine, Vol. 43, No. 10, 1313-1320, 2013.
doi:10.1016/j.compbiomed.2013.07.004

7. Lopez, M. M., et al. "SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA," Neuroscience Letters, Vol. 464, No. 3, 233-238, 2009.
doi:10.1016/j.neulet.2009.08.061

8. Camacho, J., J. Pico, and A. Ferrer, "Corrigendum to `the best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter?'," Anal. Chim. Acta, Vol. 642, 59-68, 2009.
doi:10.1016/j.aca.2009.02.001

9. Ortiz, A., et al. "LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease," Pattern Recognition Letters, Vol. 34, No. 14, 1725-1733, 2013.
doi:10.1016/j.patrec.2013.04.014

10. Ardekani, B. A., K. Figarsky, and J. J. Sidtis, "Sexual dimorphism in the human corpus callosum: An MRI study using the OASIS brain database," Cereb Cortex, Vol. 10, No. 25, 2514-2520, 2012.

11. Ardekani, B. A., et al. "Corpus callosum shape changes in early Alzheimer's disease: An MRI study using the OASIS brain database," Brain Struct. Funct., Vol. 219, No. 1, 343-352, 2013.
doi:10.1007/s00429-013-0503-0

12. Bin Tufail, A., et al. "Multiclass classification of initial stages of Alzheimer's disease using structural MRI phase images ," 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 317-321, 2012.
doi:10.1109/ICCSCE.2012.6487163

13. "What is OASIS? OASIS: Cross-sectional MRI data in young, middle aged, nondemented and demented older adults 2013,".
doi:http://www.oasis-brains.org/

14. MÄoller, C., et al. "Different patterns of gray matter atrophy in early- and late-onset Alzheimer's disease," Neurobiology of Aging, Vol. 34, No. 8, 2014-2022, 2013.
doi:10.1016/j.neurobiolaging.2013.02.013

15. Alexander, G. E., et al. "Gray matter network associated with risk for Alzheimer's disease in young to middle-aged adults," Neurobiology of Aging, Vol. 33, No. 12, 2723-2732, 2012.
doi:10.1016/j.neurobiolaging.2012.01.014

16. Smith, S. M., "Fast robust automated brain extraction," Human Brain Mapping, Vol. 17, No. 3, 143-155, 2002.
doi:10.1002/hbm.10062

17. Kuslansky, G., et al. "Detecting dementia with the Hopkins verbal learning test and the minimental state examination," Archives of Clinical Neuropsychology, Vol. 19, No. 1, 89-104, 2004.

18. Maxeiner, H. and M. Behnke, "Intracranial volume, brain volume, reserve volume and morphological signs of increased intracranial pressure | A post-mortem analysis," Legal Medicine, Vol. 10, No. 6, 293-300, 2008.
doi:10.1016/j.legalmed.2008.04.001

19. Buckner, R. L., et al. "A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: Reliability and validation against manual measurement of total intracranial volume," NeuroImage, Vol. 23, No. 2, 724-738, 2004.
doi:10.1016/j.neuroimage.2004.06.018

20. Fotenos, A. F., et al. "Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD," Neurology, Vol. 64, No. 6, 1032-1039, 2005.
doi:10.1212/01.WNL.0000154530.72969.11

21. Williams, M. M., et al. "Progression of Alzheimer's disease as measured by clinical dementia rating sum of boxes scores," Alzheimer's & Dementia, Vol. 9, No. 1, S39-S44, 2013.
doi:10.1016/j.jalz.2012.01.005

22. Marcus, D. S., et al. "Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults," J. Cogn. Neurosci., Vol. 19, No. 9, 1498-1507, 2007.
doi:10.1162/jocn.2007.19.9.1498

23. Zhang, Y. and L. Wu, "An MR brain images classi¯er via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.
doi:10.2528/PIER12061410

24. Gass, S. I. and T. Rapcsak, "Singular value decomposition in AHP," European Journal of Operational Research, Vol. 154, No. 3, 573-584, 2004.
doi:10.1016/S0377-2217(02)00755-5

25. Rajendra Acharya, U., et al. "Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework," Expert Systems with Applications, Vol. 39, No. 10, 9072-9078, 2012.
doi:10.1016/j.eswa.2012.02.040

26. Kuroda, M., et al. "Acceleration of the alternating least squares algorithm for principal components analysis," Computational Statistics & Data Analysis, Vol. 55, No. 1, 143-153, 2011.
doi:10.1016/j.csda.2010.06.001

27. Cuingnet, R., et al. "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, 766-781, 2011.
doi:10.1016/j.neuroimage.2010.06.013

28. Arun Kumar, M. and M. Gopal, "A hybrid SVM based decision tree," Pattern Recognition, Vol. 43, No. 12, 3977-3987, 2010.
doi:10.1016/j.patcog.2010.06.010

29. Xu, Z., P. Li, and Y. Wang, "Text classifier based on an improved SVM decision tree," Physics Procedia, Vol. 33, 1986-1991, 2012.
doi:10.1016/j.phpro.2012.05.312

30. Nasseri, M., H. Tavakol-Davani, and B. Zahraie, "Performance assessment of different data mining methods in statistical downscaling of daily precipitation," Journal of Hydrology, Vol. 492, 1-14, 2013.
doi:10.1016/j.jhydrol.2013.04.017

31. Acevedo-Rodriguez, J., et al. "Computational load reduction in decision functions using support vector machines," Signal Processing, Vol. 89, No. 10, 2066-2071, 2009.
doi:10.1016/j.sigpro.2009.03.032

32. Deris, A. M., A. M. Zain, and R. Sallehuddin, , "Overview of support vector machine in modeling machining performances," Procedia Engineering, Vol. 24, 308-312, 2011.
doi:10.1016/j.proeng.2011.11.2647

33. Zhang, Y. and L. Wu, "Classification of fruits using computer vision and a multiclass support vector machine," Sensors, Vol. 12, No. 9, 12489-12505, 2012.
doi:10.3390/s120912489

34. Wu, K.-P. and S.-D. Wang, "Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space," Pattern Recognition, Vol. 42, No. 5, 710-717, 2009.
doi:10.1016/j.patcog.2008.08.030

35. Fei, S.-W., "Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine," Expert Systems with Applications, Vol. 37, No. 10, 6748-6752, 2010.
doi:10.1016/j.eswa.2010.02.126

36. Zhao, C., et al. "Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine," Expert Systems with Applications, Vol. 38, No. 8, 9908-9912, 2011.
doi:10.1016/j.eswa.2011.02.078

Prof. Yudong Zhang

Dr. Shuihua Wang

Dr. Zhengchao Dong