Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2017, Article ID 9060124, 13 pages
https://doi.org/10.1155/2017/9060124
Research Article

Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

Department of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea

Correspondence should be addressed to Goo-Rak Kwon; rk.ca.nusohc@nowkrg

Received 30 December 2016; Revised 22 March 2017; Accepted 30 April 2017; Published 21 June 2017

Academic Editor: Jose M. Juarez

Copyright © 2017 Debesh Jha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Brookmeyera, E. Johnsona, K. Ziegler-Grahamb, and H. M. Arrighic, “Forecasting the global burden of Alzheimer’s disease,” Alzheimer’s & Dementia, vol. 3, no. 3, pp. 186–191, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Davatzikos, P. Bhatt, L. M. Shaw, K. N. Batmanghelich, and J. Q. Trojanowski, “Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification,” Neurobiology of Aging, vol. 32, no. 12, pp. 2322.e19–2322.e27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Nordberg, J. O. Rinne, A. Kadir, and B. Langstrom, “The use of PET in Alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, pp. 78–87, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, “Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 13, pp. 4637–4642, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Magnander, E. Wikberg, J. Svensson et al., “A novel statistical analysis method to improve the detection of hepatic foci of 111 In-octreotide in SPECT/CT imaging,” EJNMMI Physics, vol. 3, no. 1, p. 1, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. P. De Visschere, M. Nezzo, E. Pattyn, V. Fonteyne, C. Van Praet, and G. Villeirs, “Prostate magnetic resonance spectroscopic imaging at 1.5 tesla with endorectal coil versus 3.0 tesla without endorectal coil: comparison of spectral quality,” Clinical Imaging, vol. 39, no. 4, pp. 636–641, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. E. D. Roberson and L. Mucke, “100 years and counting: prospects for defeating Alzheimer’s disease,” Science, vol. 314, no. 5800, pp. 781–784, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Tabaton, P. Odetti, S. Cammarata et al., “Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive Impairment,” Journal of Alzheimer’s Disease, vol. 19, no. 3, pp. 1035–1040, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. B. S. Mahanand, S. Suresh, N. Sundararajan, and K. M. Aswatha, “Identification of brain regions responsible for Alzheimer’s disease using self-adaptive resource allocation network,” Neural Networks, vol. 32, pp. 313–322, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Jeurissen, A. Leemans, and J. SIjbers, “Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI,” Medical Image Analysis, vol. 18, no. 7, pp. 953–962, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Hamelin, M. Bertoux, M. Bottlaender et al., “Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer’s disease,” Neurobiology of Aging, vol. 36, no. 11, pp. 2932–2939, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Á. Caballero, M. Brendel, A. Delker et al., “Mapping 3-year changes in gray matter and metabolism in Abeta-positive nondemented subjects,” Neurobiology of Aging, vol. 36, no. 11, pp. 2913–2924, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Yang, Y. Zhang, J. Yang et al., “Automated classification of brain images using wavelet energy and biogeography-based optimization,” Multimedia Tools and Applications, vol. 75, no. 23, pp. 15601–15617, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. E. S. El-Dahshan, H. M. Mohsen, K. Revett, and A. B. Salem, “Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm,” Expert Systems with Applications, vol. 41, no. 11, pp. 5526–5545, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Zhang, S. Wang, G. Ji, and Z. Dong, “An MR images classifier system via particle swarm optimization and kernel support vector machine,” The Scientific World Journal, vol. 2013, Article ID 130134, 9 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Álvarez, J. M. Górriz, J. Ramírez et al., “Alzheimer’s diagnosis using eigenbrains and support vector machines,” Electronics Letters, vol. 45, no. 7, pp. 973–980, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Z. Iqbal, A. Ghafoor, A. M. Siddhiqui, M. M. Riaz, and U. Khalid, “Dual-tree complex wavelet transform and SVD based medical image resolution enhancement,” Signal Processing, vol. 105, pp. 430–437, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Zhang, B. Peng, S. Wang et al., “Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection,” Scientific Reports, vol. 6, article 21816, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. D. K. Shin and Y. S. Moon, “Super-resolution image reconstruction using wavelet based patch and discrete wavelet transform,” Journal of Signal Processing Systems, vol. 81, no. 1, pp. 71–81, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Zhang, S. Wang, G. Ji, and Z. Dong, “Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging,” IEEJ Transcations on Electrical and Electronic Engineering, vol. 10, no. 1, pp. 116-117, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Beura, B. Majhi, and R. Dash, “Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurance matrix for detection of breast cancer,” Neurocomputing, vol. 154, pp. 1–14, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. N. Kingsbury, “Complex wavelets for shift invariant analysis and filtering of signals,” Applied and Computational Harmonic Analysis, vol. 10, no. 3, pp. 234–353, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Barri, A. Dooms, and P. Schelkens, “The near shift-invariance of the dual-tree complex wavelet transform revisited,” Journal of Mathematical Analysis and Applications, vol. 389, no. 2, pp. 1303–1314, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Wang, S. Lu, Z. Dong, J. Yang, M. Yang, and Y. Zhang, “Dual-tree wavelet transform and twin support vector machine for pathological brain detection,” Applied Science, vol. 6, no. 6, p. 169, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2006.
  26. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, 2012.
  27. D. Guo, Y. Zhang, Q. Xiang, and Z. Li, “Improved radio frequency identification method via radial basis function neural network,” Mathematical Problems in Engineering, vol. 2014, Article ID 420482, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Zhang, Z. Dong, L. Wu, and S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049–10053, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Manoochehri and F. Kolahan, “Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process,” The International Journal of Advaned Manufacturing Technology, vol. 73, no. 1, pp. 241–249, 2014. View at Google Scholar
  30. S. U. Aswathy, G. G. D. Dhas, and S. S. Kumar, “A survey on detection of brain tumor from MRI brain images,” in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 871–877, Kanyakumari, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Poursamad, “Adaptive feedback linearization control of anticlock braking systems using neural networks,” Mechatronics, vol. 19, no. 5, pp. 767–773, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. H. C. Yuan, F. L. Xiong, and X. Y. Huai, “A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy,” Computers and Electronics Agriculture, vol. 40, no. 1–3, pp. 57–64, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Zhang, S. Wang, P. Phillips, and G. Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection,” Knowledge-Based Systems, vol. 64, pp. 21–31, 2014. View at Google Scholar
  34. C. Plant, S. J. Teipel, A. Oswald et al., “Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease,” NeuroImage, vol. 50, no. 1, pp. 162–174, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. D. Jha and G. R. Kwon, “Alzheimer disease detection in MRI using curvelet transform with KNN,” The Journal of Korean Institute of Information Technology, vol. 14, no. 8, 2016. View at Publisher · View at Google Scholar
  36. Y. Zhang, S. Wang, and Z. Dong, “Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree,” Progress in Electromagnetic Research, vol. 144, pp. 171–184, 2014. View at Publisher · View at Google Scholar
  37. E. A. Maguire, D. G. Gadian, I. S. Johnsrude et al., “Navigation-related structural change in the hippocampi of taxi drivers,” Proceedings of the National Academy of Sciences, vol. 97, no. 8, pp. 4398–4403, 2000. View at Publisher · View at Google Scholar · View at Scopus
  38. K. R. Gray, P. Alijabar, R. A. Heckemann, A. Hammers, and D. Rueckert, “Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease,” NeuroImage, vol. 65, pp. 167–175, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, “A voxel-based morphometric study of ageing in 465 normal adult human brains,” NeuroImage, vol. 14, no. 1, Part 1, pp. 21–36, 2001. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Zhang and S. Wang, “Detection of Alzheimer’s disease by displacement field and machine learning,” PeerJ, vol. 3, article e1251, 2015. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Makeig, T.-P. Jung, A. J. Bell, D. Ghahremani, and T. Sejnowski, “Blind separation of auditory event-related brain responses into independent components,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 20, pp. 10979–10984, 1997. View at Publisher · View at Google Scholar · View at Scopus
  42. A. J. Izenman, “Linear discriminant analysis,” in Modern Multivariate Statistical Techniques, pp. 237–280, Springer, New York, 2013. View at Google Scholar
  43. M. E. Tipping and C. M. Bishop, “Probabilistic principal component analysis,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 61, no. 3, pp. 611–622, 1999. View at Publisher · View at Google Scholar
  44. B. A. Olshausen and D. J. Field, “Sparse coding with an overcomplete basis set: a strategy employed by V1,” Vision Research, vol. 37, no. 23, pp. 3311–3325, 1997. View at Publisher · View at Google Scholar · View at Scopus
  45. K. Polat, S. Güneş, and A. Arslan, “A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine,” Expert Systems with Applications, vol. 34, no. 1, pp. 482–487, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Wang, X. Yang, Y. Zhang, P. Phillips, J. Yang, and T.-F. Yuan, “Identification of green, oolong and black teas in china via wavelet packet entropy and fuzzy support vector machine,” Entropy, vol. 17, no. 10, pp. 6663–6682, 2015. View at Publisher · View at Google Scholar · View at Scopus
  47. M. J. Er, S. Wu, J. Lu, and H. L. Toh, “Face recognition with radial basis function (RBF) neural networks,” IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 697–710, 2002. View at Publisher · View at Google Scholar · View at Scopus
  48. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, pp. 1097–1105, 2012. View at Google Scholar
  49. Y. Zhang, S. Wang, P. Phillips, and G. Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection,” Knowledge-Based Systems, vol. 64, pp. 22–31, 2014. View at Publisher · View at Google Scholar · View at Scopus
  50. Y. Zhang, L. Wu, and S. Wang, “Magnetic resonance brain image classification by an improved artificial bee colony algorithm,” Progress in Electromagnetics Research, vol. 116, pp. 65–79, 2011. View at Publisher · View at Google Scholar
  51. Y. Zhang, S. Wang, G. Ji, and Z. Dong, “Genetic pattern search and its application to brain image classification,” Mathematical Problems in Engineering, vol. 2013, Article ID 580876, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. C. Pang, G. Jiang, S. Wang et al., “Gene order computation using Alzheimer’s DNA microarray gene expression data and the ant colony optimisation algorithm,” International Journal of Data Mining and Bioinformatics, vol. 6, no. 6, pp. 617–632, 2012. View at Publisher · View at Google Scholar · View at Scopus
  53. S. Wang, Y. Zhang, G.-L. Ji, J.-G. Wu, and L. Wei, “Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization,” Entrophy, vol. 17, no. 8, pp. 5711–5728, 2015. View at Publisher · View at Google Scholar · View at Scopus
  54. Y. Chen, Y. Zhang, J. Yang et al., “Curve-like structure extraction using minimal path propagation with backtracking,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 988–1003, 2016. View at Publisher · View at Google Scholar · View at Scopus
  55. Z. Dong, Y. Zhang, F. Liu, Y. Duan, A. Kangarlu, and B. S. Peterson, “Improving the spectral resolution and spectral fitting of H MRSI data from human calf muscle by the SPREAD technique,” NMR in Biomedicine, vol. 27, no. 11, pp. 1325–1332, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. Y. Zhang, Z. Dong, P. Phillips et al., “Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning,” Frontlier in Computational Neuroscience, vol. 9, p. 66, 2015. View at Publisher · View at Google Scholar