Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Detection of Alzheimer’s disease (AD) from magnetic resonance images can help neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain. Currently, scholars have proposed several approaches to automatically detect AD. In this study, we aimed to develop a novel AD detection system with better performance than existing systems. 28 ADs and 98 HCs were selected from OASIS dataset. We used inter-class variance criterion to select single slice from the 3D volumetric data. Our classification system is based on three successful components: wavelet entropy, multilayer perceptron, and biogeography-base optimization. The statistical results of our method obtained an accuracy of 92.40 ± 0.83%, a sensitivity of 92.14 ± 4.39%, a specificity of 92.47 ± 1.23%. After comparison, we observed that our pathological brain detection system is superior to latest 6 other approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Agarwal P et al (2013) Swarm intelligence and its applications. Sci World J 2013:528069

    Article  Google Scholar 

  2. Aggarwal N et al (2015) 3d discrete wavelet transform for computer aided diagnosis of Alzheimer’s disease using t1-weighted brain MRI. Int J Imaging Syst Technol 25(2):179–190

    Article  Google Scholar 

  3. Ardekani BA et al (2013) Sexual dimorphism in the human corpus callosum: an MRI study using the OASIS brain database. Cereb Cortex 23(10):2514–2520

    Article  Google Scholar 

  4. Bakhshi AD et al (2013) Application of continuous-time wavelet entropy for detection of cardiac repolarisation alternans. IET Signal Processing 7(8):783–790

    Article  Google Scholar 

  5. Balochian S (2014) Artificial intelligence and its applications. Mathematical problems in engineering Article ID: 840491

  6. Behera NKS et al. (2015) Bird mating optimization based multilayer perceptron for diseases classification. In: 1st International Conference on Computational Intelligence in Data Mining (ICCIDM) Burla, India, Springer-Verlag Berlin, p 272–278

  7. Bhuiyan MAA (2016) Towards face recognition using eigenface. Int J Adv Comput Sci Appl 7(5):25–31

    Google Scholar 

  8. Bozorg Haddad O et al (2016) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resour Plan Manag 142(1):04015034

    Article  Google Scholar 

  9. Bradley PS (2013) A support-based reconstruction for SENSE MRI. Sensors 13(4):4029–4040

    Article  Google Scholar 

  10. Candra H et al. (2015) Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 37th Annual International Conference Of the Ieee Engineering In Medicine And Biology Society. Milan, Italy, IEEE p 7250–7253

  11. Chen Y et al (2016) Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Trans Image Process 25(2):988–1003

    Article  MathSciNet  Google Scholar 

  12. De Visschere P et al (2015) Prostate magnetic resonance spectroscopic imaging at 1.5 tesla with endorectal coil versus 3.0 tesla without endorectal coil: comparison of spectral quality. Clin Imaging 39(4):636–641

    Article  Google Scholar 

  13. Dil EA et al (2016) Trace determination of safranin O dye using ultrasound assisted dispersive solid-phase micro extraction: artificial neural network-genetic algorithm and response surface methodology. Ultrason Sonochem 33:129–140

    Article  Google Scholar 

  14. Dong Z (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagn Res 144:171–184

    Article  Google Scholar 

  15. Du S (2016) Multi-objective path finding in stochastic networks using a biogeography-based optimization method. Simulation 92(7):637–647

    Article  Google Scholar 

  16. Fang L, Wu L (2015) A novel demodulation system based on continuous wavelet transform. Math Probl Eng 2015:513849

    MATH  Google Scholar 

  17. Farswan P et al. (2016) A modified biogeography based optimization. In: 2nd International Conference on Harmony Search Algorithm (ICHSA), Korea Univ, Seoul, South Korea: Springer-Verlag Berlin, p 227–238

  18. Frantzidis CA et al (2014) Functional disorganization of small-world brain networks in mild Alzheimer’s disease and amnestic mild cognitive impairment: an EEG study using relative wavelet entropy (RWE). Front Aging Neurosci 6:224

    Article  Google Scholar 

  19. Goh S et al (2014) Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. JAMA Psychiatry 71(6):665–671

    Article  MathSciNet  Google Scholar 

  20. Good CD et al (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14(1):21–36

    Article  MathSciNet  Google Scholar 

  21. Gorji HT, Haddadnia J (2015) A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience 305:361–371

    Article  Google Scholar 

  22. Gorriz JM, Ramírez J (2016) Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci 2016(10):160

    Google Scholar 

  23. Gray KR et al (2013) Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage 65:167–175

    Article  Google Scholar 

  24. Heidari AA et al. (2015) An effective hybrid support vector regression with chaos-embedded biogeography-based optimization strategy for prediction of earthquake-triggered slope deformations. In: International Conference on Sensors & Models In Remote Sensing & Photogrammetry, Kish Island, Iran, Copernicus Gesellschaft Mbh, p 301–305

  25. Ibanez F et al (2015) Detection of damage in multiwire cables based on wavelet entropy evolution. Smart Mater Struct 24(8):14 Article ID: 085036

    Article  Google Scholar 

  26. Ibrahim AO et al. (2015) Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution. In: International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), Khartoum, Sudan, IEEE, p 422–427

  27. Ji TY et al. (2014) Disturbance detection using hit-or-miss wavelet singular entropy for power quality monitoring. In: IEEE Power and Energy Society General Meeting PESGM, National Harbor, MD, IEEE, p 46–52

  28. Jiang WJ et al (2016) Parameters identification of fluxgate magnetic Core adopting the biogeography-based optimization algorithm. Sensors 16(7):979

    Article  Google Scholar 

  29. Krawczyk B et al (2015) A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification. Artif Intell Med 65(3):219–227

    Article  Google Scholar 

  30. Lee SG et al (2014) Reference-free damage detection for truss bridge structures by continuous relative wavelet entropy method. Structural Health Monitoring-an International Journal 13(3):307–320

    Article  MathSciNet  Google Scholar 

  31. Li J (2016) Detection of left-sided and right-sided hearing loss via fractional Fourier transform. Entropy 18(5):194

    Article  Google Scholar 

  32. Liu G (2016) Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Adv Mech Eng 8(2):11

    Google Scholar 

  33. Liu G et al (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248

    Google Scholar 

  34. Lu HM et al (2012) Maximum local energy: an effective approach for multisensor image fusion in beyond wavelet transform domain. Computers & Mathematics with Applications 64(5):996–1003

    Article  MATH  Google Scholar 

  35. Lu HM et al (2016a) Turbidity underwater image restoration using spectral properties and light compensation. IEICE Trans Inf Syst E99D(1):219–227

    Article  Google Scholar 

  36. Lu HM et al (2016b) Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. J Vis Commun Image Represent 38:504–516

    Article  Google Scholar 

  37. Magnander T et al (2016) A novel statistical analysis method to improve the detection of hepatic foci of (111)In-octreotide in SPECT/CT imaging. EJNMMI Physics 3(1):1

    Article  Google Scholar 

  38. Maguire EA et al (2000) Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A 97(8):4398–4403

    Article  Google Scholar 

  39. Makbol NM et al (2016) Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics. IET Image Process 10(1):34–52

    Article  Google Scholar 

  40. Mashhadban H et al (2016) Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 119:277–287

    Article  Google Scholar 

  41. Meng GL et al (2015) Meteorological factors related to emergency admission of elderly stroke patients in shanghai: analysis with a multilayer perceptron neural network. Med Sci Monit 21:3600–3607

    Article  Google Scholar 

  42. Mirjalili S et al (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209

    Article  MathSciNet  Google Scholar 

  43. Mondal U et al (2016) Servomechanism for periodic reference input: discrete wavelet transform-based repetitive controller. Trans Inst Meas Control 38(1):14–22

    Article  MathSciNet  Google Scholar 

  44. Park JS, Ju I (2016) Prescription drug advertising, disease knowledge, and older adults’ optimistic bias about the future risk of alzheimer’s disease. Health Commun 31(3):346–354

    Article  Google Scholar 

  45. Peng, I.B., et al. (2015) The cost of synchronizing imbalanced processes in message passing systems. In: International Conference on Cluster Computing, Chicago, IL, IEEE, p 408–417

  46. Peng B et al (2016) Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Sci Rep 6:21816

    Article  Google Scholar 

  47. Peters S et al (2013) Detection of irreversible changes in susceptibility-weighted images after whole-brain irradiation of children. Neuroradiology 55(7):853–859

    Article  Google Scholar 

  48. Peterson BS (2011) A two-level iterative reconstruction method for compressed sensing MRI. Journal of Electromagnetic Waves and Applications 25(8–9):1081–1091

    Google Scholar 

  49. Peterson BS (2014) Energy preserved sampling for compressed sensing MRI. Comput Math Methods Med 2014:546814

    MATH  Google Scholar 

  50. Phillips P et al (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagn Res 152:41–58

    Article  Google Scholar 

  51. Plant C et al (2010) Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage 50(1):162–174

    Article  Google Scholar 

  52. Pu X, He W (2015) Chaotic biogeography-based optimization algorithm for job scheduler in cloud computing. In: International Conference on Mechanical Science and Mechanical Design, Destech Publications, Changsha, Peoples R China, p 223–229

  53. Rajchl M et al (2016) Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling. Med Image Anal 27:45–56

    Article  Google Scholar 

  54. Saghatforoush A et al (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255–266

    Article  Google Scholar 

  55. Savio A, Grana M (2013) Deformation based feature selection for computer aided diagnosis of Alzheimer’s disease. Expert Syst Appl 40(5):1619–1628

    Article  Google Scholar 

  56. Shamshirband S et al (2016) Estimation of reference evapotranspiration using neural networks and cuckoo search algorithm. J Irrig Drain Eng 142(2):04015044

    Article  Google Scholar 

  57. Shiyang L et al (2007) Analysis of heart rate fluctuation based on wavelet entropy. Fluctuation and Noise Letters 7(2):L135–L142

    Article  Google Scholar 

  58. Sonawane JS, Patil DR (2014) Prediction of heart disease using multilayer perceptron neural network. In: IEEE International Conference on Information Communication and Embedded Systems, Chennai, India, p 5–11

  59. Sterkenburg TF (2016) Solomonoff prediction and Occam’s razor. Philos Sci 83(4):459–479

    Article  MathSciNet  Google Scholar 

  60. Sun P (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Biomed Mater Eng 26(s1):1283–1290

    Google Scholar 

  61. Torrents-Barrena J et al (2015) Complex wavelet algorithm for computer-aided diagnosis of Alzheimer’s disease. Electron Lett 51(20):1566–1567

    Article  Google Scholar 

  62. Wang L et al (2014) The effect of APOE epsilon 4 allele on cholinesterase inhibitors in patients with Alzheimer disease evaluation of the feasibility of resting state functional connectivity magnetic resonance imaging. Alzheimer Dis Assoc Disord 28(2):122–127

    Article  Google Scholar 

  63. Watamura N et al (2016) Colocalization of phosphorylated forms of WAVE1, CRMP2, and tau in Alzheimer’s disease model mice: involvement of Cdk5 phosphorylation and the effect of ATRA treatment. J Neurosci Res 94(1):15–26

    Article  Google Scholar 

  64. Wei G (2010) Color image enhancement based on HVS and PCNN. Science China Inf Sci 53(10):1963–1976

    Article  MathSciNet  Google Scholar 

  65. Wei L (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17(8):5711–5728

    Google Scholar 

  66. Wilkins HM, Swerdlow RH (2016) Relationships between mitochondria and Neuroinflammation: implications for Alzheimer’s disease. Curr Top Med Chem 16(8):849–857

    Article  Google Scholar 

  67. Wu L (2008) Improved image filter based on SPCNN. Science in China Series F: Information Sciences 51(12):2115–2125

    Google Scholar 

  68. Wu L (2011) Optimal multi-level Thresholding based on maximum Tsallis entropy via an artificial bee Colony approach. Entropy 13(4):841–859

    Article  MATH  Google Scholar 

  69. Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388

    Article  Google Scholar 

  70. Wu J (2016a) Fruit classification by biogeography-based optimization and feedforward neural network. Expert Syst 33(3):239–253

    Article  Google Scholar 

  71. Wu X (2016b) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation 92(9):873–885

    Article  Google Scholar 

  72. Yang G et al (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools and Applications. doi:10.1007/s11042-015-2649-7

    Article  Google Scholar 

  73. Yuan TF (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 9:66

    Google Scholar 

  74. Zainuddin Z, Fard SP (2015) Approximation of multivariate 2 pi-periodic functions by multiple 2 pi-periodic approximate identity neural networks based on the universal approximation theorems. In: 11th International Conference on Natural Computation. Zhangjiajie, Peoples R China, IEEE, p 8–13

  75. Zhan T (2016) Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning. Prog Electromagn Res 156:105–133

    Article  Google Scholar 

  76. Zhang Y (2015) Detection of Alzheimer’s disease by displacement field and machine learning. PeerJ 3:e1251

    Article  Google Scholar 

  77. Zhou X-X (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9):861–871

    Article  Google Scholar 

  78. Zhou X-X et al (2016) Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Trans Electr Electron Eng 11(3):1–10

    Article  Google Scholar 

  79. Zou YC et al (2015) Wavelet entropy based analysis and forecasting of crude oil price dynamics. Entropy 17(10):7167–7184

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by NSFC (61602250, 61503188), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau, (SDL201608), and Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607). The authors express their gratitude to the OASIS dataset supported by NIH grants (P50 MH071616, P01 AG03991, P50AG05681, R01 AG021910, R01 MH56584, and U24 RR021382).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Shui-Hua Wang, Wen-Juan Jia, Fang-Yuan Liu, Meng-Meng Yang & Yu-Dong Zhang

  2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China

    Yin Zhang

  3. School of Information Engineering, Yangzhou University, Yangzhou, China

    Yu-Jie Li

  4. Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY, 10032, USA

    Yu-Dong Zhang

Authors
  1. Shui-Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu-Jie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen-Juan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fang-Yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meng-Meng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yu-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Dong Zhang.

Ethics declarations

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.

Appendices

Appendix 1

Table 6 Stratified cross validation segment over our dataset
Full size table

Appendix 2

Table 7 Successful identified result over each fold
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, SH., Zhang, Y., Li, YJ. et al. Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl 77, 10393–10417 (2018). https://doi.org/10.1007/s11042-016-4222-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4222-4

Keywords

Search

Navigation