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Automated classification of brain images using wavelet-energy and biogeography-based optimization

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Abstract

It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.

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References

  1. Bafroui HH, Ohadi A (2014) Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 133:437–445

    Article  Google Scholar 

  2. Cai ML et al (2014) Motion recognition for 3D human motion capture data using support vector machines with rejection determination. Multimedia Tools Appl 70(2):1333–1362

    Article  Google Scholar 

  3. Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92

    Article  Google Scholar 

  4. Choudhary R et al (2009) Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosyst Eng 102(2):115–127

    Article  MathSciNet  Google Scholar 

  5. Christy AA, Raj P (2014) Adaptive biogeography based predator–prey optimization technique for optimal power flow. Int J Electr Power Energy Syst 62:344–352

    Article  Google Scholar 

  6. Dai Y, Zhang JX, Xue Y (2013) Use of wavelet energy for spinal cord vibration analysis during spinal surgery. Int J Med Robot Comput Assist Surg 9(4):433–440

    Article  Google Scholar 

  7. Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res Pier 137:1–17

    Article  Google Scholar 

  8. Dong Z et al (2014) Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique. NMR Biomed 27(11):1325–1332

    Article  Google Scholar 

  9. El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441

    Article  Google Scholar 

  10. El-Dahshan ESA et al (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545

    Article  Google Scholar 

  11. Fang L, Wu L, Zhang Y (2015) A novel demodulation system based on continuous wavelet transform. Math Probl Eng 2015:9

    Google Scholar 

  12. 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 

  13. Guo DL et al. (2014) Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network. Math Probl Eng. No. pp. 2014

  14. Guo WA et al (2014) Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Eng Optim 46(11):1465–1484

    Article  MathSciNet  Google Scholar 

  15. Kalbkhani H, Shayesteh MG, Zali-Vargahan B (2013) Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomed Signal Process Control 8(6):909–919

    Article  Google Scholar 

  16. Lee SH et al (2013) Diagnostic method for insulated power cables based on wavelet energy. Ieice Electron Express 10(12):20130335

    Article  Google Scholar 

  17. Maitra M, Chatterjee A (2006) A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomed Signal Process Control 1(4):299–306

    Article  Google Scholar 

  18. Messina A (2008) Refinements of damage detection methods based on wavelet analysis of dynamical shapes. Int J Solids Struct 45(14–15):4068–4097

    Article  MATH  Google Scholar 

  19. Mookiah MRK et al (2012) Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl-Based Syst 33:73–82

    Article  Google Scholar 

  20. Nanthagopal AP, Rajamony RS (2013) Classification of benign and malignant brain tumor CT images using wavelet texture parameters and neural network classifier. J Vis 16(1):19–28

    Article  Google Scholar 

  21. Padma A, Sukanesh R (2014) Segmentation and classification of brain CT images using combined wavelet statistical texture features. Arab J Sci Eng 39(2):767–776

    Article  Google Scholar 

  22. Panchal MB, Mahapatra DR (2013) Influence of crack parameters on wavelet energy correlated damage index. Adv Vib Eng 12(3):311–318

    Google Scholar 

  23. Ramasamy R, Anandhakumar P (2011) Brain Tissue Classification of MR Images Using Fast Fourier Transform Based Expectation-Maximization Gaussian Mixture Model. in Advances in Computing and Information Technology, ed: Springer, pp. 387–398

  24. Saritha M, Paul Joseph K, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156

    Article  Google Scholar 

  25. Simon D (2011) A probabilistic analysis of a simplified biogeography-based optimization algorithm. Evol Comput 19(2):167–188

    Article  Google Scholar 

  26. Wu YL et al (2014) Gaze direction estimation using support vector machine with active appearance model. Multimedia Tools Appl 70(3):2037–2062

    Article  Google Scholar 

  27. Xu S et al (2014) Multi-task least-squares support vector machines. Multimedia Tools Appl 71(2):699–715

    Article  Google Scholar 

  28. Zhang Y, 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 

  29. Zhang Y, Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial Bee colony algorithm. Prog Electromagn Res 116:65–79

    Article  Google Scholar 

  30. Zhang Y et al (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053

    Article  Google Scholar 

  31. Zhang Y et al (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013:9

    Google Scholar 

  32. Zhang Y et al (2013) Genetic pattern search and its application to brain image classification. Math Probl Eng 2013:8

    MathSciNet  MATH  Google Scholar 

  33. Zhang Y et al (2014) Energy preserved sampling for compressed sensing MRI. Comput Math Methods in Med 2014:12

    MATH  Google Scholar 

  34. Zhang Y et al (2015) Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ Trans Electr Electron Eng 10(1):116–117

    Article  Google Scholar 

  35. Zhang Y et al (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with tsallis entropy and generalized eigenvalue proximate support vector machine (GEPSVM). Entropy 17(4):1795–1813

    Article  Google Scholar 

Download references

Acknowledgments

This paper was supported by NSFC (610011024, 61273243, 51407095), Program of Natural Science Research of Jiangsu Higher Education Institutions (13KJB460011, 14KJB520021), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2014009-3, BE2013012-2), Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), and Science Research Foundation of Hunan Provincial Education Department (12B023).

Conflict of interest

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

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan City University, Yiyang, 413000, China

    Gelan Yang

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

    Yudong Zhang, Genlin Ji & Shuihua Wang

  3. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, 210042, China

    Yudong Zhang, Jiquan Yang, Chunmei Feng & Qiong Wang

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

    Zhengchao Dong

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  1. Gelan Yang
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  2. Yudong Zhang
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  3. Jiquan Yang
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  4. Genlin Ji
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  5. Zhengchao Dong
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  6. Shuihua Wang
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  7. Chunmei Feng
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  8. Qiong Wang
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Correspondence to Yudong Zhang.

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Yang, G., Zhang, Y., Yang, J. et al. Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 75, 15601–15617 (2016). https://doi.org/10.1007/s11042-015-2649-7

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  • DOI: https://doi.org/10.1007/s11042-015-2649-7

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