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Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images

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Abstract

In this paper, a parameterless Jaya optimization based neural network filter named as Jaya-functional link multilayer perceptron (Jaya-FLMLP) is proposed for the elimination of Poisson noise from X-ray images. In this proposed adaptive filter, Jaya is applied for updating the weights of the FLMLP network. The proposed neural filter is a combination of a functional link artificial neural network (FLANN) and Multilayer Perceptron (MLP) network. The performance of Jaya-FLMLP is also compared with other five competitive networks such as Wiener, MLP, Least Mean Squares based Functional Link Artificial Neural Network (LMS-FLANN), Particle Swarm Optimization based Functional Link Artificial Neural Network (PSO-FLANN) and Cat Swarm Optimization based Functional Link Artificial Neural Network (CSO-FLANN). The comparison of performance is investigated by the Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Reduction in Decibels (NRDB) values. The simulation results and non-parametric Friedman’s test reveal the superiority of the Jaya-FLMLP filter over others.

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References

  1. Chen S, Suzuki K (2014) Bone suppression in chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing and consistency processing. Comput Intell Biomed Imaging 33:211–235

    Article  Google Scholar 

  2. Chen F, Wu Y (2015) Improving image recognition by hierarchical model and denoising. 11th International Conference on Natural Computation, 2015

  3. Dash PP, Patra D (2011) Evolutionary neural network for noise cancellation in image data. Int J Comput Vis Robot 2:206–217

    Article  Google Scholar 

  4. Dehuri S, Cho SB (2010) Evolutionarily optimized features in functional link neural network for classification. Expert Syst Appl 37:4379–4391

    Article  Google Scholar 

  5. Dokur Z, Olmez T (2002) Segmentation of ultrasound images by using a hybrid neural network. Pattern Recogn Lett 23:1825–1836

    Article  MATH  Google Scholar 

  6. Duan H, Wang X (2016) Echo state networks with orthogonal pigeon-inspired optimization for image restoration. IEEE Trans Neural Networks Learn Syst 27:1–13

    Article  MathSciNet  Google Scholar 

  7. García DM, Gutiérrez JG, Santos JCR (2012) On the evolutionary optimization of k-NN by label-dependent feature weighting. Pattern Recogn Lett 33:2232–2238

    Article  Google Scholar 

  8. Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 15:178–188

    Article  Google Scholar 

  9. Ginneken BV, Stegmann MB, Loog M (2006) Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database. Med Image Anal 10:19–40

    Article  Google Scholar 

  10. He L, Land R, Greenshields I (2009) A nonlocal maximum likelihood estimation method for rician noise reduction in mr images. IEEE Trans Med Imaging 28:165–172

    Article  Google Scholar 

  11. Kumar M, Mishra SK (2015) Kumar M, Mishra SK (2015) Particle swarm optimization-based functional link artificial neural network for medical image denoising. In: Computational Vision and Robotics. 1st edn, Springer, New Delhi, pp 105–111. https://doi.org/10.1007/978-81-322-2196-8_13

  12. Kumar M, Mishra SK (2017) Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed Res 28:4159–4164

    Google Scholar 

  13. Kumar M, Mishra SK, Sahu SS (2016) Cat swarm optimization based functional link artificial neural network filter for gaussian noise removal from computed tomography images. Appl Comput Intell Soft Comput 2016:1–6

    Article  Google Scholar 

  14. Li Y, Lu J, Wang L, Fan Y, Li S, Yahagi T (2007) Removing noise from medical CR image using multineural network filter based on noise intensity distribution. Proc. - Third Int. Conf. Nat. Comput, 2007

  15. Majhi B, Panda G (2011) Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique. Expert Syst Appl 38(1):321–333

    Article  Google Scholar 

  16. Mirjalili S, Hashim SZM, Sardroudi HM (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218:11125–11137

    MathSciNet  MATH  Google Scholar 

  17. Pao Y (1989) Adaptive pattern recognition and neural networks. Addison Wesley, Boston

    MATH  Google Scholar 

  18. Perry SW, Guan L (2000) Weight assignment for adaptive image restoration by neural networks. IEEE Trans Neural Netw 11:156–170

    Article  Google Scholar 

  19. Poisson noise (2016) (Online). Available: https://in.mathworks.com/help/images/ref/imnoise.html. Accessed 25 Jun 2016

  20. Rao RV (2016) Jaya : A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 719–34:2016

    Google Scholar 

  21. Rao RV, Waghmare GG (2016) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49:1–24

    Google Scholar 

  22. Rao RV, More KC, Taler J, Ocłoń J (2016) Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 103:572–582

    Article  Google Scholar 

  23. Saadi S, Guessoum A, Bettayeb M (2013) ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess Microsyst 37:52–64

    Article  Google Scholar 

  24. Sicuranza GL, Carini A (2011) A generalized FLANN filter for nonlinear active noise control. IEEE Trans Audio Speech Lang Process 19:2412–2417

    Article  Google Scholar 

  25. Sivakumar K (1993) Image Restoration Using a Multilayer Perceptron with a Multilevel Sigmoidal Function. IEEE Trans of Signal Process 41:2018–2022

    Article  MATH  Google Scholar 

  26. Suzuki K, Horiba I, Sugie N (2003) Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans Pattern Anal Mach Intell 25:1582–1596

    Article  Google Scholar 

  27. Wang A, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13:600–612

    Article  Google Scholar 

  28. Wang H, Lv Y, Chen H, Li Y, Zhang Y, Lu Z (2016) Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4242-0

  29. Wen H, Wen J (2013) Image Denoising and Restoration Using Pulse Coupled Neural Networks. 6th Int. Congr. Image Signal Process. 2013

  30. Zeng W, Lu X, Tan X (2013) A local structural adaptive partial differential equation for image denoising. Multimed Tools Appl 74:743–757

    Article  Google Scholar 

  31. Zhang D, Mabu S, Hirasawa K (2010) Noise reduction using genetic algorithm based pcnn method. IEEE conf. Systems Man and Cybernetics (SMC). 2010

  32. Zhao H, Zhang J (2008) Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization. Signal Process 88:1946–1957

    Article  MATH  Google Scholar 

  33. Zhao H, Zhang J (2010) Pipelined Chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter. IEEE Trans Syst Man, Cybern Part B Cybern 40:162–172

    Article  Google Scholar 

  34. Zhou YT, Chellappa R, Vaid A, Jenkins BK (1988) Image restoration using a neural network. IEEE Trans Acoust 36:1141–1151

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Birla Institute of Technology, Ranchi, India

    M. Kumar

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  1. M. Kumar
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  2. S. K. Mishra
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Corresponding author

Correspondence to M. Kumar.

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Fig. 11
figure 11

X-rays Images (a) Severe pneumonia (b) Pelvis. (Image Courtesy: radiology images database - http://cdn.lifeinthefastlane.com)

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Kumar, M., Mishra, S.K. Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images. Multimed Tools Appl 77, 24405–24425 (2018). https://doi.org/10.1007/s11042-017-5592-y

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  • DOI: https://doi.org/10.1007/s11042-017-5592-y

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