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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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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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Correspondence to M. Kumar.

Appendix

Appendix

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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