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ANN Application for Medical Image Denoising

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 816))

Abstract

Nowadays, medical image denoising is crucial for accurate diagnosis of the critical diseases. For denoising these images, conventional wavelet technique (universal threshold) uses a fixed value of threshold which is non-adaptive. The main aim of this paper is to develop a steepest descent (SD)-based learning algorithm, which is used in Artificial Neural Networks (ANN), to reduce the noise in images adaptively. A new soft thresholding function is proposed as the activation function of the ANN. From the results, it is found that proposed algorithm performed well when compared with conventional wavelet technique in terms of mean squared error (MSE), peak signal-to-noise ratio (PSNR).

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References

  1. Al Jumah, A.: Denoising of an image using discrete stationary wavelet transform and various thresholding techniques. Published Online February 2013 J. Sig. Inf. Process., 33–41(2013)

    Article  Google Scholar 

  2. Donoho, D.: De-noising by softhresholding. EEE Trans. Inform Theory 41(3), 612–627 (1995)

    Google Scholar 

  3. Portilla, J., Strela, V., Wainwright, M., Wainwright, M., Simoncelli, E.: Image denoising using Gaussian scale mixtures in the wavelet domain. IEEE Trans. Image Process. 12(11), 1338–1351 (2003)

    Article  MathSciNet  Google Scholar 

  4. Roy, S., Sinha, N., Sen, A.K.: A new hybrid image denoising method. Int. J. Inf. Technol. Knowl. Manage. 2(2), 491–497 (2010)

    Google Scholar 

  5. Hedaoo, P., Godbole, S.S.: Wavelet thresholding approach for image denoising. Int. J. Netw. Secur. Appl. 3(4), 16–21 (2011)

    Google Scholar 

  6. Martin, V., Chang, S.G.: Yu, B.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9) (2000)

    Google Scholar 

  7. Bahendwar, Y.S., Sinha, G.R.: A comparative performance analysis of discrete wavelet transforms for denoising of medical images. In: Mandal, D.K., Syan, C.S. (eds.) CAD/CAM, Robotics and Factories of the Future. Lecture Notes in Mechanical Engineering. Springer, New Delhi (2016)

    Google Scholar 

  8. Shang, H.-q., Gao, R.-p., Wang, C.-y.: An improvement of wavelet shrinkage denoising via wavelet coefficient transformation. J. Vibr. Shock, pp 165–168(2011)

    Google Scholar 

  9. Nezamabadi-Pour, M., Nasri, H.: Image denoising in the wavelet domain using a new adaptive thresholding function. Neuro Comput. 72, 1012–1025 (2009)

    Google Scholar 

  10. Zhang, X.-P.: Thresholding neural network for adaptive noise reduction. IEEE Trans. Neural Netw. 12(3), 261–270 (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of ECE, MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India

    M. Laxmi Prasanna Rani

  2. Department of ECE, AUCE(A), Andhra University, Visakhapatnam, Andhra Pradesh, India

    G. Sasibhushana Rao

  3. Department of ECE, JNTUK, Kakinada, 533003, Andhra Pradesh, India

    B. Prabhakara Rao

Authors
  1. M. Laxmi Prasanna Rani
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  2. G. Sasibhushana Rao
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  3. B. Prabhakara Rao
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Corresponding author

Correspondence to M. Laxmi Prasanna Rani .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University New Delhi , New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Rani, M.L.P., Sasibhushana Rao, G., Prabhakara Rao, B. (2019). ANN Application for Medical Image Denoising. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_53

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