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A Hybrid Method Based CT Image Denoising Using Nonsubsampled Contourlet and Curvelet Transforms

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Proceedings of International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 459))

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Abstract

Computed tomography (CT) is one of the most widespread radio-logical tools for diagnosis purpose. To achieve good quality of CT images with low radiation dose has drawn a lot of attention to researchers. Hence, post-processing of CT images has become a major concern in medical image processing. This paper presents a novel edge-preserving image denoising scheme where noisy CT images are denoised using nonsubsampled contourlet transform (NSCT) and curvelet transform separately. By estimating variance difference on both denoised images, final denoised CT image has been achieved using a variation-based weighted aggregation. The proposed scheme is compared with existing methods and it is observed that the performance of proposed method is superior to existing methods in terms of visual quality, image quality index (IQI), and peak signal-to-noise ratio (PSNR).

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References

  1. Boone, J. M., Geraghty, E. M., Seibert, J. A., Wootton-Gorges, S. L.: Dose reduction in pediatric CT: a rational approach. Journal of Radiology, vol. 228(2), pp 352–360 (2003).

    Article  Google Scholar 

  2. Kim, D., Ramani, S., Fessler, J. A.: Accelerating X-ray CT ordered subsets image reconstruction with Nesterov first-order methods. In Proc. Fully Three-Dimensional Image Reconst. in Radiology and Nuclear Medicine, pp. 22–25 (2013).

    Google Scholar 

  3. Chang, S. G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. on Image Process., vol. 9(9): 1532–1546 (2000).

    Article  MathSciNet  MATH  Google Scholar 

  4. Donoho, D. L., Johnstone, I. M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika, vol. 81(3): 425–455 (1994).

    Article  MathSciNet  MATH  Google Scholar 

  5. Donoho, D. L.: Denoising by soft thresholding. IEEE Transactions on Information Theory, vol. 41(3): 613–627 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  6. Chang, S. G., Yu, B., Vetterli, M.: Spatially adaptive thresholding with context modeling for image denoising. IEEE Transactions on Image Process., vol. 9(9): 1522–1531 (2000).

    Article  MathSciNet  MATH  Google Scholar 

  7. Donoho, D. L., Johstone, I. M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc., vol. 90(432): 1200–1224 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  8. Fathi, A., Naghsh-Nilchi, A. R.: Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, vol. 21(9): 3981–3990 (2012).

    Article  MathSciNet  Google Scholar 

  9. Borsdorf, A., Raupach, R., Flohr, T., Hornegger, J.: Wavelet Based Noise Reduction in CT-Images Using Correlation Analysis. IEEE Transactions on Medical Imaging, vol. 27(12): 1685–1703 (2008).

    Article  Google Scholar 

  10. Rabbani, H., Nezafat, R., Gazor, S.: Wavelet-Domain Medical Image Denoising Using Bivariate Laplacian Mixture Model. IEEE Transaction on Biomedical Engineering, vol. 56(12): 2826–2837 (2009).

    Article  Google Scholar 

  11. Sendur, L., Selesnick, W. I.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transaction on signal processing, vol. 50(11): 2744–2756 (2002).

    Article  Google Scholar 

  12. Bhadauria, H. S., Dewal, M. L.: Performance evaluation of curvelet and wavelet based denoising methods on brain computed tomography images. In IEEE Int Conf Emerg Trends Electr Comput Technol (ICETECT) pp. 666–670, (2011).

    Google Scholar 

  13. Kingsbury N. C.: The dualtree complex wavelet transform: a new efficient tool for image restoration and enhancement. In Proc. 9th European Signal Processing Conference (EUSIPCO 98), pp. 319–322 (1998).

    Google Scholar 

  14. Diwakar, M., Sonam, Kumar M.: CT image denoising based on complex wavelet transform using local adaptive thresholding and Bilateral filtering. In ACM Proceeding of the Third International Symposium on Women in Computing and Informatics pp. 297–302, (2015).

    Google Scholar 

  15. Cunha A. L. da, Zhou J. P., Do M. N.: The Nonsubsampled Contourlet Transform: Theory, Design, and Applications. IEEE Transactions on Image Processing, vol. 15(10):3089–3011 (2006).

    Article  Google Scholar 

  16. Donoho DL, Duncan MR: Digital curvelet transform: strategy, implementation and experiments. Standford University, (1999).

    Google Scholar 

  17. Ouyang H. B., Quan H. M., Tang Y. , ZengY. Z.: Image Denoising Algorithm using Adaptive Bayes Threshold Subband Based on NSCT. In Electronic Design Engineering, vol. 19(23):185–188 (2011).

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India

    Manoj Diwakar & Manoj Kumar

Authors
  1. Manoj Diwakar
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  2. Manoj Kumar
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Corresponding author

Correspondence to Manoj Diwakar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Partha Pratim Roy

  4. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Sen

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Diwakar, M., Kumar, M. (2017). A Hybrid Method Based CT Image Denoising Using Nonsubsampled Contourlet and Curvelet Transforms. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_51

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  • DOI: https://doi.org/10.1007/978-981-10-2104-6_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2103-9

  • Online ISBN: 978-981-10-2104-6

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