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Edge Preservation Based CT Image Denoising Using Wavelet and Curvelet Transforms

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 436))

Abstract

Computed tomography (CT) is a well-known medical radiological tool to diagnose the human body. Radiation dose is one of the major factors, which affects the quality of CT images. High radiation dose may improve the quality of image in terms of reducing noise, but it may be harmful for the patients. Due to low radiation dose, reconstructed CT images are noisy. To improve quality of noisy CT image, a postprocessing method is proposed. The goal of proposed scheme is to reduce the noise as much as possible by preserving the edges. The scheme is divided into two phases. In first phase, wavelet transform based denoisng is performed using bilateral filtering and thresholding. In second phase, a method noise thresholding based on curvelet transform is performed using the outcome of first phase. The proposed scheme is compared with existing methods. From experimental evaluation, it is observed that the performance of proposed scheme is superior to existing methods in terms of visual quality, PSNR and image quality index (IQI).

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References

  1. Huda, W., Scalzetti, E.M., Levin, G.: Technique factors and image quality as functions of patient weight at abdominal CT. J. Radiol. 217(2), 430–435 (2000)

    Google Scholar 

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

    Google Scholar 

  3. Siegel, M.J., Schmidt, B., Bradley, D., Suess, C., Hildebolt, C.: Radiation dose and image quality in pediatric CT: effect of technical factors and phantom size and shape. J. Radiol. 233(2), 515–522 (2004)

    Google Scholar 

  4. Li, T., Li, X., Wang, J.: Nonlinear sinogram smoothing for low-dose x-ray CT. IEEE Trans. Nucl. Sci. 51(5), 2505–2513 (2004)

    Google Scholar 

  5. Candes, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Google Scholar 

  6. Samuel, J.L., Emil, Y.S., Xiaochuan, P.: Accurate image reconstruction from few-view and limited-angle data in diffraction tomography. J. Opt. Soc. Am. A 25(7), 1772–1782 (2008)

    Google Scholar 

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

    Google Scholar 

  8. Kachelriess, M., Knaup, M., Bockenbach, O.: Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware. J. Med. Phys. 34(4), 1474–1486 (2007)

    Google Scholar 

  9. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings on Computer Vision, Bombay, pp. 839–846 (1998)

    Google Scholar 

  10. Wenxuan, S., Jie, L., Minyuan, W.: An image denoising method based on multiscale wavelet thresholding and bilateral filtering. Wuhan Univ. J. Nat. Sci. 15(2), 148–152 (2010)

    Google Scholar 

  11. Buades, A., Coll, B., Morel Song, J.M.: A review of image denoising algorithms, with a new one. SIAM J. Multiscale Model. Simul. 4(2), 490–530 (2005)

    Google Scholar 

  12. Shreyamsha Kumar, B.K.: Image denoising based on non-local means filter and its method noise thresholding. SIViP 7(6), 1211–1227 (2013)

    Google Scholar 

  13. Mustafa, Z.A., Kadah, Y.M.: Multi resolution bilateral filter for MR image denoising. In: Proceedings of 1st Middle East Conference on Biomedical Engineering (MECBME), Sharjah, pp. 180–184 (2011)

    Google Scholar 

  14. Li, K., Zhang, R.: Multiscale wiener filtering method for low-dose CT images. In: Proceedings of IEEE Biomedical Engineering and Informatics, pp. 428–431 (2010)

    Google Scholar 

  15. Li, Y., Yi, X., Xu, J., Li, Y.: Wavelet packet denoising algorithm based on correctional wiener filtering. J. Inf. Comput. Sci. 10(9), 2711–2718 (2013)

    Google Scholar 

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

    Google Scholar 

  17. Marpe, D., Cycon, H.L., Zander, G., Barthel, K.U.: Context-based denoising of images using iterative wavelet thresholding. In: Proceedings of SPIE on Visual Communications and Image Processing, pp. 907–914 (2002)

    Google Scholar 

  18. Cristobal, G., Cuesta, J., Cohen, L.: Image Filtering and denoising through the scale transform. In Proceedings of the International Symposium on Time-Frequency and Time-Scale Analysis, Pittsburgh, pp. 617–620 (1988)

    Google Scholar 

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

    Google Scholar 

  20. Donoho, D.L.: Denoising by soft thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  24. Donoho, D.L., Duncan, M.R.: Digital Curvelet Transform: Strategy, Implementation and Experiments. Standford University, Standford (1999)

    Google Scholar 

  25. Bhadauria, H.S., Dewal, M.L.: Performance evaluation of curvelet and wavelet based denoising methods on brain computed tomography images. IEEE International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 666–670 (2011)

    Google Scholar 

  26. Liu, X., Tanaka, M., Okutomi, M.: Single image noise level estimation for blind denoising. IEEE Trans. Image Process. 22(12), 5226–5237 (2013)

    Google Scholar 

  27. Borsdorf, A., Raupach, R., Flohr, T., Hornegger, J.: Wavelet based noise reduction in CT-images using correlation analysis. IEEE Trans. Med. Imaging 27(12), 1685–1703 (2008)

    Google Scholar 

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Authors and Affiliations

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

    Manoj Kumar &  Manoj Diwakar

Authors
  1. Manoj Kumar
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  2. Manoj Diwakar
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Correspondence to Manoj Kumar .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Chankyapuri, Rm 327, South Asian Univ, Akbar Bhawan, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Manoj Kumar, Manoj Diwakar (2016). Edge Preservation Based CT Image Denoising Using Wavelet and Curvelet Transforms. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_64

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_64

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0447-6

  • Online ISBN: 978-981-10-0448-3

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