Advertisement

Reducing Impurities in Medical Images Based on Curvelet Domain

  • Vo Thi Hong TuyetEmail author
  • Nguyen Thanh Binh
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 144)

Abstract

Medical image quality greatly affects the diagnostic process. Most of the tasks of increasing the quality of medical images are deblurring or denoising process. These tasks are the difficult problems in medical image processing because they must keep edge features. In the cases, the medical images that have blur combined with noise are a more difficult problem. In this paper, we proposed a method for reducing impurities in medical images based on curvelet domain. The proposed method uses curvelet coefficient combined with augmented lagrangian function to denoising combined with deblurring in medical images. For evaluating the results of the proposed method, we have compared the results with the other recent methods available in literature.

Keywords

Deblurring Denoising Curvelet transform Augmented lagrangian method Medical image 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Strang, G.: Wavelets and dilation equations: A brief introduction. SIAM Review 31(4) (1989)Google Scholar
  2. 2.
    Edwards, T.: Discrete Wavelet Transforms: Theory and Implementation (1992)Google Scholar
  3. 3.
    Kociolek, M., Materka, A., Strzelecki, M., Szczypínski, P.: Discrete Wavelet transform – derived features for digital image texture analysis. In: Proc. of International Conference on Signals and Electronic Systems, pp. 163–168 (2001)Google Scholar
  4. 4.
    Binh, N.T., Khare, A.: Image Denoising, Deblurring and Object Tracking, A new Generation wavelet based approach. LAP LAMBERT Academic Publishing (2013)Google Scholar
  5. 5.
    Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Img. Processing, 2091–2106 (2005)Google Scholar
  6. 6.
    da Cunha, A.L., Zhou, J., Do, M.N.: Nonsubsampled Contourlet Transform: Theory, Design, and Applications. IEEE Trans. Img. Proc, 3089–3101 (2005)Google Scholar
  7. 7.
    da Cunha, A.L., Zhou, J., Do, M.N.: Nonsubsampled Contourlet Transform: Filter design and applications in denoising (2006)Google Scholar
  8. 8.
    Candes, E.J.: Ridgelets: Theory and Applications. Stanford University (1998)Google Scholar
  9. 9.
    Zhang, B.J., Fadili, M., Starck, J.L.: Wavelets, ridgelets and curvelets for poisson noise removal. IEEE Transactions on Image Processing, 1093–1108 (2008)Google Scholar
  10. 10.
    Starck, J.L., Candès, E.J., Donoho D.L.: The curvelet transform for image denoising. IEEE Trans. Image Processing, 670–684 (2002)Google Scholar
  11. 11.
    Binh, N.T., Khare, A.: Multilevel threshold based image denoising in curvelet domain. Journal of Computer Science and Technology, 632–640 (2010)Google Scholar
  12. 12.
    Chan, S.H., Khoshabeh, R., Gibson, K.B., Gill, P.E., Nguyen, T.Q.: An Augmented Lagrangian Method for Total Variation Video Restoration. IEEE Trans. Image Process. 20(11), 3097–3111 (2011)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Khare, A., Tiwary, U.S.: A new method for deblurring and denoising of medical images using complex wavelet transform. IEEE (2005)Google Scholar
  14. 14.
    Ruikar, S.D., Doye, D.D.: Wavelet Based Image Denoising Technique. International Journal of Advanced Computer Science and Applications 2(3) (2011)Google Scholar
  15. 15.
    Candes, E.J., Demanet, L., Donoho, D.L., Ying, L.: Fast Discrete Curvelet Transforms. Multiscale Modeling and Simulation 5, 861–899 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Lina, J.M., Mayrand, M.: Complex Daubechies Wavelets. Journal of Applied and Computational Harmonic Analysis 2, 219–229 (1995)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam

Personalised recommendations