A New Hybrid Approach for Denoising Medical Images

  • Deepa Bharathi
  • Sumithra Manimegalai Govindan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


The most significant feature of diagnostic medical images is the removal of impulse noise which is commonly found in medical images and to make better image quality. In recent years, technological development has significantly improved in analyzing medical imaging. This paper proposes different hybrid filtering techniques for the removal of noise, by topological approach. The hybrid filters used here are hybrid median filter [hybrid min filter (H1F) and hybrid max filter (H2F)]. These filters are treated in terms of a finite set of certain estimation and neighborhood building operations. A set of such operations is suggested on the base of the analysis of a wide variety of nonlinear filters described in the literature. It is suggested from the simulation results that the proposed scheme yields better image quality after denoising. This approach is incorporated with spatial domain and frequency domain analysis. Results obtained by hybrid filtering technique are measured by the statistical quantity measures: Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR). Overall results indicate that the enhancement quality was performed well in proposed method when compared to other filtering techniques.


Image enhancement hybrid filter PSNR RMSE denoising 


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  1. 1.
    Pitas, I., Venetsanopoulos, A.N.: Nonlinear Digital Filters: Principles and Applications. Springer, NJ (1990)MATHGoogle Scholar
  2. 2.
    Rank, K., Unbehauen, R.: An Adaptive Recursive 2-D Filter for Removal of Gaussian Noise in Images. IEEE Transactions on Image Processing, 431–436 (1992)Google Scholar
  3. 3.
    Tukey, J.W.: Nonlinear (nonsuperposable) methods for smoothing data. In: Proc. Congr. Rec. EASCOM 1974, pp. 673–681 (1974)Google Scholar
  4. 4.
    Senel, H.G., Peters, R.A., Dawant, B.: Topological Median Filter. IEEE Trans on Image Processing 11(2), 89–104 (1992)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, Y., Liang, D., Ma, H., Wang, Y.: An Algorithm for Image Denoising Based on Mixed Filter. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21-23, pp. 9690–9693 (2006)Google Scholar
  6. 6.
    Juneja, M., Mohan, R.: An Improved Adaptive Median Filtering Method for Impulse Noise Detection. International Journal of Recent Trends in Engineering 1(1), 274–278 (2009)Google Scholar
  7. 7.
    Al-amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: A Comparative Study of Removal Noise from Remote Sensing Image. International Journal of Comp. Science. 7(1), 32–36 (2010)Google Scholar
  8. 8.
    Sivakumar, R.: Denoising of Computer Tomography images using curvelet transform. ARPN Journal of Engineering and Applied Sciences 2(1), 21–26 (2007)Google Scholar
  9. 9.
  10. 10.
    Gonzalez, R., Woods, R.: Digital Image Processing. Adison -Wesley, New York (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Deepa Bharathi
    • 1
  • Sumithra Manimegalai Govindan
    • 2
  1. 1.KPR Institute of Engineering TechnologyCoimbatoreIndia
  2. 2.BannariAmman Institute of TechnologySathyamangalamIndia

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