From Nonlinear Digital Filters to Shearlet Transform: A Comparative Evaluation of Denoising Filters Applied on Ultrasound Images

  • S. Latha
  • Dhanalakshmi SamiappanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


Ultrasound images suffer from poor bone and air penetration, lighting conditions, light scattering, bending, absorption, and reflection. The blur and noise present in the image may be removed by suitable denoising algorithms, so that the preprocessed image will provide better results in further processing. Various denoising algorithms are analyzed, and the results are compared with denoising performance evaluation metrics like PSNR, Mean Square Error, Structural Similarity, and Correlation.


(5–6) curvelet Denoise Homomorphic Speckle Ultrasound 



The authors thankfully acknowledges the financial support provided by The Institution of Engineers (India) for carrying out Research & Development work in this subject.


  1. 1.
    Xu, L., Li, J., Shu, Y., Peng, J.: SAR image denoising via clustering based principal component analysis. IEEE Trans. Geosci. Remote Sens. 52(11), 6858–6869 (2014)Google Scholar
  2. 2.
    Papari, G., Idowu, N., Varslot, T.: Fast bilateral filtering for denoising large 3D images. IEEE Trans. Image Process. 26(1), 251–261 (2017).
  3. 3.
    Ranjani, J.J., Chithra, M.S.: Bayesian denoising of ultrasound images using heavy-tailed Levy distribution. IET Image Proc. 9(4), 338–345 (2014)Google Scholar
  4. 4.
    Guo, Q., Zhang, C., Zhang, Y., Liu, H.: An efficient SVD-based method for image denoising. IEEE Trans. Circuits Syst. Video Technol. 26(5), 868–880 (2016)Google Scholar
  5. 5.
    Ramos-Llorden, G., Vegas-Sanchez-Ferrero, G., Martin-Fernandez, M., Alberola-Lopez, C., Aja Fernandez, S.: Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Proc. 24(1), 345–358 (2015)Google Scholar
  6. 6.
    Lahmiri, S.: Denoising techniques in adaptive multi-resolution domains with applications to biomedical images. Healthc. Technol. Lett. 4(1), 25–29 (2016)Google Scholar
  7. 7.
    Cao, M., Li, S., Wang, R., Li, N.: Interferometric phase denoising by median patch-based locally optimal wiener filter. IEEE Geosci. Remote Sens. Lett. 12(8), 1730–1734 (2015)Google Scholar
  8. 8.
    Devi Priyaa, K., Sasibhushana Raob, G., Subba Rao, P.S.V.: Comparative analysis of wavelet thresholding techniques with wavelet-wiener filter on ECG signal. Procedia Comput. Sci. 87, 178–183 (2016)Google Scholar
  9. 9.
    Lu, L., Jin, W., Wang, X.: Non-local means image denoising with a soft threshold. IEEE Signal Process. Lett. 22(7), 833–837 (2015)Google Scholar
  10. 10.
    Fedorov, V, Ballester, C.: Affine non-local means image denoising. IEEE Trans. Image Process. 26(5), 2137–2148 (2017)Google Scholar
  11. 11.
    Qiao, T., Ren, J., Wang, Z., Zabalza, J., Sun, M., Zhao, H., Li, S., Benediktsson, J.A., Dai, Q., Marshall, S.: Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis. IEEE Trans. Geosci. Remote Sens. 55(1), 119–133 (2017)Google Scholar
  12. 12.
    Yang, S., Min, W., Zhao, L., Wang, Z.: Image noise reduction via geometric multiscale ridgelet support vector transform and dictionary learning. IEEE Trans. Image Process. 22(11), 4161–4169 (2013)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSRM Institute of Science and TechnologyKattankulathur, KancheepuramIndia

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