Wavelet and Total Variation Based Method Using Adaptive Regularization for Speckle Noise Reduction in Ultrasound Images

  • Nishtha Rawat
  • Manminder Singh
  • Birmohan SinghEmail author


Ultrasound (US) images are useful in medical diagnosis. US is preferred over other medical diagnosis technique because it is non-invasive in nature and has low cost. The presence of speckle noise in US images degrades its usefulness. A method that reduces the speckle noise in US images can help in correct diagnosis. This method also should preserve the important structural information in US images while removing the speckle noise. In this paper, a method for removing speckle noise using a combination of wavelet, total variation (TV) and morphological operations has been proposed. The proposed method achieves denoising by combining the advantages of the wavelet, TV and morphological operations along with the utilization of adaptive regularization parameter which controls the amount of smoothing during denoising. The work in this paper has the capability of reducing speckle noise while preserving the structural information in the denoised image. The proposed method demonstrates strong denoising for synthetic and real ultrasound images, which is also supported by the results of various quantitative measures and visual inspection.


Speckle noise Ultrasound images Wavelet transforms Total variation Morphological operations 





Total variation


Speckle reducing anisotropic diffusion


Perona Malik


Faster oriented speckle reducing anisotropic diffusion


Peak signal to noise ratio


Mean squared error


Root mean squared error


Universal Quality Index


Signal to noise ratio


Mean absolute error


Feature Similarity Index Metric


Speckle Suppression Index


Mean Preservation Speckle Suppression Index


Speckle Suppression and Mean Preservation Index


Normalized correlation


Average difference


Normalized absolute error


Structural Similarity Index Metric


Fourth order partial differential equations


Modified total variation


Split Bregman



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSLIETLongowalIndia

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