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

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Abstract

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.

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Abbreviations

US:

Ultrasound

TV:

Total variation

SRAD:

Speckle reducing anisotropic diffusion

PM:

Perona Malik

FOSRAD:

Faster oriented speckle reducing anisotropic diffusion

PSNR:

Peak signal to noise ratio

MSE:

Mean squared error

RMSE:

Root mean squared error

UQI:

Universal Quality Index

SNR:

Signal to noise ratio

MAE:

Mean absolute error

FSIM:

Feature Similarity Index Metric

SSI:

Speckle Suppression Index

MPSSI:

Mean Preservation Speckle Suppression Index

SMPI:

Speckle Suppression and Mean Preservation Index

NK:

Normalized correlation

AD:

Average difference

NAE:

Normalized absolute error

SSIM:

Structural Similarity Index Metric

FOPDE:

Fourth order partial differential equations

MTV:

Modified total variation

SB:

Split Bregman

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SLIET, Longowal, 148106, India

    Nishtha Rawat, Manminder Singh & Birmohan Singh

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  1. Nishtha Rawat
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Rawat, N., Singh, M. & Singh, B. Wavelet and Total Variation Based Method Using Adaptive Regularization for Speckle Noise Reduction in Ultrasound Images. Wireless Pers Commun 106, 1547–1572 (2019). https://doi.org/10.1007/s11277-019-06229-w

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