Despeckling of ultrasound images using novel adaptive wavelet thresholding function

Randhawa, Simarjot Kaur; Sunkaria, Ramesh Kumar; Puthooran, Emjee

doi:10.1007/s11045-018-0616-y

Cited by 22 publications

(13 citation statements)

References 33 publications

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“…So far, a stable pixel position quantizer with a dynamic response bas been proposed. We adopt it here to control the threshold in (10) to e preserving ability of the ADPDE method. The adaptive threshold Assuming that it is currently in a homogeneous region, the window corresponding to pixel p has a small radius.…”

Section: Threshold Adaptive Adpde Modelmentioning

confidence: 99%

“…So far, a stable pixel position quantizer with a dynamic response based on VC has been proposed. We adopt it here to control the threshold in (10) to enhance edge-preserving ability of the ADPDE method. The adaptive threshold Ψ AT at (x, y) is obtained by…”

Section: Threshold Adaptive Adpde Modelmentioning

confidence: 99%

“…Various speckle suppression filters are proposed to solve the abovementioned challenges, which can be mainly categorized into the transform-domain ones [10,11] and spatial-domain ones [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The transform-domain filters may bring artificial frequencies in the recovered images and ubiquitously have a high computational cost due to the transformation and anti-transformation steps.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Wang

et al. 2022

Remote Sensing

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Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images.

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Section: Threshold Adaptive Adpde Modelmentioning

confidence: 99%

Section: Threshold Adaptive Adpde Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Wang

et al. 2022

Remote Sensing

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“…( 1)Where is the noisy signal, is the useful signal, and is the noise (Randhawa, 2018). The noise distribution is Gaussian white noise in Lidar.…”

Section: Wavelet Threshold Denoising Modelmentioning

confidence: 99%

Denoising of Lidar Echo Signal Based on Wavelet Adaptive Threshold Method

Long

Zhou

Wang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

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“…Such filters firstly transform image and apply despeckling operation in the transformed domain. Here, we recognize thresholding-based methods [ 32 – 34 ], coefficient correlation-based techniques and Bayesian estimation-based techniques [ 35 – 37 ]. Moreover, currently popular approach is the use of machine and deep learning methods such as Convolutional Neural Networks (CNN) [ 38 – 40 ], Residual Learning Network (ResNet) [ 41 ] or Feature-guided Denoising Convolutional Neural Network (FDCNN) [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

et al. 2022

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Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

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Despeckling of ultrasound images using novel adaptive wavelet thresholding function

Cited by 22 publications

References 33 publications

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Denoising of Lidar Echo Signal Based on Wavelet Adaptive Threshold Method

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

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