A new wavelet family for speckle noise reduction in medical ultrasound images

Leal, Andréia Seixas; Paiva, Henrique Mohallem

doi:10.1016/j.measurement.2019.03.050

Cited by 38 publications

(8 citation statements)

References 25 publications

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“…Subsequently, they used breast ultrasound image database containing 109 cases for experiment to demonstrate the improvement of classification by using the tested denoising algorithms. Further, the authors in [ 33 ] introduced a new wavelet type Usi and tested it on 110 images from various areas, however, it was tailored for the speckle noise and thus was not tested on any other noise. Evaluation criteria —Besides conventional metrics, such as MSE, PSNR and SSIM, this study includes the insights from the clinical experts, who evaluated the filtration results from the clinical point of view.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…• Calculate C h and C v according to (25) and (27) for the segmentation of shearlet coefficients in homogeneous, heterogeneous and strongly heterogeneous regions. • Calculate parameters δ and α, from (22) and (23).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This method utilises DTCWT for the obtainment of wavelet coefficients in different subbands. A new orthonormal wavelet family referred to as 'usi' was proposed in [22] for US despeckling.…”

Section: Introductionmentioning

confidence: 99%

Segmentation‐based MAP despeckling of medical ultrasound images in shearlet domain based on normal inverse Gaussian distribution

Garg

Khandelwal

2020

IET Image Processing

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“…This method removes the speckle noise very well. Leal et al [26] proposed a new orthonormal wavelet relation for image denoising. Different wavelets were analyzed and performed better in edge preservation and speckle reduction.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Manikandan¹,

Karthikeyan²,

Babu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image. Then, the system adaptively selects the better-suited filter for the specific image region, resulting in significant speckle noise suppression and retention of detailed features. The suggested approach uses a weighted average filter to distinguish between noise and edges at the filtering stage. In addition, we apply a structural similarity measure as a tuning parameter depending on the kind of noise in the ultrasound pictures. The proposed methodology shows that the proposed fuzzy adaptive filter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

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A new wavelet family for speckle noise reduction in medical ultrasound images

Cited by 38 publications

References 25 publications

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

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

Segmentation‐based MAP despeckling of medical ultrasound images in shearlet domain based on normal inverse Gaussian distribution

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

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