Ultrasound speckle reduction using adaptive wavelet thresholding

Bedi, Anterpreet Kaur; Sunkaria, Ramesh Kumar

doi:10.1007/s11045-021-00799-4

Cited by 8 publications

(18 citation statements)

References 41 publications

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“…4. The previous techniques considered are wavelet denoising [21] [23], relaxed median (RMedian) denoising [14] [15], speckle reducing anisotropic diffusion (SRAD) [17][16] [18], and local statistics based filtering (Lee) [9] [10]. In each of these techniques, the original technique is implemented with the implementation details suggested in the most recent variant.…”

Section: Resultsmentioning

confidence: 99%

Speckle Reduction in Medical Ultrasound Imaging based on Visual Perception Model

Kadah¹,

Elnokrashy²,

Alsaggaf³

et al. 2022

IJACSA

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Ultrasound imaging technology is one of the most important clinical imaging modalities due to its safety, low cost, in addition to its versatile applications. The main technical problem in this technology is its noisy appearance due to the presence of speckle, which makes reading imaging more difficult. In this study, a new method of speckle reduction in medical ultrasound images is proposed based on adaptive shifting of the contrast sensitivity function of human vision using a bias field map estimated from the original image. The aim of this work is to have an effective image enhancement strategy that reduces speckle while preserving diagnostically useful image features and allowing practical implementation in real-time for medical ultrasound imaging applications. The new method is used to improve the visual perception of image quality of ultrasound images by adding a local brightness bias to the areas with speckle noise. This allows the variations in image pixels due to speckle noise to be better perceived by the human observer because of the visual perception model. The performance of the proposed method is objectively assessed using quantitative image quality metrics and compared to previous methods. Furthermore, given that image quality perception is subjective, the level of added bias is controlled by a single parameter that accommodates the different needs for different users and applications. This method has potential to offer better viewing conditions of ultrasound images, which translates to higher diagnostic accuracy.

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Section: Resultsmentioning

confidence: 99%

Speckle Reduction in Medical Ultrasound Imaging based on Visual Perception Model

Kadah¹,

Elnokrashy²,

Alsaggaf³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The multi‐scale filters could preserve more details 37 . The most popular filters of single‐scale and multi‐scale filters are Lee, 9 Frost, 35 Kuan, 11,34 anisotropic diffusion, 16,30 and Butterworth, 36 shearlet, 38 curvelet, 39 and wavelet 13,14 …”

Section: Methodsmentioning

confidence: 99%

“…Another group is frequency domain filters, in which the original images are transformed into frequency domain before denoising. The most popular spatial and frequency domain filters are Lee, 9 Frost, 35 Kuan, 11,34 anisotropic diffusion, 16,30 Butterworth, 36 and wavelet, 13,14 respectively. The third categorization of filters is based on how images are processed, single-scale or multi-scale.…”

Section: Image Denoising Filtersmentioning

confidence: 99%

“…37 The most popular filters of single-scale and multi-scale filters are Lee, 9 Frost, 35 Kuan, 11,34 anisotropic diffusion, 16,30 and Butterworth, 36 shearlet, 38 curvelet, 39 and wavelet. 13,14 Although filters use different methods, the purpose of all filters is to reduce noise from images. In this study, a total of 67 denoising filters with free available codes by the original authors were included as follows:…”

Section: Image Denoising Filtersmentioning

confidence: 99%

“…7,8 The main aim of denoising techniques is to remove unwanted noises while keeping important useful image features. Previous studies proposed denoising algorithms based on different methods such as single-scale, [9][10][11][12] multiscale, 13,14 diffusion, 15,16 non-local means, 17,18 linear, 9,18 nonlinear, 15,19,20 or hybrid (combination of methods) 17,21 methods to overcome this issue and remove noises from ultrasound images. Previous studies implemented different approaches to develop denoising filters.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Ardakani

Mohammadi

Faeghi

et al. 2023

Int J Imaging Syst Tech

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Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full‐reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full‐reference and three no‐reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second‐best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre‐processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.

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Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Medical diagnostics rely heavily on ultrasound imaging since it is non-invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal-dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non-homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non-homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two-step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non-subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

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Ultrasound speckle reduction using adaptive wavelet thresholding

Cited by 8 publications

References 41 publications

Speckle Reduction in Medical Ultrasound Imaging based on Visual Perception Model

Speckle Reduction in Medical Ultrasound Imaging based on Visual Perception Model

Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

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