A hybrid algorithm for speckle noise reduction of ultrasound images

Singh, Karamjeet; Ranade, Sukhjeet K.; Singh, Chandan Deep

doi:10.1016/j.cmpb.2017.06.009

Cited by 51 publications

(37 citation statements)

References 41 publications

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“…Other studies have sought to improve the thresholding rule in US images. Such is the case of the speckle-noise reduction method reported by Zong et al [15], which is based on the model in (5). They studied hard and soft thresholding methods to develop a combined approach in order to reduce speckle and improve the features of the image by calculating the Wavelet Discrete Transform (DWT) formulated in [42], which describes the j levels of f (m, n), as in (12).…”

Section: A Wavelet-based Methodsmentioning

confidence: 99%

“…Many commercial US imaging systems are forced to compress the detected echo envelope signal using a logarithmic transformation due to their limited display range [16], [24]. For this reason, Equation (2) becomes (5), and it is simplified in (6).…”

Section: A Modelingmentioning

confidence: 99%

“…where σ c represents the Gaussian standard deviation in the spatial support and σ s is the standard deviation in the range domain. These two parameters control the weight decrease in the spatial domain and intensity, respectively, which indicates that the selection of such parameters is important to eliminate noise from images, considering that high values produce an excessive smoothing of the image, blurring fine structures and edges in the image, while low values produce poor noise elimination results [5]. Therefore, in (23), they spatially weight the Euclidian distance between p and ξ , while in (24) they work in the intensity domain [23], [58].…”

Section: Modern Techniquesmentioning

confidence: 99%

“…Thus, dp(u(B i ), u(B j )) is defined as Pearson's distance with γ = 0.5, which substitutes the usual L 2 -norm. However, the OBNLM filter presents certain difficulties because it can effectively remove the noise from the images that contain fine structural details, but it produces an excessive smoothing of the edges and the textures in the picture [5]. Other authors [71] presented a measurement similar to the OBNLM within the same Bayesian framework they used to define the Euclidian distance implementing the model in Equation (30).…”

Section: Nl(u)(mentioning

confidence: 99%

“…Therefore, reducing noise in US medical images is an important step that has become a pre-processing requirement in order to conduct better analyses and diagnoses in many applications (such as the visualization of body organs and object detection), without affecting critical diagnostic features in the image. However, removing speckle noise from US medical images poses a challenge because said noise cannot be uniformly modeled and is tissue-dependent, which makes its reduction even more complicated [1], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

et al. 2020

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In recent years, many studies have examined filters for eliminating or reducing speckle noise, which is inherent to ultrasound images, in order to improve the metrological evaluation of their biomedical applications. In the case of medical ultrasound images, said noise can produce uncertainty in the diagnosis because details, such as limits and edges, should be preserved. Most algorithms can eliminate speckle noise, but they do not consider the conservation of these details. This paper describes, in detail, 27 techniques that mainly focus on the smoothing or elimination of speckle noise in medical ultrasound images. The aim of this study is to highlight the importance of improving said smoothing and elimination, which are directly related to several processes (such as the detection of regions of interest) described in other articles examined in this study. Furthermore, the description of this collection of techniques facilitates the implementation of evaluations and research with a more specific scope. This study initially covers several classical methods, such as spatial filtering, diffusion filtering, and wavelet filtering. Subsequently, it describes recent techniques in the field of machine learning focused on deep learning, which are not yet well known but greatly relevant, along with some modern and hybrid models in the field of speckle-noise filtering. Finally, five Full-Reference (FR) distortion metrics, common in filter evaluation processes, are detailed along with a compensation methodology between FR and Non-Reference (NR) metrics, which can generate greater certainty in the classification of the filters by considering the information of their behavior in terms of perceptual quality provided by NR metrics. INDEX TERMS Diffusion filtering, image pre-processing, metrological evaluation, spatial filtering, speckle noise, ultrasound images, wavelet filtering.

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Section: A Wavelet-based Methodsmentioning

confidence: 99%

Section: A Modelingmentioning

confidence: 99%

Section: Modern Techniquesmentioning

confidence: 99%

Section: Nl(u)(mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

et al. 2020

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Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

Arora

Singh

Girdhar

et al. 2022

Int J Imaging Syst Tech

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Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

Rengarajan

Devasena²,

Gopu³

2023

Int J Imaging Syst Tech

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Breast cancer (BC) is the second leading cause of death in women worldwide, caused by the uncontrolled proliferation of malignant cells in the breast. It is very tough to differentiate cancer from non‐cancer cells because of their shape and structure. Therefore, early diagnosis and classification of breast tumors as benign is a hot research topic. However, existing methods for early diagnosis have shortcomings like high computational cost and low classification accuracy. To trounce those limitations, this paper presented a computer‐aided diagnosis (CAD) system employing metaheuristic algorithms and machine learning classifiers. The proposed system uses fused features of ultrasound (US) and elastography images to enhance classification accuracy. Firstly, both US and elastography images are separately preprocessed using an enhanced wavelet thresholding method to diminish speckle noise. Secondly, a new segmentation algorithm is proposed to extract the lesion region from the preprocessed US image. Then the contour of the segmented lesion is mapped on an elastography image to obtain the corresponding region of interest (ROI), and texture and morphology features are separately extracted and concatenated. Subsequently, the enhanced grasshopper optimization algorithm (EGOA) is adopted to select salient features and reduce the dimension. Finally, based on the selected features, two machine learning classifiers, linear discriminant analysis (LDA) and support vector machine (SVM), are employed to classify the lesions into benign and malignant. The effectiveness of the proposed method is validated on 520 tumor images, including both benign and malignant samples. The suggested performance has been assessed by computing the confusion matrix and receiver operating characteristic curve. The developed method, EGOA‐SVM, outperforms the other classifiers by reaching a higher classification rate of 99.4%, specificity of 100%, the sensitivity of 99.2%, and Matthew's coefficient of 0.979. The introduced method can improve the radiologist's performance in categorizing breast lesions with a good classification rate. It could be a promising tool for clinical use.

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A hybrid algorithm for speckle noise reduction of ultrasound images

Cited by 51 publications

References 41 publications

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

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