Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution

Gai, Shan; Zhang, Boyu; Yang, Cihui; Lei, Yu

doi:10.1016/j.dsp.2017.10.006

Cited by 63 publications

(19 citation statements)

References 41 publications

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“…The noise is basically often present in diagnostic imaging methods. Nowadays, the research has tended to focus on importance of noise reducing for example, References [ 59 , 60 , 61 ]. In connection with previous findings, reducing noise from these images is a complex task, but very important.…”

Section: Resultsmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

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Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

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Section: Resultsmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

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“…In this paper, we use the Wiener DCT-based image filtering with hard threshold. As discussed earlier, the speckle noise of medical ultrasound image is modeled as multiplicative noise and non-Gaussian distributed [35] and defined by:…”

Section: B Dct-based Ultrasound Image Filteringmentioning

confidence: 99%

“…Then, the multiplicative noise becomes the additive noise and is approximated as an additive zero mean Gaussian noise [35]. It means, we could consider g(n, m) ≈ x(n, m) + v(n, m) as the new model of ultrasound images in our coming experiments in logarithmic mode.…”

Section: B Dct-based Ultrasound Image Filteringmentioning

confidence: 99%

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

et al. 2020

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In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.

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“…Features used for image classification usually include principal component analysis (PCA), generalized 2-dimensional principal component analysis (G2DPCA), independent component analysis (ICA), and wavelet. For two-dimensional images, monogenic signal perfectly reproduces the monogenic amplitude of the signal energy, monogenic phase of the signal structure information, and monogenic orientation of the signal geometry information, which has been widely used in the field of image processing [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

Sun

2021

Wireless Communications and Mobile Computing

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It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

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Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution

Cited by 63 publications

References 41 publications

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

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