Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

Gupta, Savita; Chauhan, R. C.; Saxena, Shivam

doi:10.1049/ip-vis:20050975

Cited by 67 publications

(29 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The solution to this problem is to use the redundant discrete wavelet transform (RDWT), which omits the down-sampling operation. In this paper, the RDWT is employed which provides significant improved noise reduction performance at the cost of redundancy [8][9].…”

Section: Problem Formulationmentioning

confidence: 99%

Spatially adaptive Bayesian wavelet thresholding for speckle removal in medical ultrasound images

et al. 2007

View full text Add to dashboard Cite

In this paper, a novel spatially adaptive wavelet thresholding method based on Bayesian maximum a posteriori (MAP) criterion is proposed for speckle removal in medical ultrasound (US) images. The method firstly performs logarithmical transform to original speckled ultrasound image, followed by redundant wavelet transform. The proposed method uses the Rayleigh distribution for speckle wavelet coefficients and Laplacian distribution for modeling the statistics of wavelet coefficients due to signal. A Bayesian estimator with analytical formula is derived from MAP estimation, and the resulting formula is proven to be equivalent to soft thresholding in nature which makes the algorithm very simple. In order to exploit the correlation among wavelet coefficients, the parameters of Laplacian model are assumed to be spatially correlated and can be computed from the coefficients in a neighboring window, thus making our method spatially adaptive in wavelet domain. Theoretical analysis and simulation experiment results show that this proposed method can effectively suppress speckle noise in medical US images while preserving as much as possible important signal features and details.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Spatially adaptive Bayesian wavelet thresholding for speckle removal in medical ultrasound images

et al. 2007

View full text Add to dashboard Cite

show abstract

“…This method does not rely on exact prior knowledge of noise distribution, but employs preliminary detection of wavelet coefficients to empirically estimate the statistical distribution of signal and noise. The Bayesian framework was also explored to perform wavelet thresholding adapted to non-Gaussian statistics of signals (Achim et al 2001;S. Gupta et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter

Esakkirajan

Vimalraj²,

Muhammed

et al. 2013

Ultrasound in Medicine & Biology

View full text Add to dashboard Cite

“…The authors in [17] combined homomorphic filtering principle along with elliptical thresholding concept in complex discrete wavelet transform domain. The authors in [7] employed a maximum a posteriori (MAP) estimator in wavelet domain to estimate the noise free coefficients. The authors used Rayleigh statistical distribution to model the magnitude of speckle in the log domain and Gaussian distribution to model the wavelet coefficients of log transformed reflectivity of medical ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering

Barthakur¹,

Hazarika²,

Nath³

2013

IJCA

View full text Add to dashboard Cite

Multiplicative speckle noise which is inherently present in medical ultrasound images degrades the important clinical informations and badly affects the quality of the diagnosis. It is necessary to reduce the speckle noise to improve the visual quality of ultrasound images for better diagnoses. In this paper, a wavelet based method for despeckling of the ultrasound images is introduced where a local Wiener filter along with speckle reducing anisotropic diffusion (SRAD) filter are employed in a homomorphic framework. The signal variance in the local wiener filter is estimated from the output image of the SRAD filter. Since the size and shape of the locally adaptive window is an important issue in estimating the signal variance, nearly arbitrarily shaped windows are used for better performance. The experimental results using synthetically speckled ultrasound images show that the speckle noise is reduced to a great extent while preserving the important clinical information. In order to demonstrate the effectiveness of the proposed method, the method is compared with several other existing methods in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), edge preservation index (β), and standard deviation to mean (S/M) ratio.

show abstract

Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

Cited by 67 publications

References 15 publications

Spatially adaptive Bayesian wavelet thresholding for speckle removal in medical ultrasound images

Spatially adaptive Bayesian wavelet thresholding for speckle removal in medical ultrasound images

Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter

Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering

Contact Info

Product

Resources

About