Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors

TitleSpeckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors
Publication TypeJournal Article
Year of Publication2008
AuthorsRabbani, H., M. Vafadust, P. Abolmaesumi, and S. Gazor
JournalBiomedical Engineering, IEEE Transactions on
Pagination2152 -2160
Date Publishedsep.
KeywordsAlgorithms, Artifacts, biomedical ultrasonics, Computer-Assisted, expectation maximization algorithm, Gaussian noise, image diagnostics, image enhancement, Image Interpretation, medical image processing, medical ultrasound images, mixture distribution, Rayleigh noise, Reproducibility of Results, Sensitivity and Specificity, signal processing, speckle, speckle noise reduction, Ultrasonography

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.


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