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A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

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Abstract

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran, Iran

    Mohamad Forouzanfar & Hamid Abrishami Moghaddam

  2. GRAMFC Unité de Génie Biophysique et Médical, Faculté de Médecine, 3 rue des Louvels, 80036, Amiens, France

    Hamid Abrishami Moghaddam

  3. Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran

    Masoumeh Gity

Authors
  1. Mohamad Forouzanfar
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  2. Hamid Abrishami Moghaddam
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  3. Masoumeh Gity
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Correspondence to Hamid Abrishami Moghaddam.

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Forouzanfar, M., Moghaddam, H.A. & Gity, M. A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images. SIViP 4, 359–375 (2010). https://doi.org/10.1007/s11760-009-0126-3

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  • DOI: https://doi.org/10.1007/s11760-009-0126-3

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