Wavelet-based Despeckling of Medical Ultrasound Images

References

• O V Michailovich, and A Tannenbaum, “Despeckling of Medical Ultrasound Images”, IEEE Transactions on Ultrasonics, Ferroelectronics, and Frequency Control, Vol. 53, no. 1, pp. 64–78, 2006.
   Google Scholar
• J W Goodman, “Statistical properties of laser speckle patterns”, in Laser Speckle and Related Phenomena., In: J C Dainty, Editor. Berlin: Springer-Verlag; pp. 9–77, 1977.
   Google Scholar
• R F Wagner, S W Smith, J M Sandrik, and H Lopez, “Statastics of speckle in ultrasound B-scans”, IEEE Trans. Sonics Ultrason., Vol. 30, no. 3, pp. 156–63, May. 1983.
   Google Scholar
• C B Burckhardt, “Speckle in ultrasound B-mode scans”, IEEE Trans. Sonics Ultrason., Vol. SU-25, no. 3, pp. 1–6, Jan. 1978.
   Google Scholar
• J S Lee, “Speckle analysis and smoothing of synthetic aperture radar images”, Comput. Graph. Image Processing, Vol. 17, no. 1, pp. 24–32, 1981.
   Google Scholar
• T Loupas, W N Mcdicken, and P L Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasound images”, IEEE Trans. Circuits Syst., Vol. 36, pp. 129–35, Jan. 1989.
   Google Scholar
• A K Jain, “Fundamentals of Digital Image Processing”, 3rd ed., Engle wood Cliffs, NJ: Prentice Hall; pp. 569, 1989.
   Google Scholar
• S G Mallat, “A Wavelet Tour of Signal Processing”, New York: Academic; pp. 620, 1998.
   Google Scholar
• X Zong, A F Laine, and E A Geiser, “Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing”, IEEE Trans. Med. Imaging, Vol. 17, pp. 532–40, 1998.
   Google Scholar
• D L Donoho, “De-noising by Soft-Thresholding”, IEEE Trans. Inform. Theory, Vol. 41, pp. 613–27, 1995.
   Google Scholar
• D L Donoho, and I Johnstone, “Adapting to unknown smoothness via wavelet shrinkage”, J. Amer. Stat. Assoc., Vol. 90, pp. 1200–24, 1995.
   Google Scholar
• E P Simoncelli, and E H Adelson, “Noise removal via Bayesian wavelet coring”, in Third Int Conf. Image Processing, Vol. 1, pp. 379–82,1996.
   Google Scholar
• A Achim, A Bezerianos, and P Tsakalides, “Novel Bayesian multiscale method for speckle removal in medical ultrasound images”, IEEE Trans. Med. Imaging, Vol. 20, no. 8, pp. 772–83, 2001.
   Google Scholar
• S Gupta, R C Chauhan, and S C Saxena, “Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution”, IEE Proc. Vis. Image Signal Process., Vol. 152, no. 1, pp. 129–35, 2005.
   Google Scholar
• S Gupta, L Kaur, R C Chauhan, and S C Saxena, “A versatile technique for visual enhancement of medical ultrasound images”, Digital Signal Processing, Vol. 17, no. 3, pp. 542–60, 2007.
   Google Scholar
• S Gupta, R C Chauhan, and S C Saxena, “Homomorphic wavelet thresholding technique for denoising medical ultrasound images”, Taylor and Francis Int. J. Med. Eng. Technol. Vol. 29, pp. 208–14, 2005.
   Google Scholar
• B I Raju, and M A Srinivasan, “Statistics of Envelope of High-Frequency Ultrasonic Backscatter form Human Skin In Vivo”, IEEE Transactions on Ultrasonics, Ferroelectronics, and Frequency Control, Vol. 49, no. 7, pp. 871–82,2002.
   Google Scholar
• P M Shankar, “A general statistical model for ultrasonic backscattering from tissues”, IEEE Transactions on Ultrasonics, Ferroelectronics, and Frequency Control, Vol. 47, no. 3, pp. 727–36, 2000.
   Google Scholar
• S Mallat, “A theory for multiresolution signal decomposition: The wavelet representation”, IEEE Trans. Patterns Anal. Machine Intell. Vol. 11, no. 7, pp. 674–92,1989.
   Google Scholar
• S Solbø, and T Eltoft, “Homomorphic Wavelet-Based Statistical Despeckling of SAR images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, no. 4, 2004.
   Google Scholar
• S G Chang, B Yu, and M Verterli, “Adaptive wavelet thresholding for image denoising and compression”, IEEE Trans. on Image Processing, Vol. 9, no. 9, pp. 1532–1546, 2000.
   Google Scholar
• P M Shankar, “Ultrasonic Tissue Characterization Using a Generalized Nakagami Model”, IEEE Transactions on Ultrasonics, Ferroelectronics, and Frequency Control, Vol. 48, no. 6, pp. 1716–20,2001.
   Google Scholar
• E W Stacy, and G A Mihram, “Parameter estimation for a generalized Gamma distribution”, Technometrics, Vol. 7, no. 3, pp. 349–57, 1965.
   Google Scholar
• M Simard, G DeGrandi, K P Thomson, and G B Benie, “Analysis of speckle noise contribution on wavelet decompositions of SAR images”, IEEE Trans. Geo-Sci. Remote Sens. Vol. 36, no. 6, pp. 1953–62, 1998.
   Google Scholar
• J H Mathews, and K K Fink, “Numerical Methods Using Matlab”, 4th ed., Prentice-Hall Inc., Englewood Cliffs, NJ, 2004.
   Google Scholar
• A Pižurica, W Philips, I Lemahieu, and M Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging”, IEEE Trans. Med. Imaging, Vol. 22, no. 3, pp. 323–331, 2003.
   Google Scholar
• F Sattar, L Floreby, G Salomonsson, and B Lovstorm, “Image enhancement based on a non-linear multiscale method”, IEEE Trans. on Image Processing, Vol. 6, no. 6, pp. 888–95, 1997.
   Google Scholar
• S Gupta, R C Chauhan, and S C Saxena, “Wavelet-based statistical approach for speckle reduction in medical ultrasound images”, Medical and Biological Engineering and Computing, Vol. 42, no 42, pp. 189–192, 2004.
   Google Scholar