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An Ultrasound Image Despeckling Approach Based on Principle Component Analysis
Jawad F. Al-Asad, Ali M. Reza, Udomchai Techavipoo
Pages - 156 - 177     |    Revised - 01-06-2014     |    Published - 01-07-2014
Volume - 8   Issue - 4    |    Publication Date - July 2014  Table of Contents
Covariance Matrix, Denoising, Despeckling, Principle Component Analysis, Ultrasound Imaging.
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
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2 CiteSeerX 
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5 SlideShare 
6 PdfSR 
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, Aug. 2001.
D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory, vol. 41, no. 3, pp.613–627, May 1995.
D. Tufts and C. Melissinos, “Simple, effective computation of principal eigenvectors and their eigenvalues and application to high-resolution estimation of frequencies,” IEEE Trans.Acoust., vol. 34, no. 5, pp. 1046–1053, Oct. 1986.
E. Nadernejad, “Despeckle Filtering in Medical Ultrasound Imaging,” Contemp. Eng. Sci.,vol. 2, no. 1, pp. 17–36, 2009.
G. Georgiou and F. S. Cohen, “Statistical characterization of diffuse scattering in ultrasound images.,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 45, no. 1, pp. 57–64, Jan.1998.
G. M. Henebry, “Advantages of principal components analysis for land cover segmentation from SAR image series,” in Third ERS Symposium on Space at the service of our Environment, 1997, pp. 14–21 .
J. A. Jensen and N. B. Svendsen, “Calculation of pressure fields from arbitrarily shaped,apodized, and excited ultrasound transducers.,” IEEE Trans. Ultrason. Ferroelectr. Freq.Control, vol. 39, no. 2, pp. 262–7, Jan. 1992.
J. A. Jensen, D.- Lyngby, P. Medical, B. Engineering, and I. Technology, “Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging : Field : A Program for Simulating Ultrasound Systems Field : A Program for Simulating Ultrasound Systems,” vol.34, pp. 351–353, 1996.
J. A. Jensen, O. Holm, L. J. Jerisen, H. Bendsen, S. I. Nikolov, B. G. Tomov, P. Munk, M.Hansen, K. Salomonsen, J. Hansen, K. Gormsen, H. M. Pedersen, and K. L. Gammelmark,“Ultrasound research scanner for real-time synthetic aperture data acquisition,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 52, no. 5, pp. 881–891, May 2005.
J. A.K, Fundamental of Digital Image Processing. NJ: Prentice-Hall, 1989, pp. 267–330.
J. F. Al-Asad, A. Moghadamjoo, and L. Ying, “Ultrasound image de-noising through Karhunen-Loeve (K-L) transformwith overlapping segments,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp. 318–321.
J. Shlens, A Tutorial on Principal Component Analysis. 2009.
J.-S. Lee and K. Hoppel, “Principal components transformation of multifrequency polarimetric SAR imagery,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 4, pp. 686–696, Jul. 1992.
L. Gagnon, A. Jouan, R. De, L. M. Canada, and A. Royalmount, “Speckle Filtering of SAR Images - A Comparative Study Between Complex-Wavelet-Based and Standard Filters,”1997.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D Nonlinear Phenom., vol. 60, no. 1–4, pp. 259–268, Nov. 1992.
M.-S. Lee, C.-L. Yen, and S.-K. Ueng, “Speckle reduction with edges preservation for ultrasound images: using function spaces approach,” IET Image Process., vol. 6, no. 7, p.813, 2012.
O. Michailovich and A. Tannenbaum, “Blind Deconvolution of Medical Ultrasound Images:A Parametric Inverse Filtering Approach,” IEEE Trans. Image Process., vol. 16, no. 12, pp.3005–3019, Dec. 2007.
O. V. Michailovich and A. Tannenbaum, “Despeckling of medical ultrasound images,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 53, no. 1, pp. 64–78, Jan. 2006.
P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629–639, Jul. 1990.
R. K. Mukkavilli, J. S. Sahambi, and P. K. Bora, “Modified homomorphic wavelet based despeckling of medical ultrasound images,” in 2008 Canadian Conference on Electrical and Computer Engineering, 2008, pp. 000887–000890.
S. Gupta, R. C. Chauhan, and S. C. Saxena, “Homomorphic wavelet thresholding technique for denoising medical ultrasound images.,” J. Med. Eng. Technol., vol. 29, no. 5,pp. 208–14.
T. Brox and D. Cremers, “Iterated Nonlocal Means for Texture Restoration,” no. May, pp.13–24, 2007.
T. Loupas, W. N. McDicken, and P. L. Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasonic images,” IEEE Trans. Circuits Syst., vol. 36, no.1, pp. 129–135, Jan. 1989.
X. Hao, S. Gao, and X. Gao, “A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing.,” IEEE Trans. Med. Imaging, vol. 18, no. 9, pp. 787–94,Sep. 1999.
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, no. 4, pp. 532–40, Aug. 1998.
Y. Wang, X. Fu, L. Chen, S. Ding, and J. Tian, “DTCWT based medical ultrasound images despeckling using LS parameter optimization,” 2013 IEEE Int. Conf. Image Process., pp.805–809, Sep. 2013.
Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion.,” IEEE Trans. Image Process., vol. 11, no. 11, pp. 1260–70, Jan. 2002.
Y. Yue, M. M. Croitoru, A. Bidani, J. B. Zwischenberger, and J. W. Clark, “Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images.,” IEEE Trans. Med. Imaging, vol. 25, no. 3, pp. 297–311, Mar. 2006.
Dr. Jawad F. Al-Asad
Prince Mohammad Bin Fahd University - Saudi Arabia
Professor Ali M. Reza
University of Wisconsin-Milwaukee - United States of America
Dr. Udomchai Techavipoo
National Electronics and Computer Technology Center - Thailand