Ultrasound image despeckling using low rank matrix approximation approach

Abstract The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for despeckling of ultrasound images using weighted nuclear norm minimization (WNNM). Two methods are proposed to recover the despeckled image from the speckled data. In the first approach, the homomorphic technique is applied to the speckled ultrasound image followed by low-rank minimisation technique to despeckle the ultrasound image. In the second method, in order to enhance the despeckling capacity, a pre-processing stage is incorporated taking into consideration of the statistical properties of an ultrasound video data. Experimental results on simulated and real ultrasound data prove that the proposed methods outperform the state-of-art works.

[1]  Alin Achim,et al.  Novel Bayesian multiscale method for speckle removal in medical ultrasound images , 2001, IEEE Transactions on Medical Imaging.

[2]  Lili Wu,et al.  Speckle filtering of medical ultrasonic images using wavelet and guided filter. , 2016, Ultrasonics.

[3]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[4]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[5]  Alexander A. Sawchuk,et al.  Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Kenneth E. Barner,et al.  Rayleigh-Maximum-Likelihood Filtering for Speckle Reduction of Ultrasound Images , 2007, IEEE Transactions on Medical Imaging.

[7]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..

[8]  Lei Zhang,et al.  Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision , 2016, International Journal of Computer Vision.

[9]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[10]  Jing Tian,et al.  Image despeckling using a non-parametric statistical model of wavelet coefficients , 2011, Biomed. Signal Process. Control..

[11]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[12]  J.B.T.M. Roerdink,et al.  A review of wavelet denoising in MRI and ultrasound brain imaging , 2006 .

[13]  Marios S. Pattichis,et al.  Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex , 2014, Int. J. Biomed. Imaging.

[14]  Carl-Fredrik Westin,et al.  Oriented Speckle Reducing Anisotropic Diffusion , 2007, IEEE Transactions on Image Processing.

[15]  Ju Zhang,et al.  An Integrated De-speckling Approach for Medical Ultrasound Images Based on Wavelet and Trilateral Filter , 2017, Circuits Syst. Signal Process..

[16]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[18]  Chen Wang,et al.  Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images , 2015, Biomed. Signal Process. Control..

[19]  Xiaorong Gao,et al.  Multi-Scale Nonlinear Thresholding for Ultrasonic Speckle Suppression , 1999, IEEE Trans. Medical Imaging.

[20]  Chen Wang,et al.  Comparison of Despeckle Filters for Breast Ultrasound Images , 2015, Circuits Syst. Signal Process..

[21]  Luisa Verdoliva,et al.  A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Pierre Hellier,et al.  Restoration of 3D medical images with total variation scheme on wavelet domains (TVW) , 2006, SPIE Medical Imaging.

[23]  Scott T. Acton,et al.  Ultrasound Despeckling for Contrast Enhancement , 2010, IEEE Transactions on Image Processing.

[24]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[25]  Andrew F. Laine,et al.  Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing , 1998, IEEE Transactions on Medical Imaging.

[26]  Tien D. Bui,et al.  Fast image enhancement in compressed wavelet domain , 2014, Signal Process..

[27]  Göran Salomonsson,et al.  Image enhancement based on a nonlinear multiscale method , 1997, IEEE Trans. Image Process..

[28]  Marcos Martín-Fernández,et al.  Automatic noise estimation in images using local statistics. Additive and multiplicative cases , 2009, Image Vis. Comput..

[29]  Pierrick Coupé,et al.  Nonlocal Means-Based Speckle Filtering for Ultrasound Images , 2009, IEEE Transactions on Image Processing.

[30]  Christos P. Loizou,et al.  Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery , 2014, Comput. Methods Programs Biomed..

[31]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Jong-Sen Lee,et al.  Principal components transformation of multifrequency polarimetric SAR imagery , 1992, IEEE Trans. Geosci. Remote. Sens..

[33]  John Wright,et al.  Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.

[34]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[35]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[36]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Tommi S. Jaakkola,et al.  Weighted Low-Rank Approximations , 2003, ICML.