Speckle reduction in breast cancer ultrasound images by using homogeneity modified bayes shrink

Abstract Ultrasound imaging suffers from severe artifacts caused by speckle noise. The paper introduces an algorithm for speckle noise reduction in breast cancer ultrasound images. Based on wavelet analysis and filtering, we employed a combination of homogeneity filtering and modified bayes shrink methods to remove noise while keeping the sharpness of important features. First, we replace pixel intensity by the mean of homogenous neighborhood and then, the threshold value of modified bayes shrink is employed to distinguish homogenous regions from regions with speckle noise obtained from homogeneity filtering. The proposed algorithm is called Homogeneity Modified Bayes Shrink (HMBS). A comparative study with other despeckling methods, using quantitative indices, showed the superiority of the proposed method over those methods.

[1]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

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

[3]  Zvi Friedman,et al.  The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images. , 2006, Ultrasonics.

[4]  Bo Wang,et al.  Ultrasound Speckle Reduction via Super Resolution and Nonlinear Diffusion , 2009, ACCV.

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

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

[7]  Torbjørn Eltoft,et al.  Homomorphic wavelet-based statistical despeckling of SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Dagmar Leucht,et al.  Teaching Atlas of Breast Ultrasound , 1992 .

[9]  R. S. Anand,et al.  Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images , 2014, Biomed. Signal Process. Control..

[10]  J. Baker,et al.  Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors. , 2007, Radiology.

[11]  Y. Wang,et al.  Speckle filtering of ultrasonic images using a modified non local-based algorithm , 2011, Biomed. Signal Process. Control..

[12]  Filippo Attivissimo,et al.  Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images , 2012 .

[13]  M. Oelze,et al.  An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

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

[15]  Heitor S. Ramos,et al.  Speckle reduction with adaptive stack filters , 2013, Pattern Recognit. Lett..

[16]  Rangaraj M. Rangayyan,et al.  Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer , 2006 .

[17]  Yuzhong Shen,et al.  Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter , 2011, Pattern Recognit. Lett..

[18]  Kathleen M. Harris,et al.  Analysis of False‐Negative Cancer Cases Identified with a Mammography Audit , 1999, The breast journal.

[19]  Pavel Crystal,et al.  Using sonography to screen women with mammographically dense breasts. , 2003, AJR. American journal of roentgenology.

[20]  G. R. Suresh,et al.  Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance , 2009 .

[21]  Manish Khare,et al.  Despeckling of medical ultrasound images using Daubechies complex wavelet transform , 2010, Signal Process..

[22]  Filippo Attivissimo,et al.  A Suitable Threshold for Speckle Reduction in Ultrasound Images , 2013, IEEE Transactions on Instrumentation and Measurement.

[23]  M. Giger,et al.  Computerized lesion detection on breast ultrasound. , 2002, Medical physics.

[24]  Yazhu Chen,et al.  Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image , 2008 .

[25]  Yi Shen,et al.  Gaussian wavelet based dynamic filtering (GWDF) method for medical ultrasound systems. , 2007, Ultrasonics.

[26]  Fan Zhang,et al.  Nonlinear Diffusion in Laplacian Pyramid Domain for Ultrasonic Speckle Reduction , 2007, IEEE Transactions on Medical Imaging.

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

[28]  M. Giger,et al.  Computerized detection and classification of cancer on breast ultrasound. , 2004, Academic radiology.

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

[30]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[31]  James A. Zagzebski,et al.  Essentials Of Ultrasound Physics , 1996 .