Despeckling of Medical Ultrasound Images Using Fast Bilateral Filter and NeighShrinkSure Filter in Wavelet Domain

The diagnostic quality of medical ultrasound (US) images is affected by a multiplicative type of noise known as speckle noise. In this paper, a new denoising scheme based on thresholding of wavelet coefficients in different sub-bands is presented. NeighShrinkSure filter is used for thresholding detail band wavelet coefficients (high pass component). Also, as in medical US images approximation band coefficients (low pass component) also consist of speckle noise so fast bilateral is applied on these coefficients to improve the performance of proposed method. Experiments were performed on synthetic and real US images. The performance of the proposed method with four other existing methods is evaluated objectively and subjectively. Objective evaluation is carried out using parameters PSNR, SNR, SSIM and FOM and for subjective evaluation denoised US images obtained from all methods are inspected visually. The results obtained illustrate the effectiveness of the proposed method over other existing methods.

[1]  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.

[2]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[3]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.

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

[5]  Adam Krzyzak,et al.  Image denoising with neighbour dependency and customized wavelet and threshold , 2005, Pattern Recognit..

[6]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[7]  Sanjay Ghosh,et al.  On Fast Bilateral Filtering Using Fourier Kernels , 2016, IEEE Signal Processing Letters.

[8]  J. Goodman.  Some fundamental properties of speckle , 1976 .

[9]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[10]  C. Burckhardt.  Speckle in ultrasound B-mode scans , 1978, IEEE Transactions on Sonics and Ultrasonics.

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

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

[13]  Zhou Dengwen,et al.  Image denoising with an optimal threshold and neighbouring window , 2008 .

[14]  Amit Garg,et al.  Speckle noise reduction in medical ultrasound images using coefficient of dispersion , 2016, 2016 International Conference on Signal Processing and Communication (ICSC).

[15]  Kostadin Dabov,et al.  BM3D Image Denoising with Shape-Adaptive Principal Component Analysis , 2009 .

[16]  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.

[17]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[18]  Antonio Fernández-Caballero,et al.  Finding out general tendencies in speckle noise reduction in ultrasound images , 2009, Expert Syst. Appl..