基于NSCT 和全变差模型的医学图像去噪

doi:10.3969/j.issn.0255-8297.2014.05.008

Journal of Applied Sciences ›› 2014, Vol. 32 ›› Issue (5): 481-485.doi: 10.3969/j.issn.0255-8297.2014.05.008

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Medical Image Denoising Using Non-subsampled Contourlet Transform and Total Variation Model

MA Xiu-li1,2, ZHOU Feng1,2, ZHOU Xiao-jun1,2

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2. Institute of Smart City, Shanghai University, Shanghai 200444, China

Online:2014-09-23 Published:2014-09-23

Abstract

Abstract: The characteristics of non-subsampled Contourlet transform (NSCT) and total variation (TV)modeling are analyzed. A mixed model of NSCT and TV is applied to medical image denoising in this paper.NSCT filter-based decomposition of noisy medical images is performed. An initial denoised image is produced using a Visu shrink threshold algorithm. The final denoised image is obtained by processing the initial denoised image with the TV model. Experimental results show that the image details are well preserved by using the proposed method. Both peak signal-to-noise ratio (PSNR) and visual quality are superior to some other denoising algorithms.

Key words: non-subsampled Contourlet transform, total variation, medical image denoising, peak signal-tonoise ratio (PSNR)

CLC Number:

TP 391

MA Xiu-li1,2, ZHOU Feng1,2, ZHOU Xiao-jun1,2. Medical Image Denoising Using Non-subsampled Contourlet Transform and Total Variation Model[J]. Journal of Applied Sciences, 2014, 32(5): 481-485.

References

[1] CHANG S Grace, YU Bin, VETTERLI Martin. An adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Processing, 2000: 1532-1546.

[2] DO M N, VETTERLI M. The contourlet transform: an efficient directional multiresolutional image representation [J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.

[3] DA Cunha A L, ZHOU J, DO M N. The nonsubsampled contourlet transform: theory, design, and applications [J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.

[4] ESLAMI R, RADHA H. Translational-invariant contourlet transform and its application to image denoising [J]. IEEE Transactions on Image Processing, 2006, 15(11): 3362-3374.

[5] Po D D Y, Do M N. Directional multiscale modeling of images using the contourlet transform [J]. IEEE Transactions on Image Processing, 2006, 15(6): 1610-1620.

[6] KANITHI Anil Kumar. Study of spatial and transform domain filters for efficient noise reduction [D]. National Institute of Technology, India, 2011.

[7] STEIDL Gabriele, WEICKERT Joachim. Relations between soft wavelet shrinkage and total variation denoising[C]//LNCS 2449, 2002: 198-205.

[8] YAN Jie, LU Wusheng. Image denoising by generalized total variation regularization and least squares fidelity [J]. Multidimensional Systems and Signal Processing, 2013.

[9] OSHER S, SOLE A, VESE L. Image decomposition and restoration using total variation minimization and the H-1 Norm [J]. SIAM Journal of Multiscale Modeling and Simulation, 2003, 1(3): 350-369.

[10] WANG Y, YANG J, YIN W, ZHANG Y. A new alternating minimization algorithm for total variation image reconstruction [J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272.

[11] HUY, JACOB M. Higher degree total variation (HDTV) regularization for image recovery [J]. IEEE Transactions on Image Processing, 2012, 21(5): 2559-2571.

Medical Image Denoising Using Non-subsampled Contourlet Transform and Total Variation Model

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