Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

Bing-quan Chen , Jin-ge Cui , Qing Xu , Ting Shu , Hong-li Liu

Journal of Central South University ›› 2019, Vol. 26 ›› Issue (1) : 120 -131.

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Journal of Central South University ›› 2019, Vol. 26 ›› Issue (1) : 120 -131. DOI: 10.1007/s11771-019-3987-9
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Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

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Abstract

In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image, a denoising method of medical image based on discrete wavelet transform (DWT) and modified median filter for medical image coupling denoising is proposed. The method is composed of four modules: image acquisition, image storage, image processing and image reconstruction. Image acquisition gets the medical image that contains Gaussian noise and impulse noise. Image storage includes the preservation of data and parameters of the original image and processed image. In the third module, the medical image is decomposed as four sub bands (LL, HL, LH, HH) by wavelet decomposition, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and in the diagonal line high frequency component. Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients, the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing. The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction. The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better, not a simple combination of the two previous methods. With DWT and improved median filter coefficients coupling denoising, it is highly practical for high-precision medical images containing complex noises. The experimental results of proposed algorithm are compared with the results of median filter, wavelet transform, contourlet and DT-CWT, etc. According to visual evaluation index PSNR and SNR and Canny edge detection, in low noise images, PSNR and SNR increase by 10%–15%; in high noise images, PSNR and SNR increase by 2%–6%. The experimental results of the proposed algorithm achieved better acceptable results compared with other methods, which provides an important method for the diagnosis of medical condition.

Keywords

medical image / image denoising / discrete wavelet transform / modified median filter / coupling denoising

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Bing-quan Chen, Jin-ge Cui, Qing Xu, Ting Shu, Hong-li Liu. Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image. Journal of Central South University, 2019, 26(1): 120-131 DOI:10.1007/s11771-019-3987-9

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References

[1]

SenelH G, DawantB. Topological median filter [J]. IEEE Trans on Image Processing, 2002, 11(2): 89-104

[2]

SureshK, PapendraK, ManojG, AshokK N. Performance comparison of median and wiener filter in image de-noising [J]. International Journal of Computer Applications, 2010, 12(4): 24-28

[3]

PanM-s, TangJ-t, YangX-li. A modified adaptive median filter method and its applications in medical images [J]. Biomedical Engineering Applications Basis & Communications, 2012, 22(22): 489-496

[4]

YiS-l, ChenZ-c, LingH-li. Modified Wiener method in diffusion weighted image denoising [J]. Journal of Central South University, 2011, 18(6): 2001-2008

[5]

HosseiniH, HessarF, MarvastiF. Real-time impulse noise suppression from images using an efficient weighted average filtering [J]. IEEE Signal Processing Letters, 2014, 22(8): 1050-1054

[6]

WinkA M, RoerdinkJ B. Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing [J]. IEEE Transactions on Medical Imaging, 2004, 23(3): 374-87

[7]

HiremathP S, AkkasaligarP T, BadigerS. Removal of gaussian noise in despeckling medical ultrasound images [J]. International Journal of Computer Science & Applications, 2013, 1(5): 25-35

[8]

IkedaM, MakinoR, ImaiK. A new evaluation method for image noise reduction and usefulness of the spatially adaptive wavelet thresholding method for CT images [J]. Australasian Physical & Engineering Sciences in Medicine, 2012, 35(4): 475-483

[9]

IndulekhaN R, SasikumarM. An efficient method for denoising medical images using 3D DWT and bilateral filter [J]. International Journal of Innovative Research in Computer and Communication Engineering, 2015, 3(6): 5634-5642

[10]

ZhouZ F, ShuiP L. Contourlet-based image denoising algorithm using directional windows [J]. Electronics Letters, 2007, 43(2): 92-93

[11]

PavithraR, RamyaR, AlaiyarasiG. Wavelet based non local means algorithm for efficient denoising of MRI images [J]. International Journal of Advanced Research in Computer and Communication Engineering, 2015, 4: 388-392

[12]

ZhouZ F, ShuiP L. Contourlet-based image denoising algorithm using directional windows [J]. Electronics Letters, 2007, 43(2): 92-93

[13]

SatheeshS, PrasadK. Medical image denoising using adaptive threshold based on contourlet transform [J]. Advanced Computing: An International Journal, 2011, 2(2): 52-58

[14]

AlasadiA H. Contourlet transform based method for medical image denoising [J]. Computer Science Journals, 2015, 9(1): 22-31

[15]

TaujuddinN S A M, IbrahimR. Enhancement of medical image compression by using threshold predicting wavelet based algorithm [M]. Advanced Computer and Communication Engineering Technology. Berlin: Springer International Publishing, 2015755765

[16]

Amjad AliS, VathsalS, Lal KishoreK. An efficient denoising technique for CT images using window based multi wavelet transformation and thresholding [J]. European Journal of Scientific Research, 2010, 2: 315-325

[17]

YangG, YeX-j, GregS, KeeganJ, MohiaddinR, FirminD. Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images [C]. SPIE Medical Imaging. International Society for Optics and Photonics, 2016

[18]

YangA F, MinL, TengS H, SunJ X. Local sparse representation for astronomical image denoising [J]. Journal of Central South University, 2013, 20(10): 2720-2727

[19]

XuG Y. An efficient switching median filter for the removal of salt and pepper noise [J]. Journal of Anhui University of Science and Technology (Natural Science), 2017, 37(1): 33-39

[20]

ChandrikaSAXENA, DeepakKOURAV. Noises and image denoising techniques: A brief survey [J]. International Journal of Emerging Technology and Advanced Engineering, 2014, 4(3): 878-885

[21]

WangZ, BovikA C, SheikhH R, SimoncelliE P. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612

[22]

CuiJ G, ChenB Q, XuQ, DengB. A wavelet threshold image denoising algorithm based on a new kind of sign function [J]. Telecommunications Science, 2017, 1: 45-52

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