Journal of Central South University

, Volume 26, Issue 1, pp 120–131 | Cite as

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

  • Bing-quan Chen (陈炳权)Email author
  • Jin-ge Cui (崔金鸽)
  • Qing Xu (徐庆)
  • Ting Shu (舒婷)
  • Hong-li Liu (刘宏立)


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.

Key words

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



为了克服医学图像采集和传输过程中的图像模糊和边缘丢失现象,提出了一种基于小波变换和 改进中值滤波的医学图像耦合去噪方法。该方法由四个模块组成:图像采集,图像存储,图像处理和 图像重建。图像采集,获取包含高斯噪声和脉冲噪声的医学图像。图像存储,包括原始图像和处理后 图像的数据、参数等信息的保存。在第三模块中,通过小波分解将医学图像分解为四个子带(LL, HL,LH,HH),其中LL 为低频部分,LH,HL,HH 分别为水平,垂直和对角线高频部分。利用改 进的小波阈值处理高频系数,保留低频系数,并在小波阈值处理后对三个高频子带进行改进中值滤波 处理。最后一个模块是图像重建,即通过小波重构去噪后的图像。这种方法的优点是结合中值滤波和 小波的优点,去噪效果更好,而不是前两种方法的简单组合。通过DWT 和改进的中值滤波器系数耦 合去噪,对于包含复杂噪声的高精度医学图像处理非常实用。将所提算法的实验结果与中值滤波,小 波变换,Contourlet 和DT-CWT 等方法的结果进行了比较。 根据视觉评价指标PSNR 和SNR 以及Canny 边缘检测得出:在低噪声图像中,PSNR 和SNR 分别提高10%~15%;在高噪声图像中,PSNR 和SNR 增加2%~6%。与其他方法相比,该算法取得了较好的结果,为医学诊断提供了重要方法。


医学图像 图像去噪 小波变换 改进中值滤波 耦合去噪 


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  1. [1]
    SENEL H G, DAWANT B. Topological median filter [J]. IEEE Trans on Image Processing, 2002, 11(2): 89–104.MathSciNetGoogle Scholar
  2. [2]
    SURESH K, PAPENDRA K, MANOJ G, ASHOK K N. Performance comparison of median and wiener filter in image de-noising [J]. International Journal of Computer Applications, 2010, 12(4): 24–28.Google Scholar
  3. [3]
    PAN Mei-sen, TANG Jing-tian, YANG Xiao-li. A modified adaptive median filter method and its applications in medical images [J]. Biomedical Engineering Applications Basis & Communications, 2012, 22(22): 489–496.Google Scholar
  4. [4]
    YI San-li, CHEN Zhen-cheng, LING Hong-li. Modified Wiener method in diffusion weighted image denoising [J]. Journal of Central South University, 2011, 18(6): 2001–2008.Google Scholar
  5. [5]
    HOSSEINI H, HESSAR F, MARVASTI F. Real-time impulse noise suppression from images using an efficient weighted average filtering [J]. IEEE Signal Processing Letters, 2014, 22(8): 1050–1054.Google Scholar
  6. [6]
    WINK A M, ROERDINK J B. Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing [J]. IEEE Transactions on Medical Imaging, 2004, 23(3): 374–87.Google Scholar
  7. [7]
    HIREMATH P S, AKKASALIGAR P T, BADIGER S. Removal of gaussian noise in despeckling medical ultrasound images [J]. International Journal of Computer Science & Applications, 2013, 1(5): 25–35.Google Scholar
  8. [8]
    IKEDA M, MAKINO R, IMAI K. 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.Google Scholar
  9. [9]
    INDULEKHA N R, SASIKUMAR M. 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.Google Scholar
  10. [10]
    ZHOU Z F, SHUI P L. Contourlet-based image denoising algorithm using directional windows [J]. Electronics Letters, 2007, 43(2): 92–93.Google Scholar
  11. [11]
    PAVITHRA R, RAMYA R, ALAIYARASI G. 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.Google Scholar
  12. [12]
    ZHOU Z F, SHUI P L. Contourlet-based image denoising algorithm using directional windows [J]. Electronics Letters, 2007, 43(2): 92–93.Google Scholar
  13. [13]
    SATHEESH S, PRASAD K. Medical image denoising using adaptive threshold based on contourlet transform [J]. Advanced Computing: An International Journal, 2011, 2(2): 52–58.Google Scholar
  14. [14]
    ALASADI A H. Contourlet transform based method for medical image denoising [J]. Computer Science Journals, 2015, 9(1): 22–31.Google Scholar
  15. [15]
    TAUJUDDIN N S A M, IBRAHIM R. Enhancement of medical image compression by using threshold predicting wavelet based algorithm [M]// Advanced Computer and Communication Engineering Technology. Berlin: Springer International Publishing, 2015: 755–765.Google Scholar
  16. [16]
    AMJAD ALI S, VATHSAL S, LAL KISHORE K. 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.Google Scholar
  17. [17]
    YANG Guang, YE Xu-jiong, GREG S, KEEGAN J, MOHIADDIN R, FIRMIN D. 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.Google Scholar
  18. [18]
    YANG A F, MIN L, TENG S H, SUN J X. Local sparse representation for astronomical image denoising [J]. Journal of Central South University, 2013, 20(10): 2720–2727.Google Scholar
  19. [19]
    XU G 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. (in Chinese)Google Scholar
  20. [20]
    CHANDRIKA SAXENA, DEEPAK KOURAV. Noises and image denoising techniques: A brief survey [J]. International Journal of Emerging Technology and Advanced Engineering, 2014, 4(3): 878–885.Google Scholar
  21. [21]
    WANG Z, BOVIK A C, SHEIKH H R, SIMONCELLI E P. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.Google Scholar
  22. [22]
    CUI J G, CHEN B Q, XU Q, DENG B. A wavelet threshold image denoising algorithm based on a new kind of sign function [J]. Telecommunications Science, 2017(1): 45–52.Google Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Physics and Electromechanical EngineeringJishou UniversityJishouChina
  2. 2.College of Information Science and EngineeringJishou UniversityJishouChina
  3. 3.College of Electrical and Information EngineeringHunan UniversityChangshaChina

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