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A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Kenneth Kagoiya, Elijah Mwangi

Kenneth Kagoiya and Elijah Mwangi. A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement. International Journal of Computer Applications 166(10):1-7, May 2017. BibTeX

	author = {Kenneth Kagoiya and Elijah Mwangi},
	title = {A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {166},
	number = {10},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017914121},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Magnetic Resonance Imaging is one of the most advanced and effective medical diagnosis methods ,however the raw image data is normally corrupted by random noise from the measurement process this reduces the accuracy and reliability of the results. Denoising methods are often used to increase the Signal-to-Noise Ratio (SNR) and improve image clarity .In this paper an adaptive Non-Local Means filter is developed in which bilateral filter is used to pre-enhance the images and then multi resolution wavelet domain is used to remove coefficients that contain more noise than signal.

In the past different methods have been used to denoise MRI images but many have not taken into consideration the Rician nature of noise distribution therefore they have not been very effective .Adaptation in this case is based on frequency and spatial information obtained from the noisy image. Knowledge of level of noise is used in an optimization procedure to minimize a Rician based likelihood function and by use of square signal intensity bias is also discarded. The method is implemented in Matlab and MRI images with different level of artificial noise are denoised using the algorithm. Measures of performance values are PSNR, 37.12dB, MSE, 15.23, UQI, 0.985, SSIM, 0.894 , EPI,0.69 for a 10% noisy image. These and also visual inspection show that there is significant improvement from results obtained using stand alone methods such as Gaussian smoothing, Wiener filter, NLM filter ,bilateral filter and wavelet thresholding.


  1. R. Nowak, “Wavelet-based Rician noise removal for Magnetic Resonance Imaging,” IEEE Transactions on Image Processing, vol. 8,no. 10, pp. 1408–1419, October 1999.
  2. T. Dylan “MRI denoising via phase error estimation” , medical imaging journal proc on image processing SPIE Vol5 747 2005.
  3. Santiago,“Restoration of DWI data using a Rician LMMSE Estimator”, IEEE Transactions on on medical imaging, Vol, 27, No, 10, October 2008.
  4. J. V. Manj´om, P. Coup´e, L. Marti-bonmati, M. Robles, and D. L. Collins, “Adaptive non-local means denoising of MR images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, pp. 192–203, 2010.
  5. M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Transactions on Image Processing, vol. 12, no. 12, pp. 1579–1590, December 2003.
  6. Z. A. Mustafa, Y. M. Kadah, “Multiresolution Bilateral Filter for MR Image Denoising ,” Biomedical Engineering Department, Cairo University, IEEE 2011.
  7. R .Sudipta, “A new hybrid image denoising method”, International Journal of Information Technology and Knowledge Management July-December 2010, volume 2,p 491-497.
  8. V. Loganayaagi, “An improved Denoising Algorithm Using Wavelet Transform for Magnetic Resonance Images”, International journal of Communications and Engineering Volume 07-No 7,Issue 01 march2012.
  9. A. Pizurica, A. M. Wink, E. Vansteenkiste, W. Philips, and J. B. T. M. Roerdink, “A review of wavelet denoising in MRI and ultrasound brain imaging,” Current Medical Imaging Reviews, vol. 2, no. 2, pp. 247–260, May 2006.
  10. S. Dolui, A. Kuurstra, C. Iv´an. Salgado Patarroyo and Oleg V. Michailovich “A New Similarity Measure for Non-Local Means Filtering of MRI Images,” October 28 2011
  11. T. Tasdizen “Principal Neighborhood Dictionaries for Non-local Means Image Denoising,” IEEE Transactions on Image Processing, Vol. xx, No. x, January 2009.
  12. P. Coup´e, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “An optimized block wise nonlocal means denoising filter for 3-D magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 27, no. 4, pp. 425–441, March 2008
  13. C. Lakshmi Devasena “Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique,” International Journal of Computer Applications (0975 – 8887) Volume 27– No.8, August 2011.
  14. Palaniappan “Denoising of dynamic magnetic resonance images by combined application of wavelet filtering and Karhunen-Loeve Transform (KLT)”, Journal of Cardiovascular Magnetic resonance 2012 14(sup1) W71.
  15. J. V. Manjon “Multi-component MR image denoising”, International Journal of Biomedical imaging volume 2009, article ID756897.
  16. M. Zhang and B. K. Gunturk, ”Multiresolution Bilateral Filtering for Image Denoising”, IEEE Trans Image Process, vol. 17,pp. 2324–2333,2008.
  17. F. Luisier, T. Blu, P. J. Wolfe. “A Cure for Noisy Magnetic Resonance Images: Chi-square Unbiased Risk Estimation”, Harvard University; 15 June 2011.
  18. A. Pizuria J Adtormann, B .Gossens, W. Phillips “Removal of Correlated Rician Noise in Magnetic Resonance Imaging”, 16th European Signal processing Conference (EUSIPCO 2008), Lausanne, Switzerland, August 25- 29 2008.
  19. A.K .Jain “Fundamentals of digital image processing”, Prentice hall Engle wood cliffs, NJ USA 1989
  20. A Pizuria, Philips A W Lemahieu and Acheroy M “ A versatile wavelet domain noise filtration technique for medical imaging” ,IEEE Trans Med Imaging 2003, 22(3) pp 232-331
  21. Hossein “Wavelet Domain medical image denoising using Bivariate laplacian mixture model”, IEEE Transactions on biomedical engineering vol 56 No 12 December 2009
  22. J. V. Manjón, Pierrick Coupe, Antoni Buades. “MRI Noise Estimation and Denoising Using Non-Local PCA.’’ Medical Image Analysis, 22:35-47. 2015.
  23. J. V. Manjón, Simon F. Eskildsen, Pierrick Coupé, Jose E. Romero, D. Louis Collins, Montserrat Robles. “Non-local Intracranial Cavity Extraction. IJBI.’’  Article ID 820205. 2014.
  24. J.V. Manjon, Pierrick Coupe, Luis Concha, Antonio Buades, D. Louis Collins, Montserrat Robles. “Diffusion Weighted Image Denoising using overcomplete Local PCA.’’ PLoS ONE 8(9): e73021. doi:10.1371/journal.pone.0073021. 2013.
  25. P. Coupé, J. V. Manjon, M. Chamberland, M. Descoteaux. “Collaborative patch-based super-resolution for diffusion-weighted images.” ’NeuroImage, 83:245-261, 2013
  26. June.15, 2014
  27. June.15, 2014
  28. June.15, 2014


MRI, Rician noise, wavelet, combinational, NLM, bilateral filter, resolution