Open Access Open Access  Restricted Access Subscription or Fee Access

Analytical Aspects of MRI Denoising using Gaussian Blurred Intensity Averaging Method Disturbed by Random Noise

Ami K. Vibhakar, Mukesh Tiwari, Jaikaran Singh


Feature extraction and object recognition are two important parameters for analyzing any MRI (magnetic resonance imaging), taken by various imaging modalities. These MRI always contain random noise so Feature extraction and object recognition becomes difficult. This noise affects randomly on pixels of MRI and will change the both amplitude and phase of pixels of MRI. This causes imperfect diagnostics of dieses and due to that we cannot start a correct treatment for a body. so MRI denoising is important exercise for making correct diagnostic. There are different approaches for noise reduction, each of which has its own advantages and limitation. MRI denoising is a difficult task as fine details in medical image should not be removed during denoising process because it contains diagnostic information. Here we are suggesting, an algorithm for MRI denoising. We are doing intensity averaging of pixels which provides kind of smoothing to the image. Intensity averaging is performed by iterations and Gaussian blurring. This will reconstruct noisy MR image. Performance matrices used to measure the quality of denoised MRI are PSNR(Peak signal to noise ration), MSE(Mean square error) and RMSE(Root mean square error) .The final result shows that this method is effectively removing the noise while preserving the edge and fine information in the images.


MRI, Random Noise, Iteration, Gaussian Blur, Convolution, Anisotropic Diffusion, Psnr, Mse, Image Denoising

Full Text:



Isaac Bankman, “Handbook of Medical Imaging”, Academic Press, 2000.

Mukesh C. Motwani, Mukesh C. Gadiya, Rakhi C. Motwani and Frederick C. Harris “Survey of Image Denoising Techniques”

J.Rajeesh, R.S.Moni, S.Palanikumar & T.Gopalakrishnan “Noise Reduction in Magnetic Resonance Images using Wave Atom Shrinkage” International Journal of Image Processing (IJIP), Volume(4) : Issue(2) 2010 pp: 131-141.

Milindkumar V. Sarode, Dr. Prashant R . Deshmukh “ Performance Evaluation of Noise Reduction Algorithm in Magnetic Resonance Images ” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, March 2011

Milindkumar V. Sarode, Dr. Prashant R. Deshmukh “Performance Evaluation of Rician Noise Reduction Algorithm in Magnetic Resonance Images” Journal of Emerging Trends in Computing and Information Sciences Volume 2 Special Issue 2010-11 CIS Journal ISSN 2079- 8407 pp 39-44.

YangWang and Haomin Zhou” Total VariationWavelet-Based Medical Image Denoising” Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2006, Article ID 89095

S. Satheesh, Dr.KVSVR Prasad,” Medical image denoising using adaptive threshold based on contourlet transform", Advanced Computing: An International Journal ( ACIJ ), Vol.2, No.2, March 2011

José V. Manjón, Neil A. Thacker, Juan J. Lull, Gracian Garcia-Martí, Luís Martí- Bonmatí, Montserrat Robles “Multicomponent MR Image Denoising” Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2009, Article ID 7 56897, 10 pages doi: 10.1155/2009/756897

Miguel E. Soto, Jorge E. Pezoa and Sergio N. Torres Thermal Noise Estimation and Removal in MRI: A NoiseCancellation Approach” 2011

Marius Lysaker, Arvid Lundervold, and Xue-Cheng 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, December 2003

Dylan Tisdall and M. Stella Atkins” MRI denoising via phase error estimation” Proc. of SPIE Vol. 5747 (SPIE, Bellingham, WA, 2005) 1605-7422/05/$15 · doi: 10.1117/12.595677. pp: 646-654

Frederick M. Waltz and John W. V. Miller” An efficient algorithm for Gaussian blur using finite-state machines” SPIE Conf. on Machine Vision Systems for Inspection and Metrology VII, published in Boston, Nov. 1998

Gedraite, E.S.; Hadad, M.” Investigation on the effect of a Gaussian Blur in image filtering and segmentation” IEEE conference publication held at Zadar, ISSN- 1334-2630, Issue Date : 14-16 Sept. 2011

Restrepo, A.; Bovik, A.C.” Adaptive trimmed mean filters for image restoration”IEEE Journals and magazines, volume 36, Issue 8, ISSN: 0096-3518, Aug-2002.

Qiang Wang; Yi Shen” The effects of fusion structures on image fusion performances”, IEEE conference publication, Volume: 1,DOC:- 18-20 May 2004, ISSN: 1091-5281

Pietro Perona and Jitendra Malik (July 1990). "Scale-space and edge detection using anisotropic diffusion". IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (7): 629–639. DOI:10.1109/34.56205.

Yu-Li You; Wenyuan Xu; Tannenbaum, A.; Kaveh, M.” Behavioral analysis of anisotropic diffusion in image processing” IEEE Journals, Volume: 5 ,Issue 11 ,DOI: 10.1109/83.541424, ISSN : 1057-7149


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.