MRI Medical Image Denoising by Fundamental Filters

Volume 2, Issue 1, February 2017     |     PP. 12-26      |     PDF (772 K)    |     Pub. Date: February 27, 2017
DOI:    335 Downloads     8009 Views  

Author(s)

Hanafy M. Ali, Computers and Systems Engineering Department, Faculty of Engineering, Minia University, El Minia, Egypt

Abstract
Nowadays Medical imaging technique Magnetic Resonance Imaging (MRI) plays an important role in medical setting to form high standard images contained in the human brain. MRI is commonly used once treating brain, prostate cancers, ankle and foot. The Magnetic Resonance Imaging (MRI) images are usually liable to suffer from noises such as Gaussian noise, salt and pepper noise and speckle noise. So getting of brain image with accuracy is very extremely task. An accurate brain image is very necessary for further diagnosis process. During this paper, a median filter algorithm will be modified. Gaussian noise and Salt and pepper noise will be added to MRI image. A proposed Median filter (MF), Adaptive Median filter (AMF) and Adaptive Wiener filter (AWF) will be implemented. The filters will be used to remove the additive noises present in the MRI images. The noise density will be added gradually to MRI image to compare performance of the filters evaluation. The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).

Keywords
MRI image, De-noising, Non-linear filter, Median filter, Adaptive filter and Adaptive Median filter.

Cite this paper
Hanafy M. Ali, MRI Medical Image Denoising by Fundamental Filters , SCIREA Journal of Computer. Volume 2, Issue 1, February 2017 | PP. 12-26.

References

[ 1 ] J.Rajeesh, R.S.Moni, S.Palanikumar and 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.
[ 2 ] M. Zhang and B.K. Gunturk,” Multi resolution bilateral filtering for image de-noising”, IEEE Trans. Image Process. 17 (12) (2008),PP. 2324–2333, 2008.
[ 3 ] A. Phophalia, A. Rajwade and S.K. Mitra, Rough set based image de-noising for brain MR images, Signal Process. 103(2014), PP.24–35, 2014.
[ 4 ] Iza Sazanita Isa, Siti Noraini Sulaiman, Muzaimi Mustapha and Sailudin Darus,” Evaluating De-noising Performances of Fundamental Filters for T2-Weighted MRI Images”, 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science, Vol. 60 , pp. 760 – 768, 2015.
[ 5 ] R. Rahmat, A. S. Malik, and N. Kamel,” Comparison of LULU and Median Filter for Image De-noising”, International Journal of Computer and Electrical Engineering, Vol. 5, No. 6, December 2013.
[ 6 ] N. Dey , A. S. Ashour,, S. Beagum, D. Sifaki Pistola , M. Gospodinov , Е. Gospodinova and R. S. Tavares,” Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising”, www.mdpi.com/journal/jimaging, J. Imaging 2015.
[ 7 ] C. Lakshmi Devasena, and M. Hemalatha,” Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique”, International Journal of Computer Applications (0975 – 8887), Volume 27, No.8, August 2011.
[ 8 ] S. A. Akar, “Determination of optimal parameters for bilateral filter in brain MRimage denoising”, Applied Soft Computing 43 (2016) 87–96,2016.
[ 9 ] A. Bovik, “Handbook of Image and Video Processing”. New York: Academic, 2000.
[ 10 ] R. Bourne,” Image filters. In Fundamentals of Digital Imaging in Medicine”, Springer London, 2010.
[ 11 ] K. Patel and H. Mewada, “A Review on Different Image De-noising Methods”, International Journal on Recent
[ 12 ] M. Erturk,” De-noising MRI using spectral subtraction”, IEEE Transaction on Bio-Medical Engineering,Vol. 60, No. 6, June 2013.
[ 13 ] J. Mohan, V. Krishnaveni, and Y. Guo,” A New Neutrosophic Approach of Wiener Filtering for MRI Denoising”, MEASUREMENT SCIENCE REVIEW, Volume 13, No. 4, 2013.
[ 14 ] Shuqian Luo ,” Filtering medical image using adaptive filter” Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE,pp. 2727 – 2729, vol.3,2001.
[ 15 ] L. Lin , X. Meng , and X. Liang,” Reduction of impulse noise in MRI images using block-based adaptive median filter”, Medical Imaging Physics and Engineering (ICMIPE), 2013 IEEE International Conference on 19-20 Oct. 2013, pp.132-134,2013.
[ 16 ] E. Hancer, C. Ozturk, and D. Karaboga, “Extraction of brain tumors from MRI images with artificial bee colony based segmentation methodology,” 2013 8th Int. Conf. Electr. Electron. Eng., pp. 516–520, Nov. 2013.
[ 17 ] M. K. S. Sivasundari, and R. Siva Kumar, “Performance Analysis of Image Filtering Algorithms for MRI Images,” Int. J. Res. Eng. Technol., vol. 3, no. 5, pp. 438–440, 2014.
[ 18 ] C. P. Loizou, M. Pantziaris, C. S. Pattichis, and I. Seimenis, “Brain MR Image normalization in texture analysis of multiple sclerosis,” J. Biomed. Graph. Comput., vol. 3, no. 1, pp. 20–34, Nov. 2012.
[ 19 ] Lijun Bao, W. Liu , Y. Zhu , Z. Pu , and I. E. Magnin,” Sparse representation based MRI de-noising with total variation”, Signal Processing, 2008. ICSP 2008. 9th International Conference on Oct. 2008,pp. 2154 – 2157,2008.
[ 20 ] Priyadharsini B.,” A Novel Noise Filtering Technique for De-noising MRI Images”, Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT’14), Vol.2, Special Issue 1, March 2014.
[ 21 ] K. Patel and H. Mewada, “A Review on Different Image De-noising Methods”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 2 Issue 1, P. 155-159 March 2014.
[ 22 ] S. Akar, “Determination of optimal parameters for bilateral filter in brain MR image de-noising”, Elsevier Applied Soft Computing, Vol. 43, pp. 87-96,2016.