A Effectual Technique of Impulse Noise Suppression for Assessing the Impact of Brain Disorders in MRI by applying Selective Weighted Median Filter
Keywords:Medical Imaging, Image processing, Biomedical Engineering
AbstractAn accurate analysis of medical images is progressively demanding in providing the absolute detection and diagnosis of diseases in medical imaging. The significant pre-processing step in MRI data processing is noise elimination. Noise deletion is essential step to increase image quality and performance of all the tasks desirable for quantitative imaging analysis. In this paper a new scheme for impulse noise removal in corrupted MRI brain images is introduced. The proposed scheme is a simple & efficient filtering technique that effectively detects and removes the salt and pepper noise. The experimental results of suggested noise purifying process executed on standard set of assessment images shows that algorithm provides a very good results with low mean-squared-error and high signal-to-noise ratio values for noise density up to 95% and outperforms significant tradeoff between fine detail preservation and noise removal in brain MRI images.
(1) M. a. Yousuf and M. N. Nobi, “A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images,” J. Sci. Res., vol. 3, no. 1, 2011.
(2) Chan, R.H., Ho, C.W., and Nikolova, M, “Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization,” IEEE Trans. Image process, pp. 1479-1485, 2005.
(3) Madhu S. Nair, K. Revathy, and Rao Tatavarti, “Removal of Salt-and Pepper Noise in Images: A New Decision-Based Algorithm”, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2008 Vol I IMECS 2008, 19-21 March, 2008, Hong Kong.
(4) Sajjad Mohsin, Sadaf Sajjad, Zeeshan Malik, and Abdul Hanan Abdullah, “Efficient Way of Skull Stripping in MRI to Detect Brain Tumor by Applying Morphological Operations, after Detection of False Background”, International Journal of Information and Education Technology, Vol. 2, No. 4, August 2012.
(5) K. S. Srinivasan, D. Ebenezer, “A New Fast and Efficient Decision- Based Algorithm for Removal of High-Density Impulse Noises,” IEEE Signal Processing Papers, Vol. 14, No. 3, pp. 189-192, March 2007.
(6) H. Hwang and R. A. Haddad, “Adaptive median filters: New algorithms and results,” IEEE Trans. Image Process., Vol. 4, No. 4, pp. 499–502, Apr. 1995.
(7) Geoffrine Judith.M.C, and N.Kumarasabapathy. “Study and analysis of impulse noise reduction filters”, Signal & Image Processing: An International Journal (SIPIJ), Vol.2 (1), pp.82-92, 2011.
(8) Anjanappa C and Sheshadri H.S., “Comparative Analysis of Efficient Impulse Noise Removal Techniques applied to Medical Images based on Mathematical Morphology”, International Research Journal of Medical Sciences ISSN 2320 –7353 Vol. 3(9), 1-12, September (2015).
(9) B. Kwan and H. Kwan, “Impulse noise reduction in brain magnetic resonance imaging using fuzzy filters,” World Academy of Science, Engineering Technology, vol. 60, pp. 1194–1197, 2011.
(10) E. George and M. Karnan, “MRI Brain Image Enhancement Using Filtering Techniques”, International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345 Vol. 3 No. 9 Sep 2012.
(11) K. Somasundaram, T.Kalaiselvi, “Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations”, Computers in Biology and Medicine 41 (2011) Elsevier Ltd, pp. 716–725
(12) Chih-Yuan Lien, Chien-Chuan Huang, Pei-Yin Chen, Yi-Fan Lin, "An Efficient Denoising Architecture for Removal of Impulse Noise in Images", IEEE Transactions on Computers, vol.62, no. 4, pp. 631-643, April 2013, doi:10.1109/TC.2011.256
(13) I. Shanthi, Dr. M.L. Valarmathi, “Speckle Noise Suppression of SAR Color Image using Hybrid Mean Median Filter”, International Journal of Computer Applications (0975 – 8887) Volume 31– No.9, October 2011
(14) Anisha K K and Dr.M.Wilscy, “Impulse Noise Removal from Medical Images Using Fuzzy Genetic Algorithm”, The International Journal of Multimedia & Its Applications (IJMA) Vol.3, No.4, November 2011.
(15) Jin Hyuk Hong, Sung Bae Cho and Ung Keun Cho (2009) “A Novel Evolutionary Method to Image Enhancement Filter Design: Method and Applications”, IEEE Transactions on Systems, Man and Cybernetics – Part B, Cybernetics, Vol. 39, No. 6, pp. 1446—1457.
(16) Sung JeaKo and Yong Hoon Lee (1991) “Center Weighted Median Filters and their application to Image Enhancement”, IEEE Transactions on Circuits and Systems, Vol 38, No 9.
(17) Chen C., Wng J., Quin W. and Dong X., “A new adaptive weight
algorithm for Salt and Pepper noise removal”, Consumer Electronics, Communication and Networks, pp.26-29, 2011.
(18) Kenny K.V. Toh and Nor A.M. Isa., “Noise adaptive fuzzy switching median filter for Salt-and- Pepper noise reduction”, IEEE signal processing letters, Vol.17 (3), pp.281-284, 2010
(19) M.Ramesh, P.Priya, Punal.M.Arabi, “A Novel Approach for Efficient Skull Stripping Using Morphological Reconstruction and Thresholding Techniques”, International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 03 Issue: 01 Jan-2014
(20) K.Somasundaram, and R.Siva Shankar, “Skull stripping based on clustering and curve fitting with quadratic equations”, ICMMSC, CCIS 283, PP 439-444. Springer – Verlag Berling Heidelberg 2012.
(21) R.C.Gonzales amd R.E.Woods, Digital image processing, Second edition, Prentice Hall, 2002.
(22) Rosniza Roslan, Nursuriati Jamil and Rozi Mahmud, “Skull Stripping Magnetic Resonance Images Brain Images: Region growing versus Mathematical Morphology”, International Journal of Computer Information Systems and Industrial Management Applications, Vol 3, pp 150-158, 2011.