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Impulse Noise Reduction in Brain Magnetic Resonance Imaging Using Fuzzy Filters

Authors: Benjamin Y. M. Kwan, Hon Keung Kwan

Abstract:

Noise contamination in a magnetic resonance (MR) image could occur during acquisition, storage, and transmission in which effective filtering is required to avoid repeating the MR procedure. In this paper, an iterative asymmetrical triangle fuzzy filter with moving average center (ATMAVi filter) is used to reduce different levels of salt and pepper noise in a brain MR image. Besides visual inspection on filtered images, the mean squared error (MSE) is used as an objective measurement. When compared with the median filter, simulation results indicate that the ATMAVi filter is effective especially for filtering a higher level noise (such as noise density = 0.45) using a smaller window size (such as 3x3) when operated iteratively or using a larger window size (such as 5x5) when operated non-iteratively.

Keywords: brain images, Fuzzy filters, Magnetic resonance imaging, Salt and pepper noise reduction

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062284

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References:


[1]H. K. Kwan, "Fuzzy Filters for Noise Reduction in Images" in Fuzzy Filters for Image Processing, edited by M. Nachtegael, D. Van der Weken, D. Van De Ville, and E.E. Kerre, under the Series in Studies in Fuzziness and Soft Computing, volume 122, Springer Verlag, March 2003, ISBN 3-540-00465-3, chapter 2, pages 25-53.
[2]S. Aja-Fernández, C. Alberola-López, and C.-F. Westin, "Noise and signal estimation in magnitude MRI and Rician distributed images: A LMMSE Approach," IEEE Transactions on Image Processing, vol. 17, no. 8, pp. 1383-1398, August 2008.
[3]H. Liu, C. Yang, N. Pan, E. Song, and R. Green, "Denoising 3D MR images by the enhanced non-local means filter for Rician noise," Magnetic Resonance Imaging, vol. 28, issue 10, pp. 1485-1496, December 2010.
[4]Y. Xie, "On medical image filtering based on rough set theory," in Proceedings of Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2008), Jinan, Shandong, China, 18-20 October 2008, pp. 276-280.
[5]Y. S. Lalitha and V. L. Mrityunjaya, "A novel approach noise filtration for MRI image sample in medical image processing," International Journal of Computer Science and Communication (IJCSC), vol. 2, no. 2, pp. 359-363, July-December 2011.
[6]A. Toprak and I. Guler, "Suppression of impulse noise in medical images with the use of fuzzy adaptive median filter," Journal of Medical Systems, vol. 30, no. 6, pp. 465-471, 2006.
[7]I. G├╝ler, A. Toprak, A. Demirhan, and R. Karakis, "MR images restoration with the use of fuzzy filter having adaptive membership parameters," Journal of Medical Systems, vol. 32, pp. 229-234, 2008.
[8]E. Bullitt, J. K. Smith, and W. Lin, "Designed Database of MR Brain Images of Healthy Volunteers," (http://hdl.handle.net/1926/594).
[9]E. Bullitt, D. Zeng, G. Gerig, S. Aylward, S. Joshi, J. K. Smith, W. Lin, and M. G. Ewend, "Vessel tortuosity and brain tumor malignancy: A blinded study," Academic Radiology, vol. 12, pp. 1232-1240, 2005.