Abstract
Medical imaging is a historical achievement to expose the patient’s internal functions or internal problems are seen in the external X-ray, MRI, and CT images; it gives more efficient and accurate results for the physicians to start further treatments to the patients. The medical field requires for new techniques like reduce the complications, improve the image quality, accuracy, real-time output, allow to early detection of the disease and reduce the human era. Different kind of medical images are available that are affected by various types of noise, and thus the various types of noise removal techniques are available to remove the noise. Noises are affecting the quality of real-time output. In the proposed method, these problems were addressed efficiently which in turn will enable real time procedures. The hybrid median filter (HMF) is used to remove the Rician noise. Normally, HMF was used to remove the impulse noise. This paper recommend a novel approach for denoising the magnetic resonance images that propose the both bareness and self-resemblance properties of MR images. Noise reduction is important issue for further visual examination for MR images. The hybrid median filter improves the image quality, preserves the edges, and also reduces the effect due to Rician noise. The visual analysis and diagnostic quality of the magnetic resonance images are preserved. The quantitative measurements based on the standard metrics like AD, MSE, PSNR, LMSE, SSIM shows that the recommended method is better than the other denoising methods for magnetic resonance images.
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References
Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. In: Larsen R, Nielsen M, Sporring J (eds) Medical image computing and computer-assisted intervention—MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4190. Springer, Berlin, Heidelberg
Muneeswaran V, Pallikonda Rajasekaran M (2017) Analysis of particle swarm optimization based 2D FIR filter for reduction of additive and multiplicative noise in images. In: Arumugam S, Bagga J, Beineke L, Panda B (eds) Theoretical computer science and discrete mathematics. ICTCSDM 2016. Lecture Notes in Computer Science, vol 10398. Springer, Cham
Muneeswaran V, Pallikonda Rajasekaran M (2018) Beltrami-regularized denoising filter based on tree seed optimization algorithm: an ultrasound image application. In: Satapathy S, Joshi A (eds) Information and communication technology for intelligent systems (ICTIS 2017)—Volume 1. ICTIS 2017. Smart innovation, systems and technologies, vol 83. Springer, Cham
Manjon JV, Coupe P, Buades A (2015) MRI noise estimation and denoising using non local PCA. Med Image Anal. https://doi.org/10.1016/j.media.2015.01.004
Dam EB, Lillholm M, Marques J, Nielsen M (2015) Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative. J Med Imaging (Bellingham) 2(2). https://doi.org/10.1117/1.jmi.2.2.024001. Epub 20 Apr 2015
Dodin P, Martel-Pelletier J, Pelletier JP et al (2011) A fully automated human knee 3D MRI bone segmentation using the ray casting technique. Med Biol Eng Comput 49:1413–1424. https://doi.org/10.1007/s11517-011-0838-8
He L, Greenshields IR (2009) A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging 28(2):165–172. https://doi.org/10.1109/TMI.2008.927338
Liu H, Yang C, Pan N, Song E, Green R (2010) Denoising 3D MR images by the enhanced non-local means filter for Rician noise. Magn Reson Imaging 28:1485–1496
Sharif M, Hussain A, Jaffar MA et al (2016) Fuzzy-based hybrid filter for Rician noise removal. Signal Image Video Proc SIViP 10(2):215–224. https://doi.org/10.1007/s11760-014-0729-1
Liu RW, Shi L, Huang W, Xu J, Yu SCH, Wang D (2014) Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters. Magn Reson Imaging 32:702–720
Chen L, Duan J, Chen CLP (2016) Rician noise removal in MR imaging using multi-image guided filter. In: 2016 3rd International conference on informative and cybernetics for computational social systems (ICCSS), Jinzhou, pp 180–184. https://doi.org/10.1109/iccss.2016.7586446
Riji R, Rajan J, Sijbers J et al (2015) Iterative bilateral filter for Rician noise reduction in MR images. SIViP 9(7):1543–1548. https://doi.org/10.1007/s11760-013-0611-6
Soyel H, Yurtkan K, Demirel H, McOwan PW (2016) Brain MR image denoising for Rician noise using intrinsic geometrical information. In: Abdelrahman O, Gelenbe E, Gorbil G, Lent R (eds) Information sciences and systems. Lecture Notes in Electrical Engineering, vol 363. Springer, Cham
Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190–212
Acknowledgements
The authors thank Dr. Suresh Ramasamy for supporting the research by providing orthopedic knee MR images and essential patient information. Also we show gratitude to the Department of Electronics and Communication Engineering of Kalasalingam University, Tamil Nadu, India, for authorizing to use the computational amenities accessible in Center for Research in Signal Processing and VLSI Design that was set up with the support of the Department of Science and Technology (DST), New Delhi, under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013).
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Rini, C., Perumal, B., Rajasekaran, M.P. (2019). Eradication of Rician Noise in Orthopedic Knee MR Images Using Local Mean-Based Hybrid Median Filter. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_70
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