Abstract
Image denoising is an important preprocessing step done with MRI images to remove various kinds of noise like speckle noise, Gaussian noise, pepper and salt noise, etc. Some filtering mechanisms have been eliminating the required parts of the image along with the noisy pixels of the image, a phenomenon called over-filtering. Anisotropic diffusion is a denoising technique having an iterative process that computes a set of functions to acquire a good degree of smoothening without loss of actual contents of the images. A filtering technique using anisotropic diffusion and application of fuzzy logic has been presented in this paper as it has given a better sharpness of the image, with a good PSNR while it was simulated over 33 MRI cardiac images.
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References
Kazmi QA, Agrawal KK, Upadhyay V (2013) Image enhancement processing using Anisotropic diffusion. Int J Comput Sci Eng Inf Technol Res (IJCSEITR) 3(1):293–300. ISSN 2249-6831
Padmashree S, Nagapadma R (2015) Performance measure analysis between anisotropic diffusion filter and bilateral filter for post processing of fractal compressed medical images. Int J Comput Appl (0975–8887) 123(12):36–43
Tanyeri U, Demirci R (2018) Wavelet-based adaptive anisotropic diffusion filter. Adv Electr Comput Eng 18(4):99–106
Cappabianco FAM, da Silva PP (2018) Non-local operational anisotropic diffusion filter. arXiv:1812.04708v1[eess. IV], 11 Dec 2018
Kushwaha S, Singh RK, A new hybrid filtering technique for minimization of over-filtering issues in ultrasound images. https://doi.org/10.4066/biomedicalresearch.29-18-762
Keeling SL, Stollberger R (2002) Nonlinear anisotropic diffusion filtering for multiscale edge enhancement. Inv Probl 18(1):175
Palma CA, Cappabianco FAM, Ide JS, Miranda PAV (2014) Anisotropic diffusion filtering operation and limitations—magnetic resonance imaging evaluation. In: Proceedings of the 19th World Congress, the international federation of automatic control cape town, South Africa, 24–29 Aug 2014, pp 3887–3892
Kamalaveni V, Rajalakshmi RA, Narayanankutty KA (2015) Image denoising using variations of Perona-Malik model with different edge stopping functions. Procedia Comput Sci 58:673–682. In: Second international symposium on computer vision and the internet (VisionNet’15), Elsevier
Guidotti P (2015) Anisotropic diffusions of image processing from Perona-Malik. Adv Stud Pure Math 99:1–30
Wei Guo W (1999) Generalized Perona-Malik equation for image restoration. IEEE Signal Process Lett 6(7):165–167
Tang J, Sun Q, Liu J, Cao Y (2007) An adaptive anisotropic diffusion filter for noise reduction in MR images. In: 2007 international conference on mechatronics and automation, IEEE explore, Sept 2007. https://doi.org/10.1109/icma.2007.4303737
Chen Y, Yan Z, Qian YH An anisotropic diffusion model for medical image smoothing by using the lattice Boltzmann method. In: 7th Asian-Pacific conference on medical and biological engineering. IFMBE proceedings, vol 19, Springer, Berlin, Heidelberg
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Bethel GNB, Meenakshi K (2014) Design and simulation of constant bit rate compressor using fuzzy logic. In: IEEE explore, first international conference on networks & soft computing, 978-1-4799-3486-7/14/$31.00, 2014 IEEE, pp 343–347
Bethel GNB, Rajinikanth TV, Raju SV (2017) Application of pre-processing and segmentation methods on cardiac MR images. Indian J Comput Sci Eng (IJCSE–UGC approved) 8(3), June–July 2017, ISSN 0976-5166
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Bethel, G.N.B., Rajinikanth, T.V., Viswanadha Raju, S. (2020). Enhancement of Anisotropic Diffusion Filtered Cardiac MR Images Using Contrast-Based Fuzzy Approach. In: Chillarige, R., Distefano, S., Rawat, S. (eds) Advances in Computational Intelligence and Informatics. ICACII 2019. Lecture Notes in Networks and Systems, vol 119. Springer, Singapore. https://doi.org/10.1007/978-981-15-3338-9_26
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DOI: https://doi.org/10.1007/978-981-15-3338-9_26
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