Medical imaging technology is becoming an important integral of larger number of applications such as diagnosis, research and analysis. Medical images like X-Ray, MRI, PET and CT have minute to minute information about brain and whole body. So the images should be exact and free of noise. Noise reduction shows the necessary role in medical imaging. There are various methods for removal of noise like filters, wavelets and thresholding. These techniques produced good results but still have some faults. The limitations of the previous technique are considering and analyzing this research and the new proposed technique presents Bilateral and Gabor Filter along with Neuro-Fuzzy and LDA as an efficient tool for noise reduction. Several published algorithms and each access has its assumptions, advantages, and limitations. This research presents a review of some important work in the area of image denoising. The proposed method gives more clear image with higher PSNR , MSE and improved SSIM value than the previous methods. In this research , the techniques used for proposed work are discussed. Noise removal from magnetic resonance images is important for further processing and visual analysis. In edge preservation Image De-noising using Bilateral filter is more effective. The proposed iterative bilateral filter polishes the denoising efficiency, preserves the fine structures and also reduces the bias due to noise. The visual and diagnostic nature of the image is well preserved. The quantitative analysis based on the basic metrics like peak signal-to-noise ratio and mean structural similarity index matrix display that the proposed method works better than the other recently proposed denoising methods for MRI. Thresholding neural networks (TNN) with a new class of easy nonlinearfunction have been widely used to enhance the efficiency of the denoising procedure. Our proposed work will be done using the Bilateral and Gabor Filter along with Neuro-Fuzzy and LDA.
Toshihiro Nishimura, MasakuniOshiro, “US Image Improvement Using Fuzzy NeuralNetwork with Epanechnikov Kernel”, 978-1-4244-4649-0/09/ ©2009 IEEE.
Mr. S. Hyder Ali, Dr.(Mrs.) R. Sukanesh, Ms. K. Padma Priya “ Medical image de-noising using neural networks”.
RehmanAmjad, SulongGhazali, Saba Tanzila “An intelligent approach to image denoising”, (JATIT 2005-2010).
SontakkeTrimbak R, RaiRajeshKumar, “Implementation of image de-noising using thresholding techniques”, IJCTEE.
Aja-Fernández, S., Alberola-López, C., Westin, C.F.: Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach. IEEE Trans. Image Process. 17, 1383–1398 (2008).
Lysaker, M.; Lundervold, A.; Xue-Cheng Tai, \"Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,\" Image Processing, IEEE Transactions on , vol.12, no.12, pp.1579,1590, Dec. 2003 .
Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., Garciá-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using non-local means. Med. Image Anal. 12, 514–523 (2008) .
E.Salari, S. Zhang ,“Image de-noising using neural network based non-linear filter in wavelet domain”, 0-7803-8874-7/05/IEEE(2005)
F.Marvasti, N.sadati, S.M.E Sahraeia, “ Wavelet image De-noising based on neural network and cycle spinning” 1424407281/07/IEEE(2007).
Dr. T.Santhanam, S.Radhika, “Applications of neural networks for noise and filter classification to enhance the image quality”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011 (IJCAI 2011).
Al-SobouYazeed A. (2012) “Artificial neural networks as an image de-noising tool” World Appl. Sci. J., 17 (2): 218-227, 2012
AkutagawaMastake, ChanYongjia, Katayama Masato, YohsukeKinouchi, QinyuZhang,“Additive and multiplicative noise reduction by back propagation neural network”, Proceedings of the 29th Annual International Conference of the IEEE EMBS Internationale, Lyon, France August 23-26, 2007 IEEE(2007).
R. Riji, JenyRajan, Jan Sijbers, Madhu S. Nair “Iterative bilateral filter for Rician noise reduction in MR images”, Received: 31 October 2012 / Revised: 22 December 2013 / Accepted: 23 December 2013 © Springer-Verlag London 2014.
A.N. Netravali and B.G. Haskell, Digital Pictures: Representation, Compression, and Standards (2nd Ed), Plenum Press, New York, NY (1995).
M. Rabbani and P.W. Jones, Digital Image Compression Techniques, Vol TT7, SPIE Optical Engineering Press, Bellvue, Washington (1991).
De-noising, LDA, Gabor filter, Neuro Fuzzy, Bilateral Filter.
