We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Review on: Image De-noising using Bilateral Filter with Multilinear Discriminant Analysis and SVM

by Rohit Jaspal, Amandeep Ummat
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 1
Year of Publication: 2015
Authors: Rohit Jaspal, Amandeep Ummat
10.5120/21030-2887

Rohit Jaspal, Amandeep Ummat . Review on: Image De-noising using Bilateral Filter with Multilinear Discriminant Analysis and SVM. International Journal of Computer Applications. 119, 1 ( June 2015), 15-17. DOI=10.5120/21030-2887

@article{ 10.5120/21030-2887,
author = { Rohit Jaspal, Amandeep Ummat },
title = { Review on: Image De-noising using Bilateral Filter with Multilinear Discriminant Analysis and SVM },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 1 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 15-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number1/21030-2887/ },
doi = { 10.5120/21030-2887 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:02:51.142411+05:30
%A Rohit Jaspal
%A Amandeep Ummat
%T Review on: Image De-noising using Bilateral Filter with Multilinear Discriminant Analysis and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 1
%P 15-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Noise removal from magnetic resonance images is important for further processing and visional analysis. Bilateral filter is known for its effective performance in edge-preserved image denoising. Here, an iterative bilateral filter is proposed for filtering the Rician noise in the magnitude magnetic resonance images. It improves the denoising efficiency. It also preserves the fine structures of the image. It also reduces the bias due to Rician noise. Thus we can preserve the quality of image. The quantitative analysis based on the standard metrics like peak signal-to-noise ratio and mean structural similarity index matrix. It shows the proposed method which performs better than the other recently proposed denoising methods for MRI.

References
  1. Wright, G. : Magnetic resonance imaging. IEEE Signal Process. Mag. 1, 56–66 (1997)
  2. Nishimura, D. G. : Principles of Magnetic Resonance Imaging. Stanford University, Stanford, CA (2010)
  3. 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)
  4. Wiest-Daesslé, N. , Prima, S. , Coupé, P. , Morrissey, S. P. , Barillo,C. : Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. In: Proceedings of MICCAI, pp. 171-179 (2008)
  5. Manjón, J. V. , Coupé, P. , Martí-Bonmatí, L. , Collins, D. L. , Robles, M. : Adaptive non local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)
  6. Sijbers, J. , den Dekker, A. J. , Van der Linden, A. , Verhoye,M. , Van Dyck, D. : Adaptive anisotropic noise filtering for magnitude MR data. Magn. Reson. Imaging 17, 1533–1539 (1999)
  7. Samsonov, A. A. , Johnson, C. R. :Noise adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magn. Reson. Med. 52, 798–806 (2004)
  8. Perona, P. , Malik, J. : Scale space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)
  9. You,Y. L. ,Kaveh,M. : Fourth order partial differential equations for noise removal. IEEE Trans. Image Process. 9, 1723–1730 (2000)
  10. Lysaker, M. , Lundervold, A. , Tai, X. C. : Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 12, 1579–1590 (2003)
  11. Basu, S. , Fletcher, T. , Whitaker, R. : Rician noise removal in diffusion tensor MRI. In: Proceedings of MICCAI, pp. 117-1-25 (2006)
  12. 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)
Index Terms

Computer Science
Information Sciences

Keywords

Bilateral filtering Magnetic resonance imaging Rician noise Image denoising Search Vector Machine Multilinear Discriminant Analysis