CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Independent Component Analysis based Denoising of Magnetic Resonance Images

by Neelabh Sukhatme, Shailja Shukla
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 2
Year of Publication: 2012
Authors: Neelabh Sukhatme, Shailja Shukla
10.5120/8537-2078

Neelabh Sukhatme, Shailja Shukla . Independent Component Analysis based Denoising of Magnetic Resonance Images. International Journal of Computer Applications. 54, 2 ( September 2012), 13-18. DOI=10.5120/8537-2078

@article{ 10.5120/8537-2078,
author = { Neelabh Sukhatme, Shailja Shukla },
title = { Independent Component Analysis based Denoising of Magnetic Resonance Images },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 2 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number2/8537-2078/ },
doi = { 10.5120/8537-2078 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:39.153079+05:30
%A Neelabh Sukhatme
%A Shailja Shukla
%T Independent Component Analysis based Denoising of Magnetic Resonance Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 2
%P 13-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).

References
  1. G. A. Wright, "Magnetic Resonance Imaging", IEEE Signal Processing Magazine, vol. 1, pp. 56-66, 1997.
  2. A. Macovski,"Noise in MRI", Magnetic Resonance Med, vol. 36, pp. 494-497, 1996.
  3. A. K. Jain. Fundamentals of digital image processing. Prentice Hall, Englewood Clis, NJ, c1989.
  4. E. R. McVeigh, R. M. Henkelman, and M. J. Bronskill, "Noise and filtration in magnetic resonance imaging", Med. Phys. , vol. 3, pp. 604-618, 1985.
  5. M. C. Motwani, M. C. Gadiya, R. C. Motwani, and Jr. F. C. Harris. Survey of image denoising techniques. In Proceedings of Global Signal Processing Expo and Conference (GSPx 04), Santa Clara, CA, USA, September 2004.
  6. H. G. Stark. Wavelets and Signal Processing: An Application-Based Introduction. Springer, Berlin, Heidelberg, 2005.
  7. Tinku Acharya and Ajoy K. Ray, "Image Processing-Principles and Appilications", Hoboken, New Jersey, A John Wiley & Sons, Mc. Publication, 2005.
  8. Hyvarinen, J. Karhunen, and E. Oja," Independent Component Analysis," John Wiley & Sons, New York, 2001.
  9. Balika S. Tawade, Rupali S. Kamathe, "MRI Image Denoising by the ICA & PCA", Advance Image Processing, 2011.
  10. Hyvärinen, A. , Hoyer, P. , Oja, E. : "Image Denoising by Sparse Code Shrinkage". In: Hykin, S. , Kosko, B. (eds): Intelligent Signal Processing, IEEE Press, 2001.
  11. Y. Fisher, Fractal Image Compression — Theory and Application Springer-Verlag , New York , 1995.
  12. Detail information about structural similarity index from www. en. wikipedia. org/wiki/Structural_similarity.
  13. Sachin D Ruikar, Dharmpal D Doye,"Wavelet Based Image Denoising Technique", IJACSA,Vol. 2, No. 3, pp 49-53 March 2011.
  14. M. N. Do, M. Vetterli, "The contourlet transform: an efficient directional multiresolution image representation,"IEEE Trans. on Image Process. , vol. 14, no. 12, pp. 2091–2106, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete wavelet transform (DWT) Independent component analysis (ICA) Magnetic resonance imaging(MRI) Mean square error (MSE) Mean structural similarity index metric (MSSIM Peak signal to noise ratio (PSNR)

Powered by PhDFocusTM