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Noise Reduction in Magnetic Resonance Images using Wave Atom Shrinkage
J.Rajeesh, R.S.Moni, S.Palanikumar, T.Gopalakrishnan
Pages - 131 - 141     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
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KEYWORDS
De-noising, Gaussian noise, Magnetic Resonance Images, Rician noise, Wave Atom Shrinkage
ABSTRACT
De-noising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution. Unlike additive Gaussian noise, Rician noise is signal dependent, and separating signal from noise is a difficult task. An efficient method for enhancement of noisy magnetic resonance image using wave atom shrinkage is proposed. The reconstructed MRI data have high Signal to Noise Ratio (SNR) compared to the curvelet and wavelet domain de-noising approaches.
CITED BY (21)  
1 Isa, I. S., Sulaiman, S. N., Mustapha, M., & Darus, S. (2015). Evaluating Denoising Performances of Fundamental Filters for T2-Weighted MRI Images. Procedia Computer Science, 60, 760-768.
2 Dey, N., Ashour, A. S., Beagum, S., Pistola, D. S., Gospodinov, M., Gospodinova, ?. P., & Tavares, J. M. R. (2015). Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising. Journal of Imaging, 1(1), 60-84.
3 Jangra, S., & Yadav, M. S. (2014). Rician Noise Reduction in MRI Images using Wave Atom Transform.
4 Jangra, S., & Yadav, M. S. (2014). A Review of Rician Noise Reduction in MRI Images using Wave Atom Transform.
5 Bukhari, I., & Hyat, K. (2013). Wave Atom Based Watermarking. arXiv preprint arXiv:1305.3021.
6 Dorairangaswamy, M. A. (2013). An Extensive Review of Significant Researches on Medical Image Denoising Techniques. International Journal of Computer Applications, 64(14), 1-12.
7 Nair, P. C., & Suganthi, G. Comparative Analysis of Various Denoising Techniques for MRI Images.
8 Kumar, V., Saini, S., & Dhiman, S. (2012). Quality improvement on MRI corrupted with Rician noise using wave atom transform. International Journal of Computer Applications, 37(8), 28-32.
9 Khandelwal, P., Kumar, K., Singh, B., & Singh, R. (2012). A review on medical image modalities. International Journal of Computer Science and Management Research, 1, 844-853.
10 Kandpal, H., Katiyar, H., & Kumar, M. (2012, March). Wave atom-SVD based digital image watermarking scheme. In Engineering and Systems (SCES), 2012 Students Conference on (pp. 1-4). IEEE.
11 Vibhakar, A. K., Tiwari, M., & Singh, J. (2012). Analytical Aspects of MRI Denoising using Gaussian Blurred Intensity Averaging Method Disturbed by Random Noise. Digital Image Processing, 4(14), 748-752.
12 Vibhakar, M. A., Tiwari, M., Singh, J., & Rathore, S. (2012). Performance Analysis of Intensity Averaging By Anisotropic Diffusion Method for MRI Denoising Corrupted By Random Noise. Global Journal of Computer Science and Technology, 12(12-F).
13 Dua, G., & Raj, V. (2012). MRI Denoising Using Waveatom Shrinkage. GJRE-F: Electrical and Electronic Engineering, 12(4).
14 Leung, H. Y., Cheng, L. M., & Liu, F. (2012). Robust digital image watermarking scheme using wave atoms with multiple description coding. EURASIP Journal on Advances in Signal Processing, 2012(1), 1-14.
15 Vibhakar, A., Tiwari, M., & Singh, J. (2012). Performance Analysis for MRI Denoising using Intensity Averaging Gaussian Blur Concept and its Comparison with Wavelet Transform Method. International Journal of Computer Applications, 58(15).
16 Rajeesh, J., Moni, R. S., Palanikumar, S., & Gopalakrishnan, T. (2011). A versatile algorithm for the automatic segmentation of hippocampus based on level set. International Journal of Biomedical Engineering and Technology, 7(3), 213-224.
17 Leung, H. Y., & Cheng, L. M. (2011). Robust watermarking scheme using wave atoms. EURASIP Journal on Advances in Signal Processing, 2011, 3.
18 Leung, H. Y., & Cheng, L. M. (2011). Robust blind watermarking scheme using wave atoms. In Digital Watermarking (pp. 148-158). Springer Berlin Heidelberg.
19 Devasena, C. L., & Hemalatha, M. (2011). Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique. METHODOLOGY, 27(8).
20 Sarode, M. V., & Deshmukh, D. P. R. (2011). Performance Evaluation of Noise Reduction Algorithm in Magnetic Resonance Images. IJCSI International Journal of Computer Science Issues, 8(2), 198-202.
21 Rajeesh, J., Moni, R. S., Kumar, S. P., & Gopalakrishnan, T. (2010, October). Rician noise removal on MRI using wave atom transform with histogram based noise variance estimation. In Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on (pp. 531-535). IEEE.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PDFCAST 
14 PdfSR 
15 Free-Books-Online 
1 W. A. Edelstein, P. A. Bottomley, and L. M. Pfeifer.A signal-to-noise calibration procedure for nmr imaging systems. Med. Phys 1984;11:2:180–185.
2 E. R. McVeigh, R. M. Henkelman, and M. J. Bronskill. Noise and filtration in magnetic resonance imaging. Med. Phys 1985;12:5:586–591.
3 R. M. Henkelman. Measurement of signal intensities in the presence of noise in mr images. Med. Phys 1985;12:2:232–233.
4 M. A. Bernstein, D. M. Thomasson, and W. H. Perman. Improved detectability in low signal-to-noise ratio magnetic resonance images by means of phase-corrected real construction. Med. Phys 1989;16:5:813–817.
5 M. L. Wood, M. J. Bronskill, R. V. Mulkern, and G. E. Santyr. Physical MR desktop data. Magn Reson Imaging 1994;3:19–24.
6 H. Gudbjartsson and S. Patz. The Rician distribution of noisy MRI data. Magn Reson Med 1995;34:6:910–914.
7 A. Macovski. Noise in MRI. Magn Reson Med 1996;36:3:494–497.
8 W. A. Edelstein, G. H. Glover, C. J. Hardy, and R. W. Redington. The intrinsic SNR in NMR imaging. Magn Reson Med 1986;3:4:604–618.
9 G.A. Wright. Magnetic Resonance Imaging. IEEE Signal Processing Magazine 1997;1:56-66.
10 X. Tai, K. Lie, T. Chan, and S. Osher, Eds. Image Processing based on Partial Differential Equations 2005; New York: Springer.
11 G. Gerig, O. Kubler, R. Kikinis, and F. A. Jolesz. Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imag 1992;11:2:221–232.
12 M. Lysaker, A. Lundervold, and X. Tai. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans Image Process 2003;12:12:1579–1590.
13 A. Fan, W. Wells, J. Fisher, M. Ηetin, S. Haker, R. Mulkern, C. Tempany, and A.Willsky. A unified variational approach to denoising and bias correction in MR. Inf Proc Med Imag 2003;148–159.
14 S. Basu, P. T. Fletcher, and R. T. Whitaker. Rician noise removal in diffusion tensor MRI. Med Imag Comput Comput Assist Intervention 2006;117–125.
15 S. P. Awate and R. T. Whitaker. Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering. Proc IEEE Int Conf. Comput Vision Pattern Recognition 2005;2:44–51.
16 S. P. Awate and R. T. Whitaker. Unsupervised, information-theoretic, adaptive image filtering for image restoration. IEEE Trans Pattern Anal Mach Intell 2006;28:3:364–376.
17 K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 1975;21:1:32–40.
18 D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 2002;24:5:603–619.
19 A. Buades, B. Coll, and J. M. Morel. A non-local algorithm for image denoising. IEEE Int Conf Comp Vis Pattern Recog 2005;2:60–65.
20 A. Buades, B. Coll, and J. M. Morel. A review of image denoising algorithms, with a new one. Multiscale Modeling Simulation 2005;4:2:490–530.
21 . J. B. Weaver, Y. Xu, D. M. Healy Jr., and L. D. Cromwell. Filtering noise from images with wavelet transforms. Magn Reson Med 1991;21:2:288–295.
22 R. D. Nowak. Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans Image Process 1999;8:10:1408–1419.
23 A. M. Wink and J. B. T. M. Roerdink. Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing. IEEE Trans Image Process 2004;23:3:374–387.
24 A. Pizurica, A. M. Wink, E. Vansteenkiste, W. Philips, and J. B. T. M. Roerdink. A review of wavelet denoising in MRI and ultrasound brain imaging. Current Med Imag Rev 2006;2:2:247–260.
25 D. Tisdall and M. S. Atkins. MRI denoising via phase error estimation. Proc SPIE Med Imag 2005;646–654.
26 Gerlind Plonka and Jianwei Ma. Nonlinear Regularised Reaction-Diffusion Filter for Denoising of Images with Textures. IEEE Trans. Image Processing 2008;17:8:1283–1294.
27 S. G. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Anal Machine Intell 1989;l:11:674–693.
28 E. J. Candθs and D. L. Donoho. Curvelets. [Online] Available: http://www-stat.stanford.edu/~donoho/Reports/1999/Curvelets.pdf.
29 M. N. Do and M. Vetterli. Framing pyramids. IEEE Trans Signal Proc 2003;2329–2342.
30 E. J. Candes and D. L. Donoho. Recovering edges in ill-posed inverse problems: Optimality of Curvelet frames. Ann. Statist. 30 (2002); 784 –842.
31 L. Demanet and L. Ying. Wave atoms and sparsity of oscillatory patterns. Appl Comput Harmon Anal 2007;23:3:368–387.
32 G. Cottet and L. Germain. Image processing through reaction combined with nonlinear diffusion. Math Comput 1993;61:659–673.
33 [Online]. Available: http://www.bic.mni.mcgill.ca/brainweb/
34 A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy. A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imag 2003;22:3:323–331.
35 Suyash P. Awate and Ross T. Whitaker. Feature-Preserving MRI Denoising: A Nonparametric Empirical Bayes Approach. IEEE Trans Med Imag 2007;26:9:1242–1255.
Mr. J.Rajeesh
- India
rajeesh_j@yahoo.co.in
Mr. R.S.Moni
- India
Associate Professor S.Palanikumar
- India
Mr. T.Gopalakrishnan
- India