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Noise Reduction in Magnetic Resonance Images using Wave Atom Shrinkage
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 International Journal of Image Processing (IJIP)
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Volume:  4    Issue:  2
Pages:  89-191
Publication Date:   May 2010
ISSN (Online): 1985-2304
Pages 
 131 - 141
Author(s)  
J.Rajeesh - India
R.S.Moni - India
 
Published Date   
 10-06-2010 
Publisher 
 CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   De-noising, Gaussian noise, Magnetic Resonance Images, Rician noise, Wave Atom Shrinkage 
 
 
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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. 
 
 
 
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.
 
 
 
 
 
 
 
 
J.Rajeesh : Colleagues
R.S.Moni : Colleagues
S.Palanikumar : Colleagues
T.Gopalakrishnan : Colleagues  
 
 
 
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