A Robust and Hybrid Rician Noise Estimation Scheme for Magnetic Resonance Images


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Rician noise estimation is important in magnetic resonance (MR) images as the higher accuracy of estimated noise pixels can improve the features of a filtered MR image. The accuracy and robustness of the estimation algorithm can be improved with the existence of MR imaging artifacts. The well known estimation methods like background based methods and maximum likelihood based methods fail to calculate the noise levels properly in the presence of imaging artifacts in the scanned MR images. We propose an object based estimation algorithm which works in the presence or absence of image background and also is expected be robust to estimate various levels of noise even in the presence of image artifacts like “ghost effect”. The conventional Mean Absolute Deviation (MAD) based estimation is replaced by the Scaled Estimate(S-estimate) in this research work. This has resulted in estimation of noise both in low and high range levels uniformly. The proposed adaptive S-estimate for various window sizes makes the estimation process more accurate. The iterative process ensures the estimation of noise distribution in such a way that it coincides with the introduced noise distribution. In this paper we have adapted a pixel wise estimation process to make S-estimate faster in terms of run time profiles, called the pixel-wise-S-estimate (PWS). The simulation results show that noise estimation is improved by a factor of 4.54% and the computational complexity of the S-estimate based Rician noise removal algorithm is reduced by 82%. This technique also better suits for estimating Rician noise variance
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Rician Noise; S-estimate; Magnetic Resonance Image (MRI); MAD; Noise Detector

Full Text:

PDF


References


Sijbers. J, Poot. D, Den Dekker. A.J., Pintjens W., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, Physics in Medicine and Biology, 52 (5), 1335–1348. 2007

Aja-Fernandez. S, Niethammer. M, Kubicki. M, Shenton, M.E , Westin, C.F., Restoration of DWI data using a Rician LMMSE estimator, IEEE Transactions on Medical Imaging 27 (10), 1389–1403, 2008

Aja-Fernandez. S, Tristán-Vega. A, Alberola-López. C, Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models, Magnetic Resonance Imaging, 27 (10), 1397–1409, 2009.

Lili He, Greenshields Ian. R, "A Nonlocal Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images", IEEE Transactions on Medical Imaging, vol.28, no.2, pp.165, 172, Feb. 2009.

Coupe. P, Yger. P, Prima. S, Hellier. P, Kervrann. C, Barillot. C, An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Image, IEEE Transactions on Medical Imaging 27(4), 425–441 (2008)

Pierrick Coupe, Jose V. Manjón, Elias Gedamu, Douglas Arnold, Montserrat Robles, D. Louis Collins, An Object-Based Method for Rician Noise Estimation in MR Images, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science Volume 5762, 2009, pp 601-608

Pierrick Coupe, Jose V. Manjon, Elias Gedamu, Douglas Arnold, Montserrat Robles, D. Louis Collins, Robust Rician noise estimation for MR images, Medical Image Analysis, Volume 14, Issue 4, August 2010, Pages 483-493

Maitra. R, Faden. D, Noise Estimation in Magnitude MR Datasets, IEEE Transactions on Medical Imaging, vol.28, no.10, pp.1615,1622, Oct. 2009

Vladimir Crnojevic and Nemanja I. Petrovic, Impulse noise filtering using robust pixel-wise S-estimate of variance. EURASIP J. Adv. Signal Process 2010, Article 8 (February 2010)

Jeny Rajan, Johan Van Audekerke, Annemie Van der Linden, Marleen Verhoye, , and Jan Sijbers, An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images, in Inter-national Symbosium on Biomedical Imaging, 2012.

Ong S. H.; Foong. K. W. C; Goh, P. S.; Nowinski, W.L., Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm, IEEE Southwest Symposium on Image Analysis and Interpretation, 2006, vol., no., pp.61,65

H.P. Ng, S.H. Ong, K.W.C. Foong, W.L. Nowinski, An improved watershed algorithm for medical image segmentation, Proceedings12th International Conference on Biomedical Engineering, 2005

Cheng Guan Koay, Peter J. Basser, Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, Journal of Magnetic Resonance, Volume 179, Issue 2, April 2006, Pages 317-322

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, Medical Physics, 16(5):813-817, 1989.

Edelstein, W. A., Bottomley, Paul A., Pfeifer, Leah M., A signal-to-noise calibration procedure for NMR imaging systems in Medical Physics, Volume 11, Issue 2, March 1984, pp.180-185.

G. Koay and P J Basser, Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, Journal of Magnetic Resonance, vol. 179, pp. 317–322, 2006.

S. Aja-Fernandez and A. Tristán-Vega, Influence of noise correlation in multiple-coil statistical models with sum of squares reconstruction, Magnetic Resonance in Medicine, vol. 67, pp. 580–585, 2011.

P. Coupe, P. Hellier, S. Prima, C. Kervrann, and C. Barillot, 3D wavelet subbands mixing for image denoising, Journal of Biomedical Imaging, 2008(3):1-11, 2008.

D.L. Donoho. De-noising by Soft-Thresholding. IEEE Transactions on Information Theory, 41(3):613{627, 1995.}

MATLABR2012atoolboxmatlabdemosmri.mat

https://central.xnat.org/app/action/DisplayItemAction/search_value/CENTRAL_OASIS_LONG/search_element/xnat:projectData/search_field/xnat:projectData.ID

Thirumarai Selvi, C., Sudhakar, R., An efficient 2DWT-A architecture using distributive arithmetic algorithm, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1878-1888.

Selvaraj, D., Dhanasekaran, R., Segmentation of cerebrospinal fluid and internal brain nuclei in brain magnetic resonance images, (2013) International Review on Computers and Software (IRECOS), 8 (5), pp. 1063-1071.

Zhao, Y., Fu, Y., Zhang, J., PCNN-based label fusion for multi-atlas segmentation, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3811-3815.

Vasanthi Kumari, P., Thanushkodi, K., A secure fast 2D-discrete fractional Fourier transform based medical image compression using SPIHT algorithm with Huffman encoder, (2013) International Review on Computers and Software (IRECOS), 8 (7), pp. 1702-1710.

Thamarai Selvi, G., Duraiswamy, K., A technique to tumor detection from brain MRI images using FCM and neuro-fuzzy classifier, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1931-1942.

Selva Bhuvaneswari, K., Geetha, P., Tumor, edema and atrophy segmentation of brain MRI with wavelet transform and semantic features, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1243-1254.

Kavitha, A.R., Chellamuthu, C., Brain tumor segmentation in MRI images based on image registration and improved fuzzy C-Means (IFCM) method, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1950-1954.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize