Noise Estimation Using Back Propagation Neural Networks
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Published
Sep 16, 2021
Devinder Singh
Abstract
In this paper, a new Backpropagation Neural Network-based noise estimation method is proposed to estimate Rician noise from MRI images. To Trained BNN features of the MRI image such as contrast, homogeneity, dissimilarity, asm, energy, entropy, meanx, meany, meanglcm, varx, vary, varglcm, correlation, skewx, skewy, skew, kurtosisx, kurtosisy, kurtosis etc are used. For training to BNN four hundred fifty images are used which are downloaded from Brain web.
How to Cite
Singh, D. . (2021). Noise Estimation Using Back Propagation Neural Networks. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/823
Article Details
Keywords
Noise Estimation, Backpropagation Neural Network, Magnetic Resonance imaging.
References
[1] S.Sridhar, Digital Image Processing, 2nd Ed., Oxford, 216-235.
[2] R.C. Gonzalez, and R.E. Woods, Digital Image Processing, 2nd Ed., Pearson Education, 244-53.
[3] J. Sijbers, A. J. D Dekker., D. V. dyck and E. Raman, Estimation of signal and noise from Rician distributed data, Proceedings of the International Conference on Signal Processing and Communications, 140-142, 1998.
[4] M. Nezamzadeh, I. Cameron, A New Rician Noise Bias Correction, Proc. Intl. Soc. Mag. Reson. Med. (14) ,346, 2006.
[5] J. Sijbers, D. Poot, A. J. D Dekker, and W. Pintjens., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, Physics in Medicine & Biology, 52(5), 2007.
[6] S. Aja-Fernández, C. Alberola-López, and C. Westin, Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach, IEEE TRANSACTIONS ON IMAGE PROCESSING, 17(8), 1383-1389, 2008.
[7] S. Aja-Fernández, G. Vegas-Sánchez-Ferrero,M. Martín-Fernández, and C. Alberola-López, Automatic noise estimation in images using local statistics, Additive and multiplicative cases, Image and Vision Computing 27, 756–770, 2009.
[8] S. Yang, S. Tai, A design framework for hybrid approaches of image noise estimation and its application to noise reduction Journal of Visual Communication and Image Representation, 23(5), 812–826, 2012.
[9] P. Coupe, J.V. Manjón, E. Gedamu, D. Arnold, M. Robles, and D. L. Collins, Robust Rician noise estimation for MR images, Medical Image Analysis 14, 483–493, 2010.
[10] A. Foi, Noise Estimation and Removal in MR Imaging: The Variance-Stailization Approach, Proceeding of IEEE International Symposium on Biomedical Imaging, Chicago (IL), USA, 1809-1814, 2011
[11] P.S. Mukherjeea, and P. Qiu, Efficient bias correction for magnetic resonance image denoising, published online in Wiley Online Library, 2013.
[12] J. Rajan, A.J. DenDekker, and J. Sijbers, A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov–Smirnovtest, Signal Processing, 103, 16–23, 2014.
[2] R.C. Gonzalez, and R.E. Woods, Digital Image Processing, 2nd Ed., Pearson Education, 244-53.
[3] J. Sijbers, A. J. D Dekker., D. V. dyck and E. Raman, Estimation of signal and noise from Rician distributed data, Proceedings of the International Conference on Signal Processing and Communications, 140-142, 1998.
[4] M. Nezamzadeh, I. Cameron, A New Rician Noise Bias Correction, Proc. Intl. Soc. Mag. Reson. Med. (14) ,346, 2006.
[5] J. Sijbers, D. Poot, A. J. D Dekker, and W. Pintjens., Automatic estimation of the noise variance from the histogram of a magnetic resonance image, Physics in Medicine & Biology, 52(5), 2007.
[6] S. Aja-Fernández, C. Alberola-López, and C. Westin, Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach, IEEE TRANSACTIONS ON IMAGE PROCESSING, 17(8), 1383-1389, 2008.
[7] S. Aja-Fernández, G. Vegas-Sánchez-Ferrero,M. Martín-Fernández, and C. Alberola-López, Automatic noise estimation in images using local statistics, Additive and multiplicative cases, Image and Vision Computing 27, 756–770, 2009.
[8] S. Yang, S. Tai, A design framework for hybrid approaches of image noise estimation and its application to noise reduction Journal of Visual Communication and Image Representation, 23(5), 812–826, 2012.
[9] P. Coupe, J.V. Manjón, E. Gedamu, D. Arnold, M. Robles, and D. L. Collins, Robust Rician noise estimation for MR images, Medical Image Analysis 14, 483–493, 2010.
[10] A. Foi, Noise Estimation and Removal in MR Imaging: The Variance-Stailization Approach, Proceeding of IEEE International Symposium on Biomedical Imaging, Chicago (IL), USA, 1809-1814, 2011
[11] P.S. Mukherjeea, and P. Qiu, Efficient bias correction for magnetic resonance image denoising, published online in Wiley Online Library, 2013.
[12] J. Rajan, A.J. DenDekker, and J. Sijbers, A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov–Smirnovtest, Signal Processing, 103, 16–23, 2014.
Section
GE3- Computers & Information Technology