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Robust estimation of the noise variance from background MR data

J. Sijbers, A. J. den Dekker, D. Poot, R. Bos, M. Verhoye, N. Van Camp, A. Van der Linden, Joseph M. Reinhardt, Josien P. W. Pluim
2006 Medical Imaging 2006: Image Processing  
In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images.  ...  A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum likelihood (ML) estimation.  ...  Noise estimation In many MR applications, a large number of background data (where the true MR signal is assumed to be zero) is available to estimate the noise variance.  ... 
doi:10.1117/12.652616 dblp:conf/miip/SijbersDPBVCL06 fatcat:4tnboezg6bbc5jk4f334rl7rhi

An Object-Based Method for Rician Noise Estimation in MR Images [chapter]

Pierrick Coupé, José V. Manjón, Elias Gedamu, Douglas Arnold, Montserrat Robles, D. Louis Collins
2009 Lecture Notes in Computer Science  
The estimation of the noise level in MR images is used to assess the consistency of statistical analysis or as an input parameter in some image processing techniques.  ...  The results show the accuracy and the robustness of the proposed method.  ...  Aja for providing the source code of their respective Rician noise estimation methods and for their useful comments.  ... 
doi:10.1007/978-3-642-04271-3_73 fatcat:4pzgv5avhfb23eowjdchbirns4

Robust Rician noise estimation for MR images

Pierrick Coupé, José V. Manjón, Elias Gedamu, Douglas Arnold, Montserrat Robles, D. Louis Collins
2010 Medical Image Analysis  
The main advantage of this object-based method is its robustness to background artefacts such as ghosting.  ...  The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise.  ...  Aja for providing the source code of their respective Rician noise estimation methods and for their useful comments. We thank the reviewers for their valuable comments which have improved this paper.  ... 
doi:10.1016/ pmid:20417148 fatcat:bcwbnefkb5dwhpquixfhkmvpqa

Segmentation Based Noise Variance Estimation from Background MRI Data [chapter]

Jeny Rajan, Dirk Poot, Jaber Juntu, Jan Sijbers
2010 Lecture Notes in Computer Science  
In MR images, background data is well suited for noise estimation since (theoretically) it lacks contributions from object signal.  ...  However, background data not only suffers from small contributions of object signal but also from quantization of the intensity values.  ...  This work was financially supported by the IWT (Institute for Science and Technology, Belgium; SBO QUANTIVIAM) and the UIAP.  ... 
doi:10.1007/978-3-642-13772-3_7 fatcat:56pilxeefrgmtfqxkzh6ntrdmi

Automatic estimation of the noise variance from the histogram of a magnetic resonance image

Jan Sijbers, Dirk Poot, Arnold J den Dekker, Wouter Pintjens
2007 Physics in Medicine and Biology  
Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks.  ...  Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean-squared error (RMSE).  ...  Acknowledgements The authors thank Robert Bos from Delft University of Technology (The Netherlands) for useful discussions. Automatic estimation of the noise variance from the histogram of an MR image  ... 
doi:10.1088/0031-9155/52/5/009 pmid:17301458 fatcat:ezbgrtt42bdmhl5elakqiqwfbm

An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images

Lin-Ching Chang, Gustavo K. Rohde, Carlo Pierpaoli, J. Michael Fitzpatrick, Joseph M. Reinhardt
2005 Medical Imaging 2005: Image Processing  
Signal variance can be estimated from measurements of the noise variance in an object-and ghost-free region of the image background.  ...  Knowledge of signal variance is required for correctly computing the chi-square value, a measure of goodness of fit, when fitting signal data to estimate quantitative parameters such as T1 and T2 relaxation  ...  Liz Salak for her advice and assistance in the editing of this manuscript.  ... 
doi:10.1117/12.596008 dblp:conf/miip/ChangRP05 fatcat:weiil6yq7rc3tlcq46opzn3iwe

Noise measurement from magnitude MRI using local estimates of variance and skewness

Jeny Rajan, Dirk Poot, Jaber Juntu, Jan Sijbers
2010 Physics in Medicine and Biology  
In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data.  ...  (Some figures in this article are in colour only in the electronic version) Sijbers J and Dekker A J den 2004 Maximum likelihood estimation of signal amplitude and noise variance from MR data Magn.  ...  Acknowledgments This work was financially supported by the Inter-University Attraction Poles Program 6-38 of the Belgian Science Policy, and by the SBO-project QUANTIVIAM (060819) of the Institute for  ... 
doi:10.1088/0031-9155/55/16/n02 pmid:20679694 fatcat:ujk3hy3tkjc7zbtuwtafarjjdy

Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach

Tomasz Pieciak, Santiago Aja-Fernandez, Gonzalo Vegas-Sanchez-Ferrero
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
models of the data.  ...  Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.  ...  ACKNOWLEDGMENTS The authors thank M. Maggioni  ... 
doi:10.1109/tpami.2016.2625789 pmid:27845653 fatcat:qsplrscowzav5jan44key3ggdq

A Method for Modeling Noise in Medical Images

P. Gravel, G. Beaudoin, J.A. DeGuise
2004 IEEE Transactions on Medical Imaging  
Various types of noise (e.g., photon, electronics, and quantization) often contribute to degrade medical images; the overall noise is generally assumed to be additive with a zero-mean, constant-variance  ...  We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations.  ...  ACKNOWLEDGMENT The authors would like to thank A. Bleau and S. Rajagopalan for their critical reading of the manuscript and to P. Després, F. Dinelle, and A.  ... 
doi:10.1109/tmi.2004.832656 pmid:15493690 fatcat:v42mreo6hvamrhdvmfr57y3avu

Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels

Alexei A. Samsonov, Chris R. Johnson
2004 Magnetic Resonance in Medicine  
However, the anisotropic filter is nonoptimal for MR images with spatially varying noise levels, such as images reconstructed from sensitivity-encoded data and intensity inhomogeneity-corrected images.  ...  The new filter was shown to improve segmentation of MR brain images with spatially varying noise levels. Magn Reson Med 52:798 -806, 2004.  ...  The determination of the image noise level from background area samples is not always robust, because of artifacts (motion-related ghosting, etc) or limited size of the areas.  ... 
doi:10.1002/mrm.20207 pmid:15389962 fatcat:jjg2waj2rzfn5dzz7xwl4itkjq

Robust estimation of spatially variable noise fields

Bennett A. Landman, Pierre-Louis Bazin, Seth A. Smith, Jerry L. Prince
2009 Magnetic Resonance in Medicine  
These heuristic analyses enable robust noise field estimation in the presence of artifacts.  ...  Generalization of noise estimators based on uniform regions, difference images, and maximum likelihood are presented and compared with the estimators derived from the proposed framework.  ...  The use of a data-adaptive threshold achieves minimal loss of power for voxel-wise estimation of noise variance, while remaining robust to observations from non-Gaussian distributions and/or artifacts  ... 
doi:10.1002/mrm.22013 pmid:19526510 pmcid:PMC2806192 fatcat:7a7nfya3rjaptl2fnydi2wch4u

Quantitative Evaluation of Statistical Inference in Resting State Functional MRI [chapter]

Xue Yang, Hakmook Kang, Allen Newton, Bennett A. Landman
2012 Lecture Notes in Computer Science  
Herein, we leverage the theoretical core of SIMEX to study the properties of inference methods in the face of diminishing data (in contrast to increasing noise).  ...  The stability of inference methods with respect to synthetic loss of empirical data (defined as resilience) is used to quantify the empirical performance of one inference method relative to another.  ...  The boxplots in the white background display the results from OLS and the boxplots in the gray background are the results from Huber M-estimator.  ... 
doi:10.1007/978-3-642-33418-4_31 fatcat:twfqp4uxr5fcfmzku24stpkwem

Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach

S. Aja-Fernandez, C. Alberola-Lopez, C.-F. Westin
2008 IEEE Transactions on Image Processing  
These methods use information of the sample distribution of local statistics of the image, such as the local variance, the local mean, and the local mean square value.  ...  Additionally, a set of methods that automatically estimate the noise power are developed.  ...  ACKNOWLEDGMENT The authors would like to thank Dr. R. San José and Dr. M. Niethammer for valuable comments.  ... 
doi:10.1109/tip.2008.925382 pmid:18632347 fatcat:xtzydgo6c5do7gwvjgmpmcqape

Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach [article]

Marco Barbieri, Leonardo Brizi, Enrico Giampieri, Francesco Solera, Gastone Castellani, Claudia Testa, Daniel Remondini
2018 arXiv   pre-print
method suffers from the coarse resolution of the MR parameter space sampling.  ...  robustness to noise.  ...  Feeding the network models with noisy data with different noise variances have yielded the best results in terms of noise robustness providing a MAPE lower than 15% (evaluated on a wide range of parameter  ... 
arXiv:1811.11477v1 fatcat:xfcrbfxh35d2bpus5rl6lcnf4u

Automated model-based bias field correction of MR images of the brain

K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens
1999 IEEE Transactions on Medical Imaging  
The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue  ...  We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities.  ...  ACKNOWLEDGMENT The authors wish to acknowledge the contribution of J. Michiels and H. Bosmans.  ... 
doi:10.1109/42.811268 pmid:10628948 fatcat:kvjgibq2mfa4xcmm5nf66diyfu
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