Estimation of the Noise in Magnitude MR Images (original) (raw)
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Noise measurement from magnitude MRI using local estimates of variance and skewness
Physics in Medicine and Biology, 2010
In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without 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. Reson. Med. 51 586-94 Sijbers J et al 1998 Maximum likelihood estimation of Rician distribution parameters IEEE Trans. Med. Imag. 17 357-61 Sijbers J et al 2007 Automatic estimation of the noise variance from the histogram of a magnetic resonance image Phys. Med. Biol. 52 1335-48 Zhang Y et al 2001 Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm IEEE Trans. Med. Imaging 20 45-57
The rician distribution of noisy mri data
Magnetic Resonance in Medicine, 1995
The image intensity in magnetic resonance magnitude images in the presence of noise is shown to be governed by a Rician distribution. Low signal intensities (SNR < 2) are therefore biased due to the noise. It is shown how the underlying noise can be estimated from the images and a simple correction scheme is provided to reduce the bias. The noise characteristics in phase images are also studied and shown to be very different from those of the magnitude images. Common to both, however, is that the noise distributions are nearly Gaussian for SNR larger than two.
Robust Rician noise estimation for MR images
Medical Image Analysis, 2010
In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.
Estimating the Rician noise level in brain MR image
2014 IEEE ANDESCON, 2014
Estimation of the noise level in images is very important to assess the quality of the acquisition and to allow an efficient analysis. Moreover, it is a fundamental step, an indispensable procedure for many type of denoises and image processing. In this paper a new method to estimate the noise level in MR images is presented and evaluated. The advantage of this is the easiness for utilization during image acquisition and of course the adaptability of the idea of other areas of body. The correctness of the evaluation is addressed by comparison of Atlas noise free images where the level of Rician noise was artificially added and known. The main idea is the matching of same slices after registration in order to evaluate the level of noise. For evaluation of the range of noise in an image we used the signal noise ratio-SNR and a set of MRI with increasing levels of Rician noise. However, others metrics like the normalized cross correlation-NCC or the Root Mean Squared Error (RMSE) could be used as well. Keyworks-MRI brain atlas; Rician noise; image registration; signal noise ratio, interpolation.
An object-based method for Rician noise estimation in MR images
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
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. Most of the existing Rician noise estimation methods are based on background statistics, and as such are sensitive to ghosting artifacts. In this paper, a new object-based method is proposed. This method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The adaptation for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. A quantitative validation on synthetic phantom with artefacts is presented and a new validation framework is proposed to perform quantitative validation on real data. The results show the accuracy and the robustness of the proposed method.
Robust estimation of the noise variance from background MR data
2006
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. Both methods are evaluated in terms of accuracy and precision using simulated MR data. It is shown that the newly proposed method outperforms the commonly used method in terms of mean-squared error (MSE).
Automatic estimation of the noise variance from the histogram of a magnetic resonance image
Physics in Medicine and Biology, 2007
Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of them however requiring user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. 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). The results show that the newly proposed method is superior in terms of the RMSE. † Jan Sijbers is a Postdoctoral
Medical Imaging 2005: Image Processing, 2005
Signal intensity in magnetic resonance images (MRIs) is affected by random noise. Assessing noise-induced signal variance is important for controlling image quality. 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 times or diffusion tensor elements. Signal variance can be estimated from measurements of the noise variance in an object-and ghost-free region of the image background. However, identifying a large homogeneous region automatically is problematic. In this paper, a novel, fully automated approach for estimating the noise-induced signal variance in magnitude-reconstructed MRIs is proposed. This approach is based on the histogram analysis of the image signal intensity, explicitly by extracting the peak of the underlining Rayleigh distribution that would characterize the distribution of the background noise. The peak is extracted using a nonparametric univariate density estimation like the Parzen window density estimation; the corresponding peak position is shown here to be the expected signal variance in the object. The proposed method does not depend on prior foreground segmentation, and only one image with a small amount of background is required when the signal-to-noise ratio (SNR) is greater than three. This method is applicable to magnitude-reconstructed MRIs, though diffusion tensor (DT)-MRI is used here to demonstrate the approach.
Robust estimation of the noise variance from background MR data [6144-232]
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. Both methods are evaluated in terms of accuracy and precision using simulated MR data. It is shown that the newly proposed method outperforms the commonly used method in terms of mean-squared error (MSE).
Parameter estimation from magnitude MR images
International Journal of Imaging Systems and Technology, 1999
This article deals with the estimation of model-based parameters, such as the noise variance and signal components, from magnitude magnetic resonance (MR) images. Special attention has been paid to the estimation of T 1 -and T 2 -relaxation parameters. It is shown that most of the conventional estimation methods, when applied to magnitude MR images, yield biased results. Also, it is shown how the knowledge of the proper probability density function of magnitude MR data (i.e., the Rice distribution) can be exploited so as to avoid (or at least reduce) such systematic errors. The proposed method is based on maximum likelihood (ML) estimation.