Robust Rician Noise Estimation for MR Images
We have proposed a new object-based method to estimate noise in
magnitude MR images. 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.
Details can be found in:
Coupé P, Manjón J.V., Gedamu E., Arnold D., Robles M., Collins D.L. Robust Rician Noise Estimation for MR Images. Medical Image Analysis, 14(4), 483-493, 2010.
Demo images and the source code of the method can be found here:
Version 1.0. (March 2010)