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