Abstract
AbstractMagnetic resonance images are often influenced by noise. Reliable estimation of noise characteristics is important for image post‐processing. The benchmark algorithms often assume a uniform noise model across the image; that is, the noise features are spatially invariant. A few noise estimators are found to cope with nonhomogeneous noise; however, they either require multiple acquisitions or other additional information. In this research work, we develop a method that can accurately estimate the nonhomogeneous parameters of noise from just a single magnitude image. The proposed algorithm is a two‐step approach. In the first step, we evaluate the standard deviation of noise assuming that it follows Gaussian distribution. This step utilizes a hybrid of spatial domain edge information suppression and transforms domain coefficient statistics to estimate a single value of standard deviation of noise in magnitude MR images. In the second step, the evaluated noise is corrected using the analytical expression that establishes the relationship between Gaussian noise and Rician noise. Results on synthetic and clinical data evidence the better performance of the proposed algorithm when compared to the benchmark methods.
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