A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation
Journal
IEEE Transactions on Image Processing
Journal Volume
21
Journal Issue
8
Date Issued
2012-07-27
Author(s)
Abstract
In this paper, we derive an unbiased expression for the expected mean-squared error associated with continuously differentiable estimators of the noncentrality parameter of a chi-square random variable. We then consider the task of denoising squared-magnitude magnetic resonance (MR) image data, which are well modeled as independent noncentral chi-square random variables on two degrees of freedom. We consider two broad classes of linearly parameterized shrinkage estimators that can be optimized using our risk estimate, one in the general context of undecimated filterbank transforms, and the other in the specific case of the unnormalized Haar wavelet transform. The resultant algorithms are computationally tractable and improve upon most state-of-the-art methods for both simulated and actual MR image data. © 1992-2012 IEEE.
Subjects
Chi-square distribution | filterbank transform | image denoising | magnetic resonance (MR) imaging | Rician noise | unbiased risk estimation; Statistics - Applications; Statistics - Applications; Statistics - Methodology
Description
30 double-spaced pages, 11 figures; submitted for publication
Type
journal article