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Grayscale inhomogeneities in magnetic resonance (MR) images confound quantitative analysis of these images. Homomorphic unsharp masking and its variations have been commonly used as a post-processing method to remove inhomogeneities in MR images, However, little data is available in the literature assessing the relative effectiveness of these algorithms to remove inhomogeneities, or describing how these algorithms can affect image data. In this study, the authors address these questions quantitatively using simulated images with artificially constructed and empirically measured bias fields. The authors' results show that mean-based filtering is consistently more effective than median-based algorithms for removing inhomogeneities in MR images, and that artifacts are frequently introduced into images at the most commonly used window sizes. The authors' results demonstrate dramatic improvement in the effectiveness of the algorithms with significantly larger windows than are commonly used.