Brinkmann B H, Manduca A, Robb R A
Biomedical Imaging Resource, Mayo Foundation, Rochester, MN 55905, USA.
IEEE Trans Med Imaging. 1998 Apr;17(2):161-71. doi: 10.1109/42.700729.
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, we address these questions quantitatively using simulated images with artificially constructed and empirically measured bias fields. Our 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. Our results demonstrate dramatic improvement in the effectiveness of the algorithms with significantly larger windows than are commonly used.
磁共振（MR）图像中的灰度不均匀性会干扰这些图像的定量分析。同态锐化掩蔽及其变体通常被用作一种后处理方法，以去除MR图像中的不均匀性。然而，文献中几乎没有数据评估这些算法去除不均匀性的相对有效性，或者描述这些算法如何影响图像数据。在本研究中，我们使用具有人工构建和经验测量的偏置场的模拟图像定量地解决这些问题。我们的结果表明，基于均值的滤波在去除MR图像中的不均匀性方面始终比基于中值的算法更有效，并且在最常用的窗口大小下，伪影经常被引入图像中。我们的结果表明，与常用窗口相比，使用显著更大的窗口时，算法的有效性有了显著提高。
