Sijbers Jan, Poot Dirk, den Dekker Arnold J, Pintjens Wouter
Vision Lab, Department of Physics, University of Antwerp Universiteitsplein 1, B-2610 Wilrijk, Belgium.
Phys Med Biol. 2007 Mar 7;52(5):1335-48. doi: 10.1088/0031-9155/52/5/009. Epub 2007 Feb 8.
Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of which however require user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean squared error (RMSE). The results show that the newly proposed method is superior in terms of the RMSE.
估计磁共振（MR）图像的噪声方差对于各种后处理任务很重要。在文献中，有多种从MR图像估计噪声方差的方法，然而其中大多数需要用户交互和/或多张（完美对齐的）图像。在本文中，我们专注于基于直方图的自动噪声方差估计技术。回顾了先前描述的方法，并提出了一种基于最大似然（ML）原理的新方法。使用蒙特卡罗模拟实验以及实验性MR数据集，根据均方根误差（RMSE）对噪声方差估计方法进行了比较。结果表明，新提出的方法在RMSE方面更具优势。
