莱斯分布参数的最大似然估计。
Maximum-likelihood estimation of Rician distribution parameters.
作者信息
Sijbers J, den Dekker A J, Scheunders P, Van Dyck D
机构信息
Department of Physics, University of Antwerp, Belgium.
出版信息
IEEE Trans Med Imaging. 1998 Jun;17(3):357-61. doi: 10.1109/42.712125.
The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.
本文探讨了从莱斯分布数据（如磁共振图像幅度）中进行参数估计的问题。讨论了传统估计方法的特性，并与已知能渐近产生最优结果的最大似然（ML）估计进行了比较。与先前提出的方法不同，ML估计在高信噪比（SNR）时被证明是无偏的，在低信噪比时能产生符合物理实际的结果。
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