Thijssen J M, Oosterveld B J, Wagner R F
Institute of Ophthalmology, University of Nijmegen, The Netherlands.
Ultrason Imaging. 1988 Jul;10(3):171-95. doi: 10.1177/016173468801000302.
In search of the optimal display of echographic information for the detection of focal lesions, a systematic study was performed considering a wide range of gray level transforms (i.e., lookup tables). This range comprised power functions of the echo envelope signal (1/8 less than or equal to n less than or equal to 8), power functions of the logarithmic transform and a sigmoid function. The implications of the transforms on the first order statistics (histogram, "point signal-to-noise ratio" SNRp) and on the second order statistics (autocorrelation function) could be derived both analytically, and from the analysis of simulated and experimentally obtained echograms of homogeneously scattering tissue models. These results were employed to estimate the lesion signal-to-noise ratio SNRl, which specifies the detectability of a lesion by an ideal observer. It was found, both theoretically and practically, that the intensity display corresponds to the optimal transform (i.e., n = 2) for a low contrast lesion. When the data were first logarithmically compressed, the lesion SNR appeared to increase with increasing power (1/8 less than or equal to n less than or equal to 8). A logarithmic transform followed by a sigmoid compression did not produce much improvement. These effects of gray level transforms on the SNRl were shown to be relatively small, with the exception of powers n greater than 2 when applied to linear (i.e. amplitude) data. In the case of high lesion contrast, the sequence of log compression, followed by a square law produced the optimum SNRl. This sequence is equivalent to the processing within echographic equipment, where the TV monitor has a gamma of the order of 2.
为了寻找用于检测局灶性病变的超声信息的最佳显示方式，我们进行了一项系统研究，考虑了广泛的灰度变换（即查找表）。这个范围包括回波包络信号的幂函数（1/8≤n≤8）、对数变换的幂函数和一个Sigmoid函数。这些变换对一阶统计量（直方图、“点信噪比”SNRp）和二阶统计量（自相关函数）的影响既可以通过解析推导得出，也可以通过对均匀散射组织模型的模拟和实验获得的超声图像进行分析得出。这些结果被用于估计病变信噪比SNRl，它指定了理想观察者检测病变的能力。从理论和实践上都发现，对于低对比度病变，强度显示对应于最佳变换（即n = 2）。当数据首先进行对数压缩时，病变SNR似乎随着幂次增加（1/8≤n≤8）而增加。对数变换后接着进行Sigmoid压缩并没有带来太大改善。灰度变换对SNRl的这些影响被证明相对较小，当应用于线性（即幅度）数据时，幂次n大于2的情况除外。在高病变对比度的情况下，先进行对数压缩，然后进行平方律处理产生了最佳的SNRl。这个序列等同于超声设备中的处理过程，其中电视监视器的伽马值约为2。
