New Theoretical Results on Channelized Hotelling Observer Performance Estimation with Known Difference of Class Means.
New Theoretical Results on Channelized Hotelling Observer Performance Estimation with Known Difference of Class Means.
复制标题
已知类别均值差异的通道化 Hotelling 观察者性能估计的新理论结果。
DOI：
10.1109/tns.2012.2227340
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发表时间：
2013
影响因子：
1.8
通讯作者：
Noo,Frédéric
中科院分区：
文献类型：
--
作者：
Wunderlich,Adam;Noo,Frédéric
基于任务的图像质量评估是评价成像系统性能的一种严格、原则性的方法。为了进行这样的评估，已经认识到数学模型观测器是非常有用的，特别是对于成像系统开发和优化的目的。在医学成像领域中广泛应用的一种类型的模型观测器是信道化霍特林观测器（channelized Hotelling observer，CHO）。由于CHO性能的估计值通常包括统计变异性，因此控制和限制这种变异性以最大化图像质量研究的统计功效非常重要。在以前的论文中，我们证明，通过包括先验知识的图像类的手段，可以实现CHO性能估计的偏差和方差的大幅下降。本工作的目的是提出改进和扩展的估计理论，在我们以前的文件，这是有限的点估计与相等数量的图像从每个类。具体来说，我们提出和表征最小方差无偏点估计的观察者信噪比（SNR），允许不平等的病变缺席和病变存在的图像数量。基于此SNR点估计理论，我们证明了可以为常用的CHO性能指标构建具有确切已知覆盖概率的置信区间。此外，我们提出了简单的，近似的置信区间CHO性能，我们表明，他们在大多数情况下表现良好的利益。
Task-based assessments of image quality constitute a rigorous, principled approach to the evaluation of imaging system performance. To conduct such assessments, it has been recognized that mathematical model observers are very useful, particularly for purposes of imaging system development and optimization. One type of model observer that has been widely applied in the medical imaging community is the channelized Hotelling observer (CHO). Since estimates of CHO performance typically include statistical variability, it is important to control and limit this variability to maximize the statistical power of image-quality studies. In a previous paper, we demonstrated that by including prior knowledge of the image class means, a large decrease in the bias and variance of CHO performance estimates can be realized. The purpose of the present work is to present refinements and extensions of the estimation theory given in our previous paper, which was limited to point estimation with equal numbers of images from each class. Specifically, we present and characterize minimum-variance unbiased point estimators for observer signal-to-noise ratio (SNR) that allow for unequal numbers of lesion-absent and lesion-present images. Building on this SNR point estimation theory, we then show that confidence intervals with exactly-known coverage probabilities can be constructed for commonly-used CHO performance measures. Moreover, we propose simple, approximate confidence intervals for CHO performance, and we show that they are well-behaved in most scenarios of interest.