On Efficient Assessment of Image-Quality Metrics Based on Linear Model Observers.
On Efficient Assessment of Image-Quality Metrics Based on Linear Model Observers.
复制标题
基于线性模型观测器的图像质量指标的有效评估。
DOI：
10.1109/tns.2012.2190096
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发表时间：
2012
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
本文的动机是使用模型观察器进行图像质量评估问题，以开发和优化医学成像系统。具体来说，我们提出了一项关于观察者的受试者工作特征（ROC）曲线估计和相关汇总测量的研究。本研究通过假设观察者评级的类别均值差异已知来评估观察者表现的 ROC 估计中可能获得的统计优势。这些知识在使用已知位置病变检测任务和线性模型观察者的图像质量研究中经常可用。该研究是通过引入参数点和置信区间估计器来进行的，其中包含已知的类均值差异。对 ROC 曲线下面积的新估计量的评估表明，通过结合类别均值差异的知识可以大幅减少统计变异性。也就是说，在某些情况下，平均 95% AUC 置信区间长度可能会小七倍。我们还研究了如何有利地使用类别均值差异的知识来比较两个相关 ROC 曲线下的面积，并观察类似的增益。
This paper is motivated by the problem of image-quality assessment using model observers for the purpose of development and optimization of medical imaging systems. Specifically, we present a study regarding the estimation of the receiver operating characteristic (ROC) curve for the observer and associated summary measures. This study evaluates the statistical advantage that may be gained in ROC estimates of observer performance by assuming that the difference of the class means for the observer ratings is known. Such knowledge is frequently available in image-quality studies employing known-location lesion detection tasks together with linear model observers. The study is carried out by introducing parametric point and confidence interval estimators that incorporate a known difference of class means. An evaluation of the new estimators for the area under the ROC curve establishes that a large reduction in statistical variability can be achieved through incorporation of knowledge of the difference of class means. Namely, the mean 95% AUC confidence interval length can be as much as seven times smaller in some cases. We also examine how knowledge of the difference of class means can be advantageously used to compare the areas under two correlated ROC curves, and observe similar gains.