使用 FROC 曲线的指数变换进行非参数信号可探测性评估。

Nonparametric signal detectability evaluation using an exponential transformation of the FROC curve.

机构信息

Food and Drug Administration Center for Devices and Radiological Health, Silver Spring, MD 20993, USA.

出版信息

Med Phys. 2011 Oct;38(10):5690-702. doi: 10.1118/1.3633938.

DOI:10.1118/1.3633938

PMID:21992384

Abstract

PURPOSE

To develop an efficient nonparametric method for evaluation the detectability of signals at unknown locations in images, as a mean for image quality assessment.

METHODS

We use the free-response methodology that allows the image observer to mark and score all locations found as suspicious in an image, summarizing these results in a free-response operating characteristic (FROC) curve. However, unlike the relative (or receiver) operating characteristic (ROC), or the localization ROC (LROC), the FROC curve has an undefined, theoretically infinite, right side limit. Therefore area under the FROC curves cannot be directly used as an overall performance index, as the area under the curve is for ROC or LROC. We circumvent this drawback by using a transformation of the abscissa that leads to a finite integration range. By applying an exponential transformation we derive a nonparametric estimator for such a metric, and we study its properties by deriving analytical expressions for the mean and standard deviation in conditions of scores independence.

RESULTS

A comparative study with other related nonparametric estimators for ROC, LROC, and alternative FROC (AFROC) method is presented.

CONCLUSIONS

The new nonparametric estimator has sensitivity and scalability properties that make it particularly advantageous for signal detectability evaluation in phantom experiments using model observers.

摘要

目的

开发一种有效的非参数方法，用于评估图像中未知位置信号的可检测性，作为图像质量评估的一种手段。

方法

我们使用自由响应方法，允许图像观察者标记和评分图像中所有可疑的位置，通过自由响应操作特征（FROC）曲线总结这些结果。然而，与相对（或接收者）操作特征（ROC）或定位 ROC（LROC）不同，FROC 曲线的右侧极限是未定义的，理论上是无限的。因此，FROC 曲线下的面积不能像 ROC 或 LROC 那样直接用作整体性能指标。我们通过使用将横坐标转换为有限积分范围的方法来避免这个缺点。通过应用指数变换，我们为这种度量标准推导出了一个非参数估计器，并通过推导在评分独立条件下的均值和标准差的解析表达式来研究其性质。