Model for the detection of signals in images with multiple suspicious locations.
Model for the detection of signals in images with multiple suspicious locations.
复制标题
用于检测具有多个可疑位置的图像中的信号的模型。
DOI：
10.1118/1.3002413
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发表时间：
2008
期刊：
影响因子：
3.8
通讯作者：
Popescu,LucreţiuM
中科院分区：
文献类型：
--
作者：
Popescu,LucreţiuM
提出了一种信号检测模型，该模型结合了信号模型和噪声模型，提供了信号出现频率的数学描述，以及在背景中自然出现的信号样特征的数学描述。我们推导出表达式的似然函数的整个合奏观察到的可疑位置，在各种可能的组合信号和假信号的候选人。因此，这种形式主义是能够描述几种新类型的检测测试使用似然比统计。我们有一个全局图像异常测试和一个单独的信号检测测试。该模型还提供了一种替代机制，其中选择具有最大似然的信号和噪声特征候选的组合。这些测试可以用各种工作特性曲线（ROC、LROC、FROC等）进行分析。在该模型的数学形式中，表征可疑特征的所有细节都被简化为单个标量函数，我们将其命名为信号特异性函数，表示信号相对于给定大小的图像中具有相同值的假信号的频率具有特定值的频率。信号特异性函数对所发现的特征的可疑程度进行分级，并且可以用于将所有可疑特征特性统一为单个分数，然后应用如Swensson检测模型中的通常决策惯例[Med.Phys.23，1709-1725（1996）]。我们提出了几个例子，这些测试进行了比较。我们还展示了如何信号特异性函数可以用来模拟不同程度的准确性的观察者的知识，图像噪声和信号的统计特性。有关方面的建模的人类观察员进行了讨论。
A signal detection model is presented that combines a signal model and a noise model providing mathematical descriptions of the frequency of appearance of the signals, and of the signal‐like features naturally occurring in the background. We derive expressions for the likelihood functions for the whole ensemble of observed suspicious locations, in various possible combinations of signals and false signal candidates. As a result, this formalism is able to describe several new types of detection tests using likelihood ratio statistics. We have a global image abnormality test and an individual signal detection test. The model also provides an alternative mechanism in which is selected the combination of signal and noise features candidates that has the maximum likelihood. These tests can be analyzed with a variety of operating characteristic curves (ROC, LROC, FROC, etc.). In the mathematical formalism of the model, all the details characterizing the suspicious features are reduced to a single scalar function, which we name the signal specificity function, representing the frequency that a signal takes a certain value relative to the frequency of having a false signal with the same value in an image of given size. The signal specificity function ranks the degree of suspiciousness of the features found, and can be used to unify into a single score all the suspicious feature characteristics, and then apply the usual decision conventions as in the Swensson's detection model [Med. Phys. 23, 1709–1725 (1996)]. We present several examples in which these tests are compared. We also show how the signal specificity function can be used to model various degrees of accuracy of the observer's knowledge about image noise and signal statistical properties. Aspects concerning modeling of the human observer are also discussed.