3.1 Introduction

The receiver operating characteristics (ROC) analysis has been widely used in signal processing and communications to assess effectiveness of a sensor or detector/device for detection (Poor, 1994). In recent years, it has also become a common evaluation tool for effectiveness of a medical modality in medical diagnosis, specifically for computer-assisted diagnostic systems (Metz, 1978; Swets and Pickett, 1982), automatic target recognition (ATR) (Parker et al., 2005a, 2005b; Blasch and R. Broussard, 2000; Bauman et al., 2005), and fusion analysis (Blasch et al., 2001; Blasch and Plano, 2003; Blasch, 2008). The idea is simple. For a given detector or detection technique how can we objectively evaluate whether or not it is effective and in what sense? There are many criteria or cost functions available for such assessment, such as least-squares error, signal-to-noise ratio, and misclassification error. Unfortunately, none of these criteria can be considered as a general criterion to fit all detection problems. For example, least-squares error or signal-to-noise ratio may be a good criterion for detection problems in signal processing and communications but may not be suitable for measuring image quality or classification accuracy in image processing. So, in order to avoid using a specific criterion for performance evaluation, the ROC analysis is introduced for this purpose. It does not necessarily specify a particular criterion or cost function. Instead, it focuses ...

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