The objective evaluation of imaging systems requires three important components: (1) identification of the intended use of the resulting images, which we shall refer to as the task; (2) specification of the observer, who will make use of the images in order to perform the task; and (3) a thorough understanding of the statistical properties of the objects and resulting images. With these components, a figure of merit can be determined for evaluating the performance of the observer on the specified task . In the next sections we consider each of these elements more fully.
The ideal observer is a model that describes the performance of the optimum decision maker on a given decision task. The ideal observer therefore provides an upper bound on task performance that can be used as a gold standard in the objective evaluation of imaging systems. Knowledge of ideal-observer performance allows the physicist and perceptual scientist to determine when information needed to perform a given task is readily extracted from an image by the human observer. When ideal-observer performance is found to be far above human performance, either the system should be redesigned to better match the human's capabilities, or the human observer should be augmented or even replaced by a machine reader.
In this chapter we detail the sense in which an ideal observer is optimum, describe the calculation of the ideal-observer strategy and ensuing performance metric for a number of tasks, and show how an imaging system can be evaluated using figures of merit from signal-detection theory that summarize ideal-observer performance. Results of investigations comparing human performance to that of the ideal observer are provided for a number of visual tasks.
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