On anthropomorphic decision making in a model observer

30 Jun 2015Ali R. N. AvanakiKathryn S. EspigTom R. L. KimpeAndrew D. A. Maidment

By analyzing human readers' performance in detecting small round lesions in simulated digital breast tomosynthesis background in a location known exactly scenario, we have developed a model observer that is a better predictor of human performance with different levels of background complexity (i.e., anatomical and quantum noise). Our analysis indicates that human observers perform a lesion detection task by combining a number of sub-decisions, each an indicator of the presence of a lesion in the image stack... (read more)

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