Kathryn S. Espig

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We investigate improvements to our 3D model observer with the goal of better matching human observer performance as a function of viewing distance, effective contrast, maximum luminance, and browsing speed. Two nonlinear methods of applying the human contrast sensitivity function (CSF) to a 3D model observer are proposed, namely the Probability Map (PM) and(More)
Within the framework of a virtual clinical trial for breast imaging, we aim to develop numerical observers that follow the same detection performance trends as those of a typical human observer. In our prior work, we showed that by including spatio-temporal contrast sensitivity function (stCSF) of human visual system (HVS) in a multi-slice channelized(More)
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(More)
It is our conjecture that the variability of colors in a pathology image effects the interpretation of pathology cases, whether it is diagnostic accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital pathology images are analyzed to quantify the perceived difference in color that occurs due to display aging, in particular a change(More)
Barten’s model of spatio-temporal contrast sensitivity function of human visual system is embedded in a multi-slice channelized Hotelling observer. This is done by 3D filtering of the stack of images with the spatio-temporal contrast sensitivity function and feeding the result (i.e., the perceived image stack) to the multi-slice channelized Hotelling(More)
We specify a notion of perceived background tissue complexity (BTC) that varies with lesion shape, lesion size, and lesion location in the image. We propose four unsupervised BTC estimators based on: perceived pre and postlesion similarity of images, lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), tissue anomaly(More)
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