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Craig K. Abbey
Researcher at University of California, Santa Barbara
Publications - 238
Citations - 4738
Craig K. Abbey is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Observer (quantum physics) & Imaging phantom. The author has an hindex of 37, co-authored 218 publications receiving 4407 citations. Previous affiliations of Craig K. Abbey include University of California, San Francisco & University of Arizona.
Papers
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Proceedings ArticleDOI
Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks
TL;DR: Findings suggest that model observer performance in the computationally more tractable SKEV task can be used to optimize human performance inThe more clinically realistic SKS task using real anatomic backgrounds.
Proceedings ArticleDOI
Effect of image compression for model and human observers in signal-known-statistically tasks
TL;DR: Findings might suggest that the computationally more tractable SKEV models could be used as a good first approximation for automated evaluation of the more clinically realistic SKS task.
Journal ArticleDOI
Non-Gaussian statistical properties of breast images
TL;DR: Non-Gaussian statistical structure in breast images that is manifest in the responses of Gabor filters similar to receptive fields of the early visual system is dependent on how the image data are processed, the modality used to acquire the image, and the density of the breast tissue being imaged.
Journal ArticleDOI
Objective Assessment of Sonographic: Quality II Acquisition Information Spectrum
TL;DR: This paper expands the Kullback-Leibler divergence metric J, which quantifies the diagnostic information contained within recorded radio-frequency echo signals, into a spatial-frequency integral comprised of two spectral components: one describes patient features for low-contrast diagnostic tasks and the other describes instrumentation properties.
Journal ArticleDOI
Measures of performance in nonlinear estimation tasks: prediction of estimation performance at low signal-to-noise ratio
TL;DR: The chi2(pdf-ML) model appears to be suitable for characterization of the influence of the noise level and characteristics, the task, and the object on the shape of the probability density of the ML estimates at low SNR, and provides unique insights into the causes of the variability of estimation performance.