Craig K. Abbey received his PhD in applied mathematics from the University of Arizona in 1998. He was a postdoctoral fellow in medical physics at Cedars-Sinai Medical Center and UCLA from 1998 to 2001. From 2001 to 2004 he was a member of the faculty in biomedical engineering at UC Davis. His primary affiliation is in the Dept. of Psychological and Brain Science at UC Santa Barbara. His research focuses on how useful diagnostic information is extracted from images in the presence of noise and other signal distortions. Methods for investigating this topic include theoretical analysis of image statistics as well as visual psychophysics for evaluating human observer performance.
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We evaluate the Pre-Whitening Matched Filter (PWMF), “Eye-Filtered” Non-Pre-Whitening (NPWE) and Sparse-Channelized Difference-of-Gaussian (SDOG) models for predictive performance, and we compare various training and testing regimens. These include “training” by using reported values from the literature, training and testing on the same set of experimental conditions, and training and testing on different sets of experimental conditions. Of this latter category, we use both leave-one-condition-out for training and testing as well as a leave-one-factor-out strategy, where all conditions with a given factor level are withheld for testing. Our approach may be considered a fixed-reader approach, since we use all available readers for both training and testing.
Our results show that training models improves predictive accuracy in these tasks, with predictive errors dropping by a factor of two or more in absolute deviation. However, the fitted models are not fully capturing the effects apodization and other factors in these tasks.
High-resolution μ CT
A set of 3510 screening cases read as part of a national screening program by 10 qualified radiologist readers forms the basis for our study. The readers give a suspicion score (on a standalone device) in addition to their standard screening report. The score is time-stamped so that reading order and batch grouping can be assessed. Batches are defined as groups of cases with less than 10 minutes (600 s) between sequential readings. We use Kendall’s Tau, weighted by batch size, as a measure of association between batch position, and suspicion score or reading time. Randomization is used to get confidence intervals on the null hypothesis ( τ=0 ).
We find significant associations between batch position and both of the variables under investigation (suspicion scores and reading time). The associations are negative, suggesting that both suspicion and reading time are reduced at later points in a batch. These results are consistent with the hypothesis that readers are becoming visually adapted to the properties of the images as they progress through a batch of cases, affecting their perception and decisions about the images.
In the 3D tasks, the image display we use allows subjects to freely scroll through a volumetric image, and a localization response is made through a mouse-click on the image. The search region has a relatively modest size (approx. 8.8° visual angle). Localization responses are considered correct if they are close to the target center (within 6 voxels). The classification image methodology uses noise fields from the incorrect localizations to build an estimate of the weights used by the observer to perform the task. The basic idea is that incorrect localizations occur in regions of the image where the noise field matches the weighting profile, thereby eliciting a strong internal response.
The efficiency results indicate differences between 2D and 3D search tasks, with lower efficiency for large target in the 3D task. The classification images suggest that this finding can be explained by the lack of spatial integration across slices.
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