CRT displays are generally used for softcopy display in the digital reading room, but LCDs are being used more frequently. LCDs have many useful properties, but can suffer from significant degradation when viewed off-axis. We compared observer performance and human visual system model performance for on and off-axis CRT and LCD viewing. 400 mammographic regions of interest with different lesion contrasts were shown on and off-axis to radiologists on a CRT and LCD. Receiver Operating Characteristic (ROC) techniques were used to analyze observer performance and results were correlated with the predictions of the human vision model (JNDmetrix model). Both sets of performance metrics showed that LCD on-axis viewing was better than the CRT; and off-axis was significantly better with the CRT. Off-axis LCD viewing of radiographs can degrade observer performance compared to a CRT.
The JNDmetrix human visual system model developed by the Sarnoff Corporation is used to predict observer performance on visual discrimination tasks. It begins with two paired images as the initial input and ends with a JND map that shows the magnitude and spatial location of visible differences between the two input images. The goal of this experiment was to determine if the location and magnitude of JNDs identified by the model corresponded to visual search parameters of the human observer. Radiologists searched 20 mammograms with multiple masses and microcalcifications of different subtleties as their eye-position was recorded. The JNDmetrix model analyzed the same images and identified, with JNDs, discriminable areas on the images. Lesions with lower subtlety ratings were detected later in search than more obvious ones (FNs later than TPs). When the subtler lesions were detected (TP) dwell time was longer than more obvious lesions, but the FNs received shorter total dwell. The subtler lesions when detected (TP) received more total fixation clusters than more obvious ones, but the FNs received fewer. The correlation between the model JNDs and the eye-position parameters was high. Understanding the influence of lesion subtlety on search may help us better model and predict human observer performance.
A discrepancy exists between two studies that investigated psychophysical detection of simulated lesions (e.g. gaussians or designer nodules) embedded in filtered noise images (Johnson et al, 2002; Burgess et al, 2003). Johnson et al, 2002 identified a significant difference in the slope of the contrast detail plots (CD plots) as the presentation methodology in a 2AFC task was changed from the unlike background (unpaired) to identical backgrounds (paired). In comparable experiments, Burgess et al, 2003 challenged the results by finding no difference between the slopes (both positive) of the CD plots when using paired backgrounds or unpaired backgrounds. We found that a significant difference between the two studies, namely the presence of a circular fixation cue was responsible for the discrepancy. The detection noise due to positional uncertainty was sufficient to reduce subject's threshold for small target diameters. This effect was amplified in the paired background, switching the CD plot from a negative slope (without fixation) to a positive slope (with fixation). The effect was less dramatic with the unpaired backgrounds, however intra-observer variability seemed to be reduced with fixation cues. These results significantly reduce the discrepancies in C-D characteristics between the two studies.
Previous studies in which the JNDmetrix visual discrimination model (VDM) was applied to predict effects of image display and processing factors on lesion detectability have shown promising results for mammographic images with microcalcification clusters. In those studies, just-noticeable-difference (JND) metrics were computed for signal-present and signal-absent image pairs with the same background. When this "paired discriminability" method was applied to Gaussian signals in 1/f3 filtered noise, however, it was unable to predict detection thresholds measured in 2AFC trials for different backgrounds. We suggested previously (SPIE 2002) that a statistical model observer using channel responses from "single-ended" VDM simulations could predict detection performance with different backgrounds. The implementation and evaluation of that VDM-channelized model observer is described in this paper. Model performance was computed for sets of signal and noise images from two observer performance studies involving the detection of simulated or real breast masses. For the first study, the VDM-channelized model observer was able to predict the dependence of detection thresholds on signal size (contrast-detail slope) for 2AFC detection of Gaussian signals on different 1/f3 noise backgrounds. Variations in the detectability of masses in mammograms from the second study correlated well with model performance as a function of display type (LCD vs. CRT) and viewing angle (on-axis vs. 45° off-axis). The performance of the VDM-channelized model observer was superior to results obtained using either the VDM paired discriminability method or a conventional nonprewhitening model observer.
The goal of this project was to evaluate a human visual system model (JNDmetrix) based on JND and frequency-channel vision-modeling principles to predict the effects of monitor veiling glare on observer performance in interpreting radiographic images. The veiling glare of a high-performance CRT and an LCD display was measured. A series of mammographic images with masses of different contrast levels was generated. Six radiologists viewed the sets of images on both monitors and reported their decision confidence about the presence of a mass. The images were also run through the JNDmetrix model. Veiling glare affected observer performance (ROC Az). Performance was better on the LCD display with lower veiling glare compared to the CRT with higher veiling glare. The JNDmetrix model predicted the same pattern of results and the correlation between human and computer observers was high. Veiling glare can affect significantly observer performance in diagnostic radiology. A possible confound exists in that two different monitors were used and other physical parameters may contribute to the differences observed. A new set of studies is underway to remove that confound.
The detectability of low-contrast lesions in medical images can be affected significantly by the choice of grayscale window width and level (W/L) for electronic display. Our objective was to measure the effects of various W/L conditions on lesion detectability in simulated and real mammographic images, and then correlate observer performance with predictions of detection thresholds derived from a visual discrimination model (VDM). In the first experiment, detection thresholds were measured in 2AFC trials for five W/L conditions applied to simulated mammographic backgrounds and lesions (i.e., Gaussian "masses" and blurred-disk "microcalcification clusters") using nonmedical observers. In the second experiment, the detectability of real microcalcification clusters in digitized mammograms was evaluated for three W/L conditions in an ROC observer study with mammographers. For the simulated images, there was generally good agreement between model and experimental thresholds and their variations across W/L conditions. Both experimental and model results showed significant reductions in thresholds when W/L processing was applied locally near the lesion. ROC results with digitized mammograms read by radiologists, however, failed to show enhanced detection of microcalcifications using a localized W/L frame, probably due to the nonuniform appearance of parenchymal tissue across the image.
Proc. SPIE. 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Image compression, Visual process modeling, Visualization, Data storage, Computer programming, Mammography, Visibility, Image quality standards, Visual compression, Picture Archiving and Communication System
The Sarnoff JNDmetrix visual discrimination model (VDM) was applied to predict the visibility of compression artifacts in mammographic images. Sections of digitized mammograms were subjected to irreversible (lossy) JPEG and JPEG 2000 compression. The detectability of compressed images was measured experimentally and compared with VDM metrics and PSNR for the same images. Artifacts produced by JPEG 2000 compression were generally easier for observers to detect than those produced by JPEG encoding at the same compression ratio. Detection thresholds occurred at JPEG 2000 compression ratios from 6:1 to 10:1, significantly higher than the average 2:1 ratio obtained for reversible (lossless) compression. VDM predictions of artifact visibility were highly correlated with observer performance for both encoding techniques. Performance was less correlated with encoder bit rate and PSNR, which was a relatively poor predictor of threshold bit rate across images. Our results indicate that the VDM can be used to predict the visibility of compression artifacts and guide the selection of encoder bit rate for individual images to maintain artifact visibility below a specified threshold.
The Sarnoff JNDmetrix visual discrimination model (VDM) was applied to predict human psychophysical performance in the detection of simulated mammographic lesions. Contrast thresholds for the detection of synthetic Gaussian masses on mean backgrounds and simulated mammographic backgrounds were measured in two-alternative, forced-choice (2AFC) trials. Experimental thresholds for 2-D Gaussian signal detection decreased with increasing signal size on mean backgrounds and on 1/f3 filtered noise images presented with identical (paired) backgrounds. For 2AFC presentations of different (unpaired) filtered noise backgrounds, detection thresholds increased with increasing signal diameter, consistent with a decreasing signal-to-noise ratio. Thresholds for mean and paired filtered noise backgrounds were used to calibrate a new low-pass, spatial-frequency channel in the VDM. The calibrated VDM was able to predict accurate detection thresholds for Gaussian signals on mean and paired 1/f3 filtered noise backgrounds. To simulate noise-limited detection thresholds for unpaired backgrounds, an approach is outlined for the development of a VDM-based model observer based on statistical decision theory.