We determined contrast thresholds for lesion detection as a function of lesion size in both mammograms and filtered noise backgrounds with the same average power spectrum, P(f)=B/f3. Experiments were done using hybrid images with digital images of tumors added to digitized normal backgrounds, displayed on a monochrome monitor. Four tumors were extracted from digitized specimen radiographs. The lesion sizes were varied by digital rescaling to cover the range from 0.5 to 16 mm. Amplitudes were varied to determine the value required for 92% correct detection in two-alternative forced-choice (2AFC) and 90% for search experiments. Three observers participated, two physicists and a radiologist. The 2AFC mammographic results demonstrated a novel contrast-detail (CD) diagram with threshold amplitudes that increased steadily (with slope of 0.3) with increasing size for lesions larger than 1 mm. The slopes for prewhitening model observers were about 0.4. Human efficiency relative to these models was as high as 90%. The CD diagram slopes for the 2AFC experiments with filtered noise were 0.44 for humans and 0.5 for models. Human efficiency relative to the ideal observer was about 40%. The difference in efficiencies for the two types of backgrounds indicates that breast structure cannot be considered to be pure random noise for 2AFC experiments. Instead, 2AFC human detection with mammographic backgrounds is limited by a combination of noise and deterministic masking effects. The search experiments also gave thresholds that increased with lesion size. However, there was no difference in human results for mammographic and filtered noise backgrounds, suggesting that breast structure can be considered to be pure random noise for this task. Our conclusion is that, in spite of the fact that mammographic backgrounds have nonstationary statistics, models based on statistical decision theory can still be applied successfully to estimate human performance.

Historically, human signal-detection responses have been assumed to be governed by external determinants (nature of the signal, the noise, and the task) and internal determinants. Variability in the internal determinants is commonly attributed to internal noise (often vaguely defined). We present a variety of experimental results that demonstrate observer inconsistency in performing noise-limited visual detection and discrimination tasks with repeated presentation of images. Our results can be interpreted by using a model that includes an internal-noise component that is directly proportional to image noise. This so-called induced internal-noise component dominates when external noise is easily visible. We demonstrate that decision-variable fluctuations lead to this type of internal noise. Given this induced internal-noise proportionality (sigma i/sigma 0 = 0.75 +/- 0.1), the upper limit to human visual signal-detection efficiency is 64% +/- 6%. This limit is consistent with a variety of results presented in earlier papers in this series.

We have measured the overall statistical efficiency of human subjects discriminating the amplitude of visual pattern signals added to noisy backgrounds. By changing the noise amplitude, the amount of intrinsic noise can be estimated and allowed for. For a target containing a few cycles of a spatial sinusoid of about 5 cycles per degree, the overall statistical efficiency is as high as 0.7 +/- 0.07, and after correction for intrinsic noise, efficiency reaches 0.83 +/- 0.15. Such a high figure leaves little room for residual inefficiencies in the neural mechanisms that handle these patterns.