The effects of imaging parameters on detectability have not yet been clarified. Therefore, we investigated the
usefulness of signal-to-noise ratios (SNRs) considered as human visual characteristics, such as the visual spatial
frequency response and the internal noise in the eye-brain system.
We examined the amplitude model (SNRa), matched filter model (SNRm), and internal noise model (SNRi) to study
the relationship between these SNRs and the visual image quality for signal detection. The test images were simulated by
the superimposition of low-contrast signals on a uniform noisy background. The SNRs were obtained for 15 imaging
cases with various signal sizes, signal contrasts, exposure levels, and number of acrylic plates used as breast phantoms.
The SNRs were calculated by measuring the spatial frequency characteristics of the signal, modulation transfer
function (MTF) of the system, display MTF, and overall Wiener spectrum (WS).
In the perceptual evaluation, we applied the 16-alternative forced choice (16-AFC) method. The signal detectability
was defined as the number of detected signals divided by the total number of signals. We studied the relationship
between SNR and signal detectability using Spearman's rank correlation coefficient.
The correlation coefficient of SNRi was 0.93, making it the highest among the three SNR types. That of SNRm was
0.91; it correlated at the same level as SNRi although it is not considered human visual characteristics. That of SNRa
was 0.45. SNRi, which incorporated the visual characteristics, explained the visual image quality well.