Ms. Yui Hayashi Profile

Yui Hayashi

at Nagoya Univ

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Proceedings Article | 3 March 2011 Paper

Study of signal-to-noise ratios considered human visual characteristics

Yui Hayashi, Maki Yamada, Yukiyoshi Kimura, Naotoshi Fujita, Yoshie Kodera

Proceedings Volume 7966, 79661S (2011) https://doi.org/10.1117/12.877998

KEYWORDS: Signal to noise ratio, Signal detection, Visualization, Image quality, Modulation transfer functions, Interference (communication), Spatial frequencies, Eye models, Visual process modeling, Image visualization

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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.

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