Abstract
Model observers which can serve as surrogates for human observers could be valuable for the assessment of image quality. For this purpose, a good correlation between human and model observer is a prerequisite. The nonprewhitening model observer with eye filter (NPWE) is an example of such a model observer. The eye filter is a mathematical approximation of the human contrast sensitivity function (CSF) and is included to correct for the response of the human eye. In the literature several approximations of the human CSF were found. In this study the relation between human and NPWE observer performance using seven eye filters is evaluated in two-alternative-forced-choice (2-AFC) detection experiments involving disks of varying diameter and signal energy and two background types. The results show that the shape of the CSF has an impact on the correlation between human and model observer. The inclusion of a CSF may indeed improve the relation between human and model observer. However, we did not find an eye filter which is optimal in both backgrounds.
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References
Rolland, J.P., Barrett, H.H.: Effect of random background inhomogeneity on observer detection performance. J. Opt. Soc. Am. A 9(5), 58–649 (1992)
Burgess, A.E.: Statistically defined backgrounds: Performance of a modified nonprewhitening observer model. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 11(4), 42–1237 (1994)
Castella, C., et al.: Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm. Optics Express 16(11), 7595–7607 (2008)
Burgess, A.E., Jacobson, F.L., Judy, P.F.: Human observer detection experiments with mammograms and power-law noise. Med. Phys. 28(4), 37–419 (2001)
Burgess, A.E., Li, X., Abbey, C.K.: Visual signal detectability with two noise components: Anomalous masking effects. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 14(9), 42–2420 (1997)
Barten, P.G.J.: Evaluation of subjective image quality with the square-root integral method. Journal of the Optical Society of America a Optics Image Science and Vision 7(10), 2024–2031 (1990)
Bochud, F.O., Abbey, C.K., Eckstein, M.P.: Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds. Journal of the Optical Society of America a Optics Image Science and Vision 17(2), 193–205 (2000)
Borasi, G., et al.: Contrast-detail analysis of three flat panel detectors for digital radiography (vol. 33, p. 1707, 2006). Medical Physics 33(9), 3580–3580 (2006)
Webster, M.A., Miyahara, E.: Contrast adaptation and the spatial structure of natural images. Journal of the Optical Society of America a Optics Image Science and Vision 14(9), 2355–2366 (1997)
Kelly, D.H.: Visual Contrast Sensitivity. Optica. Acta. 24(2), 107–129 (1977)
Barten, P.G.J.: Contrast sensitivity of the human eye and its effect on image quality, vol. PM72. SPIE Press book (1999)
Park, S., et al.: Incorporating Human Contrast Sensitivity in Model Observers for Detection Tasks. IEEE Transactions on Medical Imaging 28(3), 339–347 (2009)
Watson, A.B., Ahumada, A.J.: A standard model for foveal detection of spatial contrast. Journal of Vision 5(9), 717–740 (2005)
Kramer, M.: R2 statistics for mixed models. In: Proceedings of the Conference on Applied Statistics in Agriculture (2005)
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Bouwman, R.W., van Engen, R.E., Dance, D.R., Young, K.C., Veldkamp, W.J.H. (2014). Evaluation of Human Contrast Sensitivity Functions Used in the Nonprewhitening Model Observer with Eye Filter. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_99
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DOI: https://doi.org/10.1007/978-3-319-07887-8_99
Publisher Name: Springer, Cham
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