Abstract
The problem of detecting a signal in a noisy background is highlighted from the point of view of communication systems, sensory perception studies and assessment of the intrinsic quality of medical imaging equipment. The concepts of “psychometric curves” (PMC) and of “receiver operating characteristics” (ROC) are introduced.
The likelihood ratio approach is discussed to illustrate the techniques for constructing optimum detectors for communication systems, but at the same time to find the best figure of merit to perform the quality assessment. The concepts of “detectability index” and of the “area under ROC” are shown to be the most suitable figures of merit which can also profitably be employed in medical imaging experiments with human observers. Several medical imaging modalities are discussed and the methods to specify the detectability index from measurable characteristics of the imaging performance and of the intrinsic noise of the equipment are presented.
Keywords
- Receiver Operating Characteristic
- Human Observer
- Internal Noise
- Receiver Operating Characteristic Analysis
- Ideal Observer
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Burgess, A.E., Wagner, R.F., Jennings R.J. and Barlow H.B. (1981). Efficiency of human visual signal detection. Science 214, pp. 93–94
Burgess, A.E. (1986). On observer internal noise. In: Proceedings of SPIE, Vol. 626, Schneider, R.H. and Dwyer S.J., eds. SPIE, Washington, pp. 208–213.
Dainty, J.C. and Shaw, R. (1974). Image Science. Academic Press, London.
Dorfman, D.D. and Alf, E. (1969). Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervalsrating methods data. J. Math. Psychol. 6, pp. 487–496.
Egan, J.P. and Clark, F.R. (1966). Psychophysics and signal detection. In: Experimental methods and instrumentation in Psychology. Sidowski J.B., ed. McGraw Hill, New York.
Eijkman, E.G.J. (1979). Psychophysics. In: Handbook of Psychonomics, Vol. 1. Michon, J.A., Eijkman, E.G.J. and De Klerk, L.F.W. eds. North Holland Pubi., Amsterdam, pp. 303–363.
Green, D.M. and Swets, J.A. (1966). Signal Detection Theory and Psychophysics. Krieger, Huntington.
Hershman, R.L. and Lichtenstein, M. (1967). Detection and localization: an extension of the theory of signal detectability. J. Acoust. Soc. Am. 42, pp. 446–452.
Judy, P.F. and Swensson, R.G. (1981). Lesion detection and signal-to-noise ratio in CT images. Med. Phys. 8, pp. 13–23.
Metz, C.E. (1978). Basic principles of ROC analysis. Sem. Nucl. Med. 8, pp. 283–298.
Patton, D.D., ed. (1978). Seminars in Nuclear Medicine VIII.
Peterson, W.W., Birdsall, T.G., and Fox, W.C. (1954). The theory of signal detectability. Trans. IRE PGIT 4, pp. 171–212.
Schulman, A.L. and Mitchell, R.R. (1966). Operating characteristics from yes-no and forced choice procedures. J. Acoust. Soc. Am. 40, pp. 473–477.
Simpson, A.J. and Fitter, M.J. (1973). What is the best index of detectability? Psychol. Bull. 80, pp. 481–488.
Smith, S.W., Wagner, R.F., Sandrik, J.M. and Lopez, H. (1983). Low contrast detectability and contrast/detail analysis in medical ultrasound. IEEE Trans. SU. 30, pp. 164–173.
Starr, S.J., Metz, C.E., Lusted, L.B. and Goodenough, D.J.C. (1975). Visual detection and localization of radiographic images. Radiology 116, pp. 533–538.
Swets, J.A. ed. (1964). Signal Detection and Recognition by Human Observers: Contemporary Readings. Wiley, New York.
Swets, J.A., Pickett, R.M. et al. (1979). Assessment of diagnostic technologies. Science 205, pp. 753–759.
Swets, J.A. and Pickett, R.M. (1982). Evaluation if Diagnostic Systems: Methods from Signal Detection Theory. Academic Press, New York.
Swets, J.A. (1986). Indices of discrimination or diagnostic accuracy: their ROC’S and implied models. Psychol. Bull. 99, pp. 100–117.
Thomas, J.B. (1969). An Introduction to Statistical Communication Theory. Wiley, New York.
Thijssen, J.M. and Vendrik, A.J.H. (1968). Internal noise and transducer function in sensory detection experiments: evaluation of psychometric curves and of ROC curves. Perc. Psychophys. 3, pp. 387–400.
Thijssen, J.M. and Vendrik, A.J.H. (1971). Differential luminance sensitivity of the human visual system. Perc. Psychophys. 10, pp. 58–64.
Van Trees, H.L. (1971). Detection, Estimation and Modulation Theory Part I II. Wiley, New York.
Wagner, R.F. and Brown, D.G. (1985). Unified SNR analysis of medical imaging systems. Phys. Med. Biol. 30, pp. 489–516.
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© 1988 Springer-Verlag Berlin Heidelberg
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Thijssen, J.M. (1988). Focal Lesions in Medical Images: A Detection Problem. In: Viergever, M.A., Todd-Pokropek, A. (eds) Mathematics and Computer Science in Medical Imaging. NATO ASI Series, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83306-9_22
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DOI: https://doi.org/10.1007/978-3-642-83306-9_22
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