Skip to main content

Part of the book series: NATO ASI Series ((NATO ASI F,volume 98))

Abstract

From a practical point of view, image quality in medicine must be defined in terms of the decisons that physicians can make by reading the images. Image-based decisions concerning the actual state of an object or patient are evaluated most meaningfully by ROC analysis, which has been used to quantify detection performance in sensory psychophysics since the early 1960s and more recently has been applied to evaluate diagnostic systems. After surveying briefly some considerations that motivate the use of ROC analysis in medical image evauation, this manuscript addresses various practical issues of experimental design and data analysis, including the selection of cases and observers, the need for standards of truth, reading-order effects, data collection, curve fitting, statistical testing, and possible generalizations of conventional ROC methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Begg, C.B., and McNeil, B.J. (1988). Assessment of radiologic tests: control of bias and other design considerations, Radiology 167, pp. 565–569.

    PubMed  CAS  Google Scholar 

  • Bunch, P.C., Hamilton, J.F., Sanderson, G.K., and Simmons, A.H. (1977). A free response approach to the measurement and characterization of radiographic observer performance. Proc. SPIE 127, pp. 124–135.

    Google Scholar 

  • Chakraborty, D.P. (1988). Maximum likelihood analysis of free response receiver operating characteristic (FROC) data. Personal communication.

    Google Scholar 

  • Dorfman, D.D., and Alf, E. (1969). Maximum likelihood estimation of parameters of signal detection theory and determination of confidence intervals — rating method data, J. Math. Psychol. 6, pp. 487–496.

    Article  Google Scholar 

  • Egan, J.P. (1975). Signal detection theory and ROC analysis, Academic Press, New York.

    Google Scholar 

  • Grey, D.R., and Morgan, B.J.T. (1972). Some aspects of ROC curve-fitting: normal and logistic models, J. Math. Psychol. 9, pp. 128–139.

    Article  Google Scholar 

  • Getty, D.J., Pickett, R.M., D’Orsi, C.J., and Swets, J.A. (1988). Enhanced interpretation of diagnostic images, Invest. Radiol. 23, pp. 240–252.

    Article  PubMed  CAS  Google Scholar 

  • Goodenough, D.J., and Metz, C.E. (1977). Implications of a “noisy” observer to data processing techniques. In: Information processing in medical imaging, C. Raynaud and A.E. Todd-Pokropek (eds.), CEA, Orsay, pp. 400–419.

    Google Scholar 

  • Gray, R., Begg, C.B., and Greenes, R.A. (1984). Construction of receiver operating characteristic curves when disease verification is subject to selection bias, Med. Decis. Making 4, pp. 151–164.

    Article  PubMed  CAS  Google Scholar 

  • Green, D.M., and Swets, J.A. (1966). Signal detection theory and psychophysics, Wiley, New York. Reprint with corrections: (1974). Krieger, Huntington NY.

    Google Scholar 

  • Hanley, J.A., and McNeil, B.J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143, pp. 29–36.

    PubMed  CAS  Google Scholar 

  • Hanley, J.A., and McNeil, B.J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology 148, pp. 839–843.

    PubMed  CAS  Google Scholar 

  • International Atomic Energy Agency (1977). IAEA co-ordinated research programme on the intercomparison of computer-assisted scinigraphic techniques. In: Medical radionuclide imaging, IAEA, Vienna, 1, pp. 585–615.

    Google Scholar 

  • Kendall, M., and Stewart, A. (1976). The advanced thoery of statistics, Hafner, New York, 3, pp. 182–243.

    Google Scholar 

  • Lusted, L.B. (1978). General problems in medical decision making, with comments on ROC analysis, Seminars Nucl. Med. 8, pp. 299–306.

    Article  CAS  Google Scholar 

  • Loo, L.-N., Doi, K., Ishida, M., Metz, C.E., Chan, H.-P., Higashida, Y., and Kodera, Y. (1983). An empirical investigation of variability in contrast-detail diagram measurements, Proc. SPIE 419, pp. 68–76.

    Google Scholar 

  • MacMillan, N.A., and Kaplan, H.L. (1985). Detection theory analysis of group data: estimating sensitivity from average hit and false alarm rates, Psychol. Bull. 98, pp. 185–199.

    Article  PubMed  CAS  Google Scholar 

  • McNeil, B.J., Keeler, E., and Adelstein, S.J. (1975). Primer on certain elements of medical decision making, N. Engl. J. Med. 293, pp. 211–215.

    Article  PubMed  CAS  Google Scholar 

  • McNeil, B.J., and Hanley, J.A. (1984). Statistical approaches to the analysis of receiver operating characteristic (ROC) curves, Med. Decis. Making 4, pp. 137–150.

    Article  PubMed  CAS  Google Scholar 

  • Metz, C.E. (1978). Basic principles of ROC analysis, Seminars Nucl. Med. 8, pp. 283–298.

    Article  CAS  Google Scholar 

  • Metz, C.E. (1979). Applications of ROC analysis in diagnostic image evaluation. In: The physics of medical imaging: recording system measurements and techniques, A. Haus (ed.), Am. Inst. Phys., New York, pp. 546–572.

    Google Scholar 

  • Metz, C.E. (1986a). ROC methodology in radiologic imaging, Invest. Radiol. 21, pp. 720–733.

    Article  PubMed  CAS  Google Scholar 

  • Metz, C.E. (1986b). Statistical analysis of ROC data in evaluating diagnostic performance. In: Multiple regression analysis: applications in the health sciences, D. Herbert and R. Myers (eds.), Am. Inst. Phys., New York, pp. 365–384.

    Google Scholar 

  • Metz, C.E. (1988). Some practical issues of experimental design and data analysis in radiological ROC syudies, Invest. Radiol. (in press).

    Google Scholar 

  • Metz, C.E., and Kronman, H.B. (1980). Statistical significance tests for binormal ROC curves, J. Math. Psychol. 22, pp. 218–243.

    Article  Google Scholar 

  • Metz, C.E., Kronman, H.B., Wang, P.-L., and Shen, J.-H. (1985). ROCFIT: a modified maximum likelihood algorithm for estimating a binormal ROC curve from confidence-rating data, Dept. Radiology, Univ. Chicago, Chicago. Mini-and micro-computer versions of this and other software for ROC analysis are available from the author at no cost.

    Google Scholar 

  • Metz, C.E., Starr, S.J., Lusted, L.B., and Rossmann, K. (1975). Progress in evaluation of human observer visual detection performance using the ROC curve approach. In: Information processing in medical imaging, C. Raynaud and A.E. Todd-Pokropek (eds.), CEA, Orsay, pp. 420–436.

    Google Scholar 

  • Metz, C.E., Wang, P.-L., and Kronman, H.B. (1984). A new approach for testing the significance of differences between ROC curves measured from correlated data. In: Information processing in medical imaging, F. Deconinck (ed.), Nijhoff, The Hague, pp. 432–445.

    Chapter  Google Scholar 

  • Ransohoff, D.F., and Feinstein, A.R., (1978). Problems of spectrum and bias in evaluating the efficacy of diagnostic tests, N. Engl. J. Med. 299, pp. 926–930.

    Article  PubMed  CAS  Google Scholar 

  • Starr, S.J., Metz, C.E., Lusted, L.B., and Goodenough, D.J. (1975). Visual detection and localization of radiographic images, Radiology 116, pp. 533–538.

    PubMed  CAS  Google Scholar 

  • Swets, J.A. (1979). ROC analysis applied to the evaluation of medical imaging techniques, Invest. Radiol. 14, pp. 109–121.

    Article  PubMed  CAS  Google Scholar 

  • Swets, J.A. (1986a). Indices of discrimination or diagnostic accuracy: their ROCs and implied models, Psychol. Bull. 99, pp. 100–117.

    Article  PubMed  CAS  Google Scholar 

  • Swets, J.A. (1986b). Form of empirical ROCs in discrimination and diagnostic tasks: implications for theory and measurementof performance, Psychol. Bull. 99, pp. 181–198.

    Article  PubMed  CAS  Google Scholar 

  • Swets, J.A., and Pickett, R.M. (1982). Evaluation of diagnostic systems: methods from signal detection theory, Academic Press, New York.

    Google Scholar 

  • Weinstein, M.C., and Feinberg, H.V. (1980). Clinical decision analysis, Saunders, Philadelphia.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Metz, C.E. (1992). Evaluation of Medical Images. In: Todd-Pokropek, A.E., Viergever, M.A. (eds) Medical Images: Formation, Handling and Evaluation. NATO ASI Series, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77888-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-77888-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-77890-2

  • Online ISBN: 978-3-642-77888-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics