Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-06T02:33:54.326Z Has data issue: false hasContentIssue false

Part I - Historical Reflections and Theoretical Foundations

Published online by Cambridge University Press:  20 December 2018

Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Appiah, K.A. (2008). Experiments in Ethics. Cambridge, MA: Harvard University Press.Google Scholar
Barrett, H.H., Myers, K.J. (2003). Foundations of Image Science. Hoboken, NJ: John Wiley.Google Scholar
Béclère, A. (1964). A physiologic study of vision in fluoroscopic examinations. In: Bruwer, A. (ed.) Classic Descriptions in Diagnostic Roentgenology. Springfield, IL: Charles C. Thomas.Google Scholar
Beiden, S.V., Wagner, R.F., Campbell, G., et al. (2001). Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across modalities. Acad Radiol, 8, 605615.Google Scholar
Berbaum, K.S., Dorfman, D.D., Franken, E.A., Jr. (1989). Measuring observer performance by ROC analysis: indications and complications. Invest Radiol, 24, 228233.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., et al. (1990). Satisfaction of search in diagnostic radiology. Invest Radiol, 25, 133140.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., et al. (1991). Time course of satisfaction of search. Invest Radiol, 26, 640648.Google Scholar
Berbaum, K.S., El-Khoury, G.Y., Franken, E.A., Jr. (1994). Missed fractures resulting from satisfaction of search effect. Emerg Radiol, 1, 242249.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., et al. (2000). Role of faulty decision making in the satisfaction of search effect in chest radiography. Acad Radiol, 7, 10981106.Google Scholar
Berbaum, K.S., Brandser, E.A., Franken, E.A., et al. (2001). Gaze dwell times on acute trauma injuries missed because of satisfaction of search. Acad Radiol, 8, 304314.Google Scholar
Birkelo, C.C., Chamberlain, W.E., Phelps, P.S., et al. (1947). Tuberculosis case finding. A comparison of the effectiveness of various roentgenographic and photofluorographic methods. JAMA, 133, 359366.Google Scholar
Bunch, P.C., Hamilton, J.F., Sanderson, G.K., et al. (1978). A free-response approach to the measurement and characterization of radiographic observer performance. J Appl Photogr Eng, 4, 166171.Google Scholar
Burger, G.C.E. (1949). The perceptibility of details in roentgen examinations of the lung. Acta Radiol Diag, 31, 193222.Google Scholar
Burger, G.C.E. (1950). Phantom tests with X-ray. Philips Technical Review, 11, 291298.Google Scholar
Burger, G.C.E., Van Dijk, B. (1936). Über die physiologischen Grundlagen der Durchleuchtung. Fortschr Rontg, 54, 492496.Google Scholar
Burgess, A.E. (1995). Image quality, the ideal observer, and human performance of radiologic detection tasks. Acad Radiol, 2, 522526.Google Scholar
Burgess, A.E. (1999). The Rose model, revisited. J Opt Soc Am A, 16, 633646.Google Scholar
Burgess, A.E., Wagner, R.F., Jennings, R.J., et al. (1981). Efficiency of human visual signal discrimination. Science, 214, 9394.Google Scholar
Chakraborty, D.P. (1989). Maximum likelihood analysis of free response operating characteristic (FROC) data. Med Phys, 16, 561568.Google Scholar
Chakraborty, D.P. (2002). Statistical power in observer performance studies: comparison of receiver operating characteristic and free-response methods in tasks involving localization. Acad Radiol, 9, 147156.Google Scholar
Chakraborty, D.P., Berbaum, K.S. (2004). Observer studies involving detection and localization: modeling, analysis, and validation. Med Phys, 31, 23132330.Google Scholar
Chamberlain, W.E. (1942). Fluoroscopes and fluoroscopy. Radiology, 38, 383412.Google Scholar
Chesters, M.S. (1992). Human visual perception and ROC methodology in medical imaging. Phys Med Biol, 37, 14331476.Google Scholar
Coltman, J.W. (1948). Fluoroscopic image brightening by electronic means. Radiology, 51, 359367.Google Scholar
De Vries, H. (1943). The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and visual acuity of the eye. Physica, 10, 553564.Google Scholar
Dodd, L.E., Wagner, R.F., Armato, S.G., 3rd, et al. (2004). Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium. Acad Radiol, 11, 462475.Google Scholar
Doi, K. (2006). Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology. Phys Med Biol, 51, R5–27.Google Scholar
Dorfman, D., Alf, E.J. (1969). Maximum likelihood estimation of parameters of signal-detection theory and determination of confidence intervals – rating method data. J Math Psych, 6, 487496.Google Scholar
Dorfman, D.D., Berbaum, K.S., Metz, C.E. (1992). Receiver operating characteristic analysis. Generalization to the population of readers and patients with the jackknife method. Invest Radiol, 27, 723731.Google Scholar
Eckstein, M.P. (2001). The perception of medical images 1941–2001. Opt Photonics News, 12, 34–40.Google Scholar
Eckstein, M., Whiting, J. (1995). Lesion detection in structured noise. Acad Radiol, 2, 249253.Google Scholar
Editorial (1947). The “personal equation” in the interpretation of a chest roentgenogram. JAMA, 133, 399400.Google Scholar
Editorial (1994). The accuracy of mammographic interpretation. N Engl J Med, 331, 15211522.Google Scholar
Edwards, D.C., Kupinski, M.A., Metz, C.E., et al. (2002). Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys, 29, 28612870.Google Scholar
Egan, J., Greenberg, G., Schulman, A. (1961). Operating characteristics, signal detectability, and the method of free response. J Acoust Soc Am, 33, 9931007.Google Scholar
Elliott, P. (1964) Tables of d ’. In: Swets, J. (ed.) Signal Detection and Recognition by Human Observers. New York: John Wiley.Google Scholar
Elmore, J.G., Wells, C.K., Lee, C.H., et al. (1994). Variability in radiologists’ interpretation of mammograms. N Engl J Med, 331, 14931499.Google Scholar
Engel, F.L. (1971). Visual conspicuity, directed attention and retinal locus. Vision Res, 11, 563567.Google Scholar
Felson, B., Morgan, W.K., Bristol, L.J., et al. (1973). Observations on the results of multiple readings of chest films in coal miners’ pneumoconiosis. Radiology, 109, 1923.Google Scholar
Garland, L.H. (1949). On the scientific evaluation of diagnostic procedures. Radiology, 52, 309328.Google Scholar
Garland, L.H. (1959). Studies on the accuracy of diagnostic procedures. AJR Am J Roentgenol, 82, 2538.Google Scholar
Gitlin, J.N., Cook, L.L., Linton, O.W., et al. (2004). Comparison of “B” readers’ interpretations of chest radiographs for asbestos related changes. Acad Radiol, 11, 843856.Google Scholar
Goddard, P., Leslie, A., Jones, A., et al. (2001). Error in radiology. Br J Radiol, 74, 949951.Google Scholar
Goodenough, D.J., Rossmann, K., Lusted, L.B. (1972). Radiographic applications of signal detection theory. Radiology, 105, 199200.Google Scholar
Goodenough, D.J., Rossmann, K., Lusted, L.B. (1974). Radiographic applications of receiver operating characteristic (ROC) curves. Radiology, 110, 8995.Google Scholar
Green, D.M., Swets, J.A. (1966). Signal Detection Theory and Psychophysics. New York: John Wiley.Google Scholar
Green, D.M., Swets, J.A. (1974). Signal Detection Theory and Psychophysics. Huntington, NY: Krieger.Google Scholar
Gur, D., King, J.L., Rockette, H.E., et al. (1989). Practical issues of experimental ROC analysis. Invest Radiol, 25, 583586.Google Scholar
Hanley, J.A. (1989). Receiver operating characteristic (ROC) methodology: the state of the art. Crit Rev Diagn Im, 29, 307355.Google Scholar
Hanley, J.A., McNeil, B.J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148, 839843.Google Scholar
Henkelman, R.M., Kay, I., Bronskill, M.J. (1990). Receiver operating characteristic (ROC) analysis without truth. Med Decis Making, 10, 2429.Google Scholar
Hillis, S.L., Berbaum, K.S. (2004). Power estimation for the Dorfman–Berbaum-Metz method. Acad Radiol, 11, 12601273.Google Scholar
Hillis, S.L., Obuchowski, N.A., Schartz, K.M., et al. (2005). A comparison of the Dorfman-Berbaum-Metz and Obuchowski-Rockette methods for receiver operating characteristic (ROC) data. Stat Med, 24, 15791607.Google Scholar
Horvath, W., Tolles, W., Bostrom, R. (1956). Quantitative measurements of cell properties on Papanicolaou smears as criteria for screening. In: First International Cancer Cytology Congress. Chicago, IL: American Cancer Society.Google Scholar
Hu, C.H., Kundel, H.L., Nodine, C.F., et al. (1994). Searching for bone fractures: a comparison with pulmonary nodule search. Acad Radiol, 1, 2532.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137144.Google Scholar
Krupinski, E.A., Lund, P.J. (1997). Differences in time to interpretation for evaluation of bone radiographs with monitor and film viewing. Acad Radiol, 4, 177182.Google Scholar
Krupinski, E.A., Nodine, C.F., Kundel, H.L. (1998). Enhancing recognition of lesions in radiographic images using perceptual feedback. Opt Eng, 37, 813818.Google Scholar
Kuhl, D.E., Sanders, T.D., Edwards, R.Q., et al. (1972). Failure to improve observer performance with scan smoothing. J Nucl Med, 13, 752757.Google Scholar
Kundel, H.L. (1979). Images, image quality and observer performance. Radiology, 132, 265271.Google Scholar
Kundel, H. (2006). History of research in medical image perception. J Am Col Radiol, 3, 402408.Google Scholar
Kundel, H.L., LaFollette, P.S. (1972). Visual search patterns and experience with radiological images. Radiology, 103, 523528.Google Scholar
Kundel, H.L., Nodine, C.F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527532.Google Scholar
Kundel, H.L., Polansky, M. (1997). Mixture distribution and receiver operating characteristic analysis of bedside chest imaging using screen-film and computed radiography. Acad Radiol, 4, 17.Google Scholar
Kundel, H.L., Revesz, G. (1976). Lesion conspicuity, structured noise, and film reader error. AJR Am J Roentgenol, 126, 12331238.Google Scholar
Kundel, H.L., Wright, D.J. (1969). The influence of prior knowledge on visual search strategies during the viewing of chest radiographs. Radiology, 93, 315320.Google Scholar
Kundel, H.L., Revesz, G., Stauffer, H.M. (1968). Evaluation of a television image processing system. Invest Radiol, 3, 4450.Google Scholar
Kundel, H.L., Revesz, G., Stauffer, H.M. (1969). The electro-optical processing of radiographic images. Radiol Clin N Am, 7, 447460.Google Scholar
Kundel, H.L., Nodine, C.F., Carmody, D.P. (1978). Visual scanning, pattern recognition, and decision making in pulmonary nodule detection. Invest Radiol, 13, 175181.Google Scholar
Kundel, H.L., Nodine, C.F., Krupinski, E.A. (1989). Searching for lung nodules: visual dwell indicates locations of false-positive and false-negative decisions. Invest Radiol, 24, 472478.Google Scholar
Kundel, H.L., Nodine, C.F., Krupinski, E.A. (1990). Computer displayed eye position as a visual aid to pulmonary nodule interpretation. Invest Radiol, 25, 890896.Google Scholar
Kundel, H.L., Polansky, M., Phelan, M. (2001). Evaluating imaging systems in the absence of truth: a comparison of ROC analysis and mixture distribution analysis using CAD in mammography. Proc Soc Photo-Opt Instrum Eng, 4324, 153158.Google Scholar
Kundel, H.L., Nodine, C.F., Conant, E.F., et al. (2007). Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology, 242, 396402.Google Scholar
Kupinski, M.A., Watson, A.B., Siewerdsen, J.H., et al. (2007). Image quality. J Opt Soc Am A, 24, B198.Google Scholar
Lesgold, A., Rubinson, H., Feltovich, P., et al. (1988). Expertise in a complex skill: Diagnosing X-ray pictures. In: Chi, M., Glaser, R., Farr, M. (eds.) The Nature of Expertise. Hillsdale, NJ: Erlbaum.Google Scholar
Lusted, L.B. (1960). Logical analysis in roentgen diagnosis. Radiology, 74, 178193.Google Scholar
Lusted, L.B. (1968). Introduction to Medical Decision Making. Springfield, IL: Charles C. Thomas.Google Scholar
Lusted, L.B. (1969). Perception of the roentgen image: applications of signal detection theory. Radiol Clin N Am, 7, 435445.Google Scholar
Lusted, L.B. (1978). General problems in medical decision making with comments on ROC analysis. Semin Nucl Med, 8, 299306.Google Scholar
Lusted, L.B. (1984). Editorial: ROC recollection. Med Decis Making, 4, 131134.Google Scholar
Manning, D.J., Gale, A., Krupinski, E.A. (2005). Perception research in medical imaging. Br J Radiol, 78, 683685.Google Scholar
McNeil, B.J., Keeler, E., Adelstein, S.J. (1975). Primer on certain elements of medical decision making. N Engl J Med, 292, 211215.Google Scholar
Mello-Thoms, C., Dunn, S.M., Nodine, C.F., et al. (2003). The perception of breast cancers – a spatial frequency analysis of what differentiates masses from reported cancers. IEEE T Med Imaging, 22, 12971306.Google Scholar
Metz, C.E. (1978). Basic principles of ROC analysis. Semin Nucl Med, 8, 283298.Google Scholar
Metz, C.E. (1989). Some practical issues of experimental design and data analysis in radiographic ROC studies. Invest Radiol, 24, 235245.Google Scholar
Metz, C.E. (2007). ROC analysis in medical imaging: a tutorial review of the literature. Radiol Phys Tech, 1, 212.Google Scholar
Metz, C.E., Goodenough, D.J. (1973). Letter: On failure to improve observer performance with scan smoothing: a rebuttal. J Nucl Med, 14, 873876.Google Scholar
Metz, C.E., Goodenough, D.J., Rossmann, K. (1973). Evaluation of receiver operating characteristic curve data in terms of information theory, with applications in radiography. Radiology, 109, 297303.Google Scholar
Morgan, R.H. (1966). Visual perception in fluoroscopy and radiography. Annual oration in memory of John D. Reeves, Jr., M.D., 1924–1964. Radiology, 86, 403416.Google Scholar
Myers, K.J. (2000). Ideal observer models of visual signal detection. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging. Bellingham, WA: SPIE Press.Google Scholar
Newell, R.R., Chamberlain, W.E., Rigler, L. (1954). Descriptive classification of pulmonary shadows: a revelation of unreliability in the roentgen diagnosis of tuberculosis. Am Rev Tuberc, 69, 566584.Google Scholar
Nodine, C.F., Kundel, H.L., Lauver, S.C., et al. (1996). The nature of expertise in searching mammograms for masses. Proc Soc Photo-Opt Instrum Eng, 2712, 8994.Google Scholar
Norman, G.R., Coblentz, C.L., Brooks, L.R., et al. (1992). Expertise in visual diagnosis: a review of the literature. Acad Med, 67, S78–S83.Google Scholar
Obuchowski, N.A. (2000). Sample size tables for receiver operating characteristic studies. AJR Am J Roentgenol, 175, 603608.Google Scholar
Obuchowski, N. (2005). Fundamentals of clinical research for radiologists, ROC analysis. AJR Am J Roentgenol, 184, 364372.Google Scholar
Obuchowski, N.A., Lieber, M.L., Powell, K.A. (2000). Data analysis for detection and localization of multiple abnormalities with application to mammography. Acad Radiol, 7, 516525.Google Scholar
Oestmann, J.W., Greene, R., Kushner, D.C., et al. (1988). Lung lesions: correlation between viewing time and detection. Radiology, 166, 451453.Google Scholar
Perconti, P., Loew, M.H. (2007). Salience measure for assessing scale-based features in mammograms. J Opt Soc Am A, 24, B81–B90.Google Scholar
Proctor, R.W., Dutta, A. (1995). Perceptual skill/the development of expertise. In: Skill Acquisition and Human Performance. Thousand Oaks, CA: Sage Publications.Google Scholar
Renfrew, D.L., Franken, E.A., Jr., Berbaum, K.S., et al. (1992). Error in radiology: classification and lessons in 182 cases presented at a problem case conference. Radiology, 183, 145150.Google Scholar
Revesz, G. (1985). Conspicuity and uncertainty in the radiographic detection of lesions. Radiology, 154, 625628.Google Scholar
Revesz, G., Kundel, H.L., Graber, M.A. (1974). The influence of structured noise on the detection of radiologic abnormalities. Invest Radiol, 9, 479486.Google Scholar
Revesz, G., Kundel, H.L., Bonitatibus, M. (1983). The effect of verification on the assessment of imaging techniques. Invest Radiol, 18, 194198.Google Scholar
Robinson, P.J.A. (1997). Radiology’s Achilles’ heel: error and variation in the interpretation of the roentgen image. Br J Radiol, 70, 10851098.Google Scholar
Rose, A. (1948). The sensitivity performance of the human eye on an absolute scale. J Opt Soc Am, 38, 196208.Google Scholar
Rossmann, K. (1969). Image quality. Radiol Clin N Am, 7, 419433.Google Scholar
Rossmann, K., Wiley, B.E. (1970). The central problem in the study of radiographic image quality. Radiology, 96, 113118.Google Scholar
Samei, E., Flynn, M.J., Eyler, W. (1999). Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. Radiology, 213, 727734.Google Scholar
Samei, E., Flynn, M.J., Peterson, E., et al. (2003). Subtle lung nodules: influence of local anatomic variations on detection. Radiology, 228, 7684.Google Scholar
Samuel, S., Kundel, H.L., Nodine, C.F., et al. (1995). Mechanism of satisfaction of search: eye position recordings in the reading of chest radiographs. Radiology, 194, 895902.Google Scholar
Schade, O.S. (1964). Modern image evaluation and television (the influence of electronic television on the methods of image evaluation). Appl Optics, 3, 1721.Google Scholar
Smith, M.J. (1967) Error and Variation in Diagnostic Radiology. Springfield, IL: Thomas.Google Scholar
Starr, S.J., Metz, C.E., Lusted, L.B., et al. (1975). Visual detection and localization of radiographic images. Radiology, 116, 533538.Google Scholar
Stigler, S.M. (1968). The History of Statistics. Cambridge, MA: Harvard University Press, pp. 240242.Google Scholar
Swensson, R.G. (1993). Measuring detection and localization performance. In: Barrett, H.H., Gmitro, A.F. (eds.) Information Processing in Medical Imaging. New York, NY: Springer-Verlag.Google Scholar
Swensson, R. (1996). Unified measurement of observer performance in detecting and localizing target objects on images. Med Phys, 23, 17091725.Google Scholar
Swensson, R. (2000). Using localization data from image interpretations to improve estimates of performance accuracy. Med Decis Making, 20, 170185.Google Scholar
Swets, J.A., Pickett, R.M. (1982). Evaluation of Diagnostic Systems. Methods from Signal Detection Theory. New York, NY: Academic Press.Google Scholar
Swets, J.A., Pickett, R.M., Whitehead, S.F., et al. (1979). Assessment of diagnostic technologies. Science, 205, 753759.Google Scholar
Thomas, E.L., Lansdown, E.L. (1963). Visual search patterns of radiologists in training. Radiology, 81, 288291.Google Scholar
Tuddenham, W.J. (1962). Visual search, image organization, and reader error in roentgen diagnosis. Radiology, 78, 694704.Google Scholar
Tuddenham, W.J., Calvert, W.P. (1961). Visual search patterns in roentgen diagnosis. Radiology, 76, 255256.Google Scholar
Wagner, R.F., Brown, D.G. (1985). Unified SNR analysis of medical imaging systems. Phys Med Biol, 30, 489518.Google Scholar
Wood, B.P. (1999). Visual expertise. Radiology, 211, 13.Google Scholar
Yerushalmy, J. (1969). The statistical assessment of the variability in observer perception and description of roentgenographic pulmonary shadows. Radiol Clin N Am, 7, 381392.Google Scholar
Yerushalmy, J., Harkness, J.T., Cope, J.H., et al. (1950). The role of dual reading in mass radiography. Am Rev Tuberc, 61, 443464.Google Scholar
Zhou, X.-H., Obuchowski, N.A., McClish, D.K. (2002). Statistical Methods in Diagnostic Medicine. New York: John Wiley.Google Scholar

References

Barlow, H.B. (1953). Summation and inhibition in the frog’s retina. J Physiol, 119, 6988.Google Scholar
Blackwell, H.R. (1946). Contrast thresholds of the human eye. J Opt Soc Am, 36, 624643.Google Scholar
Campbell, F.W., Robson, J.G. (1968). Application of Fourier analysis to the visibility of gratings. J Physiol, 197, 551566.Google Scholar
DePalma, J.J., Lowry, E.M. (1962). Sine-wave response of the visual system. II. Sine-wave and square-wave contrast sensitivity. J Opt Soc Am, 52, 328335.Google Scholar
Hauske, G., Wolf, W., Lupp, U. (1976). Matched filters in human vision. Biol Cybern, 22, 181188.Google Scholar
Kuffler, S. (1953). Discharge patterns and functional organization of the mammalian retina. J Neurophysiol, 16, 3768.Google Scholar
Schade, O.H. (1956). Optical and photoelectric analog of the eye. J Opt Soc Am, 46, 721739.Google Scholar
Schade, O.H. (1975). Image Quality: A Comparison of Photographic and Television Systems. Princeton, NJ: RCA Laboratories.Google Scholar
Wandell, B. (1995). Foundations of Vision. Sunderland, MA: Sinauer Associates.Google Scholar
Watson, A.B. (2005). A standard model for foveal detection of spatial contrast. J Vision, 5, 717740.Google Scholar

References

Abbey, C.K., Eckstein, M.P. (2000). Derivation of a detectability index for correlated responses in multiple alternative forced-choice experiments. J Opt Soc Am, A17, 21012104.Google Scholar
Abbey, C.K., Eckstein, M.P. (2002). Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments. J Vision, 2, 6678.Google Scholar
Abbey, C.K., Eckstein, M.P. (2006). Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer. J Vision, 6, 335355.Google Scholar
Abbey, C.K., Eckstein, M.P. (2007). Classification images for simple detection and discrimination tasks in correlated noise. J Opt Soc Am, A24, B110–B124.Google Scholar
Abbey, C.K., Eckstein, M.P., Bochud, F.O. (1999). Estimation of human-observer templates for 2 alternative forced choice tasks. Proc SPIE Med Imag, 3663, 284295.Google Scholar
Ahumada, A.J., Beard, B.L. (1997). Image discrimination models predict detection in fixed but not random noise. J Opt Soc Am, A14, 24712478.Google Scholar
Ahumada, A.J., Lovell, J. (1971). Stimulus features in signal detection. J Acoust Soc Am, 49, 17511756.Google Scholar
Barlow, H.B. (1962). A method of determining the overall quantum efficiency of visual discriminations. J Physiol (Lond), 160, 155168.Google Scholar
Barlow, H.B. (1978). The efficiency of detecting changes in density of random dot patterns. Vision Res, 18, 637650.Google Scholar
Barrett, H.H., Swindell, W. (1981). Radiological Imaging: Theory of Image Formation, Detection and Processing. New York, NY: Academic Press.Google Scholar
Barrett, H.H, Yao, J., Rolland, J.P., Myers, K.J. (1993). Model observers for assessment of image quality. Proc Natl Acad Sci USA, 90, 97589765.Google Scholar
Bochud, F.O., Abbey, C.K., Eckstein, M.P. (1999). Further investigation of the effect of phase spectrum on visual detection in structured backgrounds. Proc SPIE Med Imag, 3663, 273281.Google Scholar
Bochud, F.O., Abbey, C.K., Eckstein, M.P. (2000). Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds. J Opt Soc Am, A17, 193205.Google Scholar
Burgess, A.E. (1985). Visual signal detection. III. On Bayesian use of prior knowledge and cross correlation. J Opt Soc Am, A2, 14981507.Google Scholar
Burgess, A.E. (1994). Statistically defined backgrounds: performance of a modified nonprewhitening matched filter model. J Opt Soc Am, A11, 12371242.Google Scholar
Burgess, A.E. (1995). Comparison of receiver operating characteristic and forced choice performance measurement methods. Med Phys, 22, 643655.Google Scholar
Burgess, A.E. (1998). Prewhitening revisited. Proc SPIE Med Imag, 3340, 5564.Google Scholar
Burgess, A.E. (1999a). The Rose model, revisited. J Opt Soc Am, A16, 633646.Google Scholar
Burgess, A.E. (1999b). Visual signal detectability with two-component noise: low-pass filter effects. J Opt Soc Am, A16, 694704.Google Scholar
Burgess, A.E., Barlow, H.B. (1983). The efficiency of numerosity discrimination in random dot images. Vision Res, 23, 811819.Google Scholar
Burgess, A.E., Colborne, B. (1988). Visual signal detection. IV. Observer inconsistency. J Opt Soc Am, A5, 617627.Google Scholar
Burgess, A.E., Ghandeharian, H. (1984a). Visual signal detection. I. Ability to use phase information. J Opt Soc Am, A1, 900905.Google Scholar
Burgess, A.E., Ghandeharian, H. (1984b). Visual signal detection. II. Signal location identification. J Opt Soc Am, A1, 906910.Google Scholar
Burgess, A.E., Judy, P.F. (2007). Signal detection in power-law noise: effect of spectrum exponents. J Opt Soc Am, A24, B52–B60.Google Scholar
Burgess, A.E., Wagner, R.F., Jennings, R.J., Barlow, H.B. (1981). Efficiency of human visual discrimination. Science, 214, 9394.Google Scholar
Burgess, A.E, Li, X., Abbey, C.K. (1997). Visual signal detectability with two noise components: anomalous masking effects. J Opt Soc Am, A14, 24202442.Google Scholar
Burgess, A.E., Jacobson, F.L., Judy, P.F. (2001). Human observer detection experiments with mammograms and power-law noise. Med Phys, 28, 419437.Google Scholar
Carlson, C., Cohen, R. (1980). A simple psychophysical model for predicting the visibility of displayed information. Proc Soc Info Display, 21, 229246.Google Scholar
Daly, S. (1993). The visible differences predictor: an algorithm for the assessment of image fidelity. In: Watson, A.B. (ed.) Digital Images and Human Vision. Cambridge, MA: MIT Press, pp. 179206.Google Scholar
Desolneux, A., Moisan, L., Morel, J.M. (2001). Edge detection by Helmholtz principle. J Math Imaging Vision, 14, 271284.Google Scholar
Eckstein, M.P., Abbey, C.K. (2001). Model observers for signal known statistically tasks. Proc SPIE Med Imag, 4324, 91102.Google Scholar
Eckstein, M.P., Ahumada, A.J., Watson, A.B. (1997). Image discrimination models predict visual detection in natural medical image backgrounds. Proc SPIE Human Vision, Visual Processing, and Digital Display VIII, 3016, 4456.Google Scholar
Eckstein, M.P., Abbey, C.K., Bochud, F.O. (2000a). Practical guide to model observers in synthetic and real noisy backgrounds. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging Vol. I: Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 593628.Google Scholar
Eckstein, M.P., Abbey, C.K., Bochud, F.O. (2000b). Visual signal detection in structured backgrounds. IV. Figures of merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses. J Opt Soc Am, A17, 206217.Google Scholar
Eckstein, M.P., Abbey, C.K., Pham, B.F. (2002). The effect of image compression for model and human observers in signal known statistically tasks. Proc SPIE Med Imag, 4686, 1324.Google Scholar
Eckstein, M.P., Zhang, Y., Pham, B., Abbey, C.K. (2003). Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks. Proc SPIE Med Imag, 5034, 123134.Google Scholar
Eckstein, M.P., Zhang, Y., Pham, B.T. (2004). Metrics of medical image quality: task-based model observers vs. image discrimination/perceptual difference models. Proc SPIE Med Imag, 5372, 4252.Google Scholar
Fiete, R.D., Barrett, H.H., Smith, W.E., Myers, K.J. (1987). Hotelling trace criterion and its correlation with human observer performance. J Opt Soc Am, A4, 945953.Google Scholar
Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Ann Eug, 7, 179188.Google Scholar
Geisler, W.S. (2003). Ideal observer analysis. In: Chalupa, L., Werner, J. (eds.) The Visual Neuro-sciences. Boston, MA: MIT Press, pp. 825837.Google Scholar
Green, D.M., Swets, J.A. (1966). Signal Detection Theory and Psychophysics. New York, NY: John Wiley.Google Scholar
Grosjean, B., Muller, S., Souchay, H. (2006). Lesion detection using an a-contrario detector in simulated digital mammograms. Proc SPIE Med Imag, 6146, 61460S.Google Scholar
Hotelling, H. (1931). The generalization of student’s ratio. Ann Math Stat, 2, 360378.Google Scholar
Ishida, M., Doi, K., Loo, L.-N., Metz, C.E., Lehr, J.L. (1984). Digital image processing: effect on detectability of simulated low-contrast radiographic patterns. Radiology, 150, 569575.Google Scholar
Jackson, W.B., Beebee, P., Jared, D.A., et al. (1996). X-ray image system design using a human visual model. Proc SPIE Med Imag, 2708, 2940.Google Scholar
Jackson, W.B., Said, M.R., Jared, D.A., et al. (1997). Evaluation of human vision models for predicting human-observer performance. Proc SPIE Med Imag, 3036, 6473.Google Scholar
Johnson, J.P, Lubin, J., Nafziger, J.S., Krupinski, E.A., Roehrig, H. (2005). Channelized model observer using a visual discrimination model. Proc SPIE Med Imag, 5749, 199210.Google Scholar
Judy, P.F., Kijewski, M.F., Swensson, R.G. (1997). Observer detection performance loss: target size uncertainty. Proc SPIE Med Imag, 3036, 3947.Google Scholar
Kersten, D.A. (1983). Spatial summation in visual noise. Vision Res, 24, 19771990.Google Scholar
Kersten, D.A. (1986). Statistical efficiency for the detection of visual noise. Vision Res, 27, 10291040.Google Scholar
Knill, D., Field, D., Kersten, D. (1990). Human discrimination of fractal images. J Opt Soc Am, A7, 11131123.Google Scholar
Kotelnikov, V.A. (1959). The Theory of Optimum Noise Immunity. New York, NY: McGraw-Hill.Google Scholar
Lubin, J. (1993). The use of psychophysical data and models in the analysis of display system performance. In: Watson, A.B. (ed.) Digital Images and Human Vision. Cambridge, MA: MIT Press, pp. 163178.Google Scholar
Myers, K.J. (2000). Ideal observer models of visual signal detection. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging Vol I: Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 558592.Google Scholar
Myers, K.J., Barrett, H.H. (1987). Addition of a channel mechanism to the ideal-observer model. J Opt Soc Am, A4, 24472457.Google Scholar
Myers, K.J., Barrett, H.H., Borgstrom, M.C., Patton, D.D., Seeley, G.W. (1985). Effect of noise correlation on detectability of disk signals in medical imaging. J Opt Soc Am, A2, 17521759.Google Scholar
Nafziger, J.S., Johnson, J.P., Lubin, J. (2005). Effects of visual fixation cues on the detectability of simulated breast lesions. Proc SPIE Med Imag, 5749, 566571.Google Scholar
North, D.O. (1943) and (1963). Analysis of the factors which determine signal–noise discrimination in pulsed carrier systems. RCA Tech Rep PTR6C (1943), reprinted in Proc IRE, 51, 10161028.Google Scholar
Pavel, M., Sperling, G., Reidl, T., Vanderbeek, A. (1987). Limits of visual communication: the effect of signal-to-noise ratio on the intelligibility of American Sign Language. J Opt Soc Am, A4, 23552365.Google Scholar
Pelli, D.G. (1981). Effects of visual noise. Doctoral thesis, Cambridge University.Google Scholar
Pelli, D.G. (1985). Uncertainty explains many aspects of visual contrast detection and discrimination. J Opt Soc Am, A2, 15081530.Google Scholar
Peterson, W.W, Birdsall, T.G., Fox, W.C. (1954). The theory of signal detectability. IRE Trans Info Theory, PGIT- 4, 171212.Google Scholar
Pollehn, H., Roehrig, H. (1970). Effect of noise on the MTF of the visual channel. J Opt Soc Am, 60, 842848.Google Scholar
Rolland, J.P., Barrett, H.H. (1992). Effect of random background inhomogeneity on observer detection performance. J Opt Soc Am, A9, 649658.Google Scholar
Rose, A. (1946). A unified approach to the performance of photographic film, television pickup tubes, and the human eye. J Soc Motion Picture Eng, 47, 273294.Google Scholar
Rose, A. (1948). The sensitivity performance of the human eye on an absolute scale. J Opt Soc Am, 38, 196208.Google Scholar
Rose, A. (1953). Quantum and noise limitations of the visual process. J Opt Soc Am, 43, 715716.Google Scholar
Rose, A. (1973). Vision – Human and Electronic. New York, NY: Plenum Press.Google Scholar
Sturm, R.E., Morgan, R.H. (1949). Screen intensification systems and their limitations. Am J Roentgenol, 62, 617634.Google Scholar
Swets, J.A. (1964). Signal Detection and Recognition by Human Observers. New York, NY: John Wiley.Google Scholar
Tanner, W.P., Birdsall, T.G. (1958). Definitions of d and η as psychophysical measures. J Acoust Soc Am, 30, 922928.Google Scholar
Tanner, W.P., Swets, J.A. (1954). A decision-making theory of visual detection. Psychol Rev, 61, 401409.Google Scholar
Tjan, B.S., Legge, G.E., Braje, W.L., Kersten, D. (1995). Human efficiency for recognizing 3-D objects in luminance noise. Vision Res, 35, 30533069.Google Scholar
Wagner, R.F., Weaver, K.E. (1972). An assortment of image quality indices for radiographic film-screen combinations – can they be resolved? Proc SPIE Med Imag, 35, 8394.Google Scholar
Watson, A.B. (1993). DCTune: a technique for visual optimization of DCT quantization matrices for individual images. Soc Info Display Digest, 24, 946949.Google Scholar
Woodward, P.M., Davies, I.L. (1952). Information theory and inverse probability in telecommunications. Proc IEE (Lond), 99 (III), 3744.Google Scholar
Yao, J., Barrett, H.H. (1992). Predicting human performance by a channelized Hotelling observer model. Proc SPIE Med Imag, 1768, 161168.Google Scholar
Zhang, Y., Abbey, C.K., Eckstein, M.P. (2006a). Observer performance detecting signals in globally nonstationary oriented noise. Proc SPIE Med Imag, 6146, 292301.Google Scholar
Zhang, Y., Abbey, C.K., Eckstein, M.P. (2006b). Adaptive mechanisms for visual detection in statistically non-stationary oriented noise. J Opt Soc Am, A23, 15491558.Google Scholar

References

Abbey, C.K., Barrett, H.H. (2001). Human and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. J Opt Soc Am, A18, 473487.Google Scholar
Abbey, C.K., Eckstein, M.P. (2006). Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer. J Vision, 6, 335355.Google Scholar
Abbey, C.K., Eckstein, M.P. (2007). Classification images for simple detection and discrimination tasks in correlated noise. J Opt Soc Am, A24, B110–B124.Google Scholar
Abbey, C.K., Eckstein, M.P., Bochud, F.O. (1999). Estimation of human-observer templates for 2 alternative forced choice tasks. Proc SPIE, 3663, 284295.Google Scholar
Abbey, C.K., Eckstein, M.P., Shimozaki, S.S., et al. (2002). Human observer templates for detection of a simulated lesion in mammo-graphic images. Proc SPIE Med Imag, 4686, 2536.Google Scholar
Aguilar, M., Anguinano, E., Pancorbo, M.A. (1993). Fractal characterization by frequency analysis. II. A new method. J Microscopy, 172, 233238.Google Scholar
Ahumada, A.J., Jr. (1996). Perceptual classification images from Vernier acuity masked by noise. Perception, 25(ECVP ‘96 suppl.), 18.Google Scholar
Anguinano, E., Pancorbo, M.A., Aguilar, M. (1993). Fractal characterization by frequency analysis. I. Surfaces. J Microscopy, 172, 223232.Google Scholar
Barlow, H.B. (1962). A method of determining the overall quantum efficiency of visual discriminations. J Physiol (Lond), 160, 155168.Google Scholar
Barrett, H.H. (1990). Objective assessment of image quality: effects of quantum noise and object variability. J Opt Soc Am, A7, 12661278.Google Scholar
Barrett, H.H., Denny, J.L., Wagner, R.F., Myers, K.J. (1995). Objective assessment of image quality: II. Fisher information, Fourier crosstalk, and figures of merit for task performance. J Opt Soc Am, A12, 834852.Google Scholar
Bath, M., Hakansson, M., Borjesson, S., et al. (2005a). Nodule detection in digital chest radiography: introduction to the RADIUS chest trial. Radiat Prot Dosimetry, 114, 8591.Google Scholar
Bath, M., Hakansson, M., Borjesson, S., et al. (2005b). Nodule detection in digital chest radiography: part of image background acting as pure noise. Radiat Protect Dosimetry, 114, 102108.Google Scholar
Bath, M., Hakansson, M., Borjesson, S., et al. (2005c). Nodule detection in digital chest radiography: effect of anatomical noise. Radiat Protect Dosimetry, 114, 109113.Google Scholar
Bochud, F.O., Verdun, F.R., Hessler, C., Valley, J.F. (1995). Detectability on radiological images: the influence of anatomical noise. Proc SPIE Med Imag, 2436, 156165.Google Scholar
Bochud, F.O., Verdun, F.R., Valley, J.F., Hessler, C., Moeckli, R. (1997). The importance of anatomical noise in mammography. Proc SPIE Med Imag, 3036, 7480.Google Scholar
Bochud, F.O., Valley, J.F., Verdun, F.R., Hessler, C., Schnyder, P. (1999a). Estimation of the noisy component of anatomical backgrounds. Med Phys, 26, 13651370.Google Scholar
Bochud, F.O., Abbey, C.K., Eckstein, M.P. (1999b). Further investigation of the effect of phase spectrum on visual detection in structured backgrounds. Proc SPIE Med Imag, 3663, 273281.Google Scholar
Bochud, F.O., Abbey, C.K., Eckstein, M.P. (2004). Search for lesions in mammograms: non-Gaussian observer response. Med Phys, 31, 2436.Google Scholar
Burgess, A.E. (1985). Detection and identification efficiency: an update. Proc SPIE, 535, 5056.Google Scholar
Burgess, A.E. (1998). Prewhitening revisited. Proc SPIE Med Imag, 3340, 5564.Google Scholar
Burgess, A.E. (2001). Evaluation of detection model performance in power-law noise. Proc SPIE Med Imag, 4324, 123132.Google Scholar
Burgess, A.E. (2005). Effect of detector element size on signal detectability in digital mammography. Proc SPIE Med Imag, 5745, 232242.Google Scholar
Burgess, A.E., Colborne, B. (1988). Visual signal detection. IV. Observer inconsistency. J Opt Soc Am, A5, 617627.Google Scholar
Burgess, A.E., Judy, P.F. (2007). Signal detection in power-law noise: effect of spectrum exponents. J Opt Soc Am, A24, B52–B60.Google Scholar
Burgess, A.E., Jacobson, F.L., Judy, P.F. (2001). Human observer detection experiments with mammograms and power-law noise. Med Phys, 28, 419437.Google Scholar
Burgess, A.E., Jacobson, F.L., Judy, P.F. (2005). Effect of breast tissue density on mass detection. Oral presentation. Presented at Medical Image Perception Society Conference XI, Windermere, UK.Google Scholar
Cargill, E., Barrett, H.H., Fiete, R.D., Kur, M., Patton, D.D. (1988). Fractal physiology and nuclear medicine scans. Proc SPIE, 914, 355361.Google Scholar
Castella, C., Kinkel, K., Verdun, F.R., et al. (2007a). Mass detection on real and synthetic mammograms: human observer templates and local statistics. Proc SPIE Med Imag, 6515, 65150U.Google Scholar
Castella, C., Abbey, C.K., Eckstein, M.P., et al. (2007b). Human linear template with mammographic backgrounds estimated with a genetic algorithm. J Opt Soc Am, A12, B1–B12.Google Scholar
Castella, C., Kinkel, K., Descombes, F., et al. (2008). Mammographic texture synthesis: second generation clustered lumpy backgrounds using a genetic algorithm. Optics Express, 16, 75957607.Google Scholar
Chakraborty, D., Kundel, H.L. (2001). Anomalous results for signal detection in mammograms. Proc SPIE Med Imag, 4324, 6876.Google Scholar
Chawla, A.S., Samei, E., Saunders, R., Abbey, C., Delong, D. (2007).Effect of dose reduction on the detection of mammographic lesions: a mathematical observer model analysis. Med Phys, 34, 33853398.Google Scholar
Cohen, G., DiBianca, F.A. (1979). The use of contrast detail dose evaluation of image quality in a computed tomographic scanner. J Comput Assist Tomogr, 3, 189195.Google Scholar
Coltman, J.W. (1948). Fluoroscopic image brightening by electronic means. Radiology, 51, 359.Google Scholar
Cook, L.T., Cox, C.G., Insana, M.F., et al. (1996). Contrast-detail analysis of the effect of image compression on computed tomographic images. Proc SPIE Med Imag, 2712, 128137.Google Scholar
Cunningham, I.A. (2000). Applied linear systems theory. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging, Vol. 1. Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 79159.Google Scholar
Eckstein, M.P., Whiting, J.S. (1995). Lesion detection in structured noise. Acad Radiol, 3, 249253.Google Scholar
Eckstein, M.P., James, S., Whiting, J.S. (1996). Visual signal detection in structured backgrounds. I. Effect of number of possible spatial locations and signal contrast. J Opt Soc Am, A13, 17771787.Google Scholar
Eckstein, M.P., Ahumada, A.J., Watson, A.B., Whiting, J.S. (1997a).What is degrading human visual detection performance in natural medical image backgrounds? Proc SPIE Med Imag, 3036, 5063.Google Scholar
Eckstein, M.P., Ahumada, A.J., Watson, A.B. (1997b). Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise. J Opt Soc Am, A14, 24062419.Google Scholar
Eckstein, M.P., Abbey, C.K., Whiting, J.S. (1998). Human vs. model observers in anatomic backgrounds. Proc SPIE Med Imag, 3340, 1626.Google Scholar
Gaskill, J.D. (1978). Linear Systems, Fourier Transforms, and Optics. New York, NY: John Wiley.Google Scholar
Good, W.F., Abrams, G.S., Catullo, V.J., et al. (2008). Digital breast tomosynthesis: a pilot observer study. Am J Roentgenol, 190, 865869.Google Scholar
Hakansson, M., Bath, M., Borjesson, S., et al. (2005a). Nodule detection in digital chest radiography: effect of nodule location. Radiat Protect Dosimetry, 114, 9296.Google Scholar
Hakansson, M., Bath, M., Borjesson, S., et al. (2005b). Nodule detection in digital chest radiography: effect of system noise. Radiat Protect Dosimetry, 114, 97101.Google Scholar
Hakansson, M., Bath, M., Borjesson, S., et al. (2005c). Nodule detection in digital chest radiography: summary of the RADIUS chest trial. Radiat Protect Dosimetry, 114, 97101.Google Scholar
Judy, P.F., Swensson, R.G., Szulc, M. (1981). Lesion detection and signal-to-noise ratio in CT images. Med Phys, 8, 323.Google Scholar
Judy, P.F., Swensson, R.G., Nawfel, R.D., Chan, K.H., Seltzer, S.E. (1992). Contrast-detail curves for liver CT. Med Phys, 19, 11671174.Google Scholar
Karssemeijer, N., Frieling, J.T., Hendriks, J.H. (1993). Spatial resolution in digital mammography. Invest Radiol, 28, 413419.Google Scholar
Kaufhold, J., Thomas, J.A., Eberhard, J.W., Galbo, C.E., GonzálezTrotter, D.E. (2002). A calibration approach to glandular tissue composition estimation in digital mammography. Med Phys, 29, 18671880.Google Scholar
Keelan, B.W., Topfer, K., Yorkston, J., Sehnert, W.J., Ellinwood, J.S. (2004). Relative impact of detector noise and anatomical structure on lung nodule detection. Proc SPIE Med Imag, 5372, 230241.Google Scholar
Kundel, H.L., Nodine, C.F., Thickman, D., Toto, L. (1985). Nodule detection with and without a chest film. Invest Radiol, 20, 9499.Google Scholar
Lehmann, L.A., Alvarez, R.E., Macovski, A., et al. (1981). Generalized image combinations in dual KVP digital radiography. Med Phys, 8, 659667.Google Scholar
Lubin, J. (1995). A visual discrimination model for imaging system design and evaluation. In: Peli, E. (ed.) Visual Models for Target Detection and Recognition. Singapore: World Scientific Publishers.Google Scholar
Muka, E., Blame, H., Daly, S. (1995). Display of medical images on CRT soft-copy displays: a tutorial. Proc SPIE Med Imag, 2431, 341359.Google Scholar
Myers, K.J., Barrett, H.H. (1987). Addition of a channel mechanism to the ideal-observer model. J Opt Soc Am, A4, 24472457.Google Scholar
Obuchowski, N.A., Beiden, S.V., Berbaum, K.S., et al. (2004). Multireader, multicase receiver operating characteristic analysis: an empirical comparison of five methods. Acad Radiol, 11, 980995.Google Scholar
Revesz, G., Kundel, H.L., Graber, M.A. (1974). The influence of structured noise on the detectability of radiological abnormalities. Invest Radiol, 9, 479486.Google Scholar
Riederer, S.J., Pelc, N.J., Chesler, D.A. (1978). The noise power spectrum in computed X-ray tomography. Phys Med Biol, 23, 446454.Google Scholar
Ruschin, M., Timberg, P., Svahna, T., et al. (2007). Improved in-plane visibility of tumors using breast tomosynthesis. Proc SPIE Med Imag, 6510, 65101J.Google Scholar
Samei, E., Flynn, M.J., Eyler, W.R. (1999). Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. Radiology, 213, 727734.Google Scholar
Samei, E., Eyler, W., Baron, L. (2000). Effects of anatomical structure on signal detection. In: Beutel, J., Kundel, H.L., Van Metter, R.L (eds.) Handbook of Medical Imaging, Vol. 1. Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 655682.Google Scholar
Samei, E., Saunders, R.S., Baker, J.A., Delong, D.M. (2007). Digital mammography: effects of reduced radiation dose on diagnostic performance. Radiology, 243, 396404.Google Scholar
Saunders, R.S., Baker, J.A., Delong, D.M., Johnson, J.P., Samei, E. (2007). Does image quality matter? Impact of resolution and noise on mammographic task performance. Med Phys, 34, 39713981.Google Scholar
Schade, O.H. (1987). Image quality: a comparison of photographic and television systems. Reprinted in SMPTE J, 100, 567595.Google Scholar
Seltzer, S.E., Judy, P.F., Swensson, R.G., Chan, K.H., Nawfel, R.D. (1994). Flattening of the contrast-detail curve for large lesions on CT liver images. Med Phys, 21, 15471555.Google Scholar
Sharp, P.F., Metz, C.E., Wagner, R.F., Myers, K.J., Burgess, A.E. (1996). ICRU Report 54, Medical Imaging: The Assessment of Image Quality. Bethesda, MD: International Commission on Radiological Units and Measurements.Google Scholar
Smith, S.W., Wagner, R.F., Sandrik, J.M., Lopez, H. (1983). Low-contrast detectability and contrast/detail analysis in medical ultra-sound. IEEE Trans Son Ultrason, SU-30, 164173.Google Scholar
Sturm, R.E., Morgan, R.H. (1949). Screen intensification systems and their limitations. Am J Roentgenol, 62, 617634.Google Scholar
Tapiovaara, M.J., Wagner, R.F. (1993). SNR and noise measurement for medical imaging. I. A practical approach based on statistical decision theory. Phys Med Biol, 3, 7192.Google Scholar
Tischenko, O., Hoeschen, C., Effenberger, O., et al. (2003). Measurement of the noise components in the medical X-ray intensity pattern due to overlaying non-recognizable structures. Proc SPIE Med Imag, 5030, 422432.Google Scholar
Wagner, R.F. (1977). Towards a unified view of radiological imaging systems. Part II: noisy images. Med Phys, 4, 279296.Google Scholar
Wagner, R.F., Brown, D.G. (1985). Unified SNR analysis of medical imaging systems. Phys Med Biol, 30, 498518.Google Scholar
Wagner, R.F., Weaver, K.E. (1972). An assortment of image quality indices for radiographic film-screen combinations – can they be resolved? Proc SPIE, 35, 8394.Google Scholar
Wagner, R.F., Weaver, K.E., Denny, E.W., Bostrum, R.G. (1974).Towards a unified view of radiological imaging systems. Part I: noiseless images. Med Phys, 1, 124.Google Scholar
Wagner, R.F., Brown, D.G., Pastel, M.S. (1979). Application of information theory to the assessment of computed tomography. Med Phys, 6, 8394.Google Scholar
Wagner, R.F., Insana, M.F., Brown, D.G. (1985). Progress in signal and texture discrimination in medical imaging. Proc SPIE, 535, 5764.Google Scholar
Wagner, R.F., Insana, M.F., Brown, D.G., Garra, B.S., Jennings, R.J. (1990). Texture discrimination: radiologist, machine and man. In: Blakemore, C. (ed.) Vision: Coding and Efficiency. London: Cambridge University Press, pp. 310318.Google Scholar
Wagner, R.F., Myers, K.J., Hanson, K.M. (1992). Task performance on constrained reconstructions: human observers compared with suboptimal Bayesian performance. Proc SPIE, 1652, 352362.Google Scholar
Wagner, R.F., Metz, C.E., Campbell, G. (2007). Assessment of medical imaging systems and computer aids: a tutorial review. Acad Radiol, 14, 723748.Google Scholar
Whiting, J.S., Eckstein, M.P., Morioka, C.A., Eigler, N.L. (1996). Effect of additive noise, signal contrast and feature motion on visual detection in structured noise. Proc SPIE Med Imag, 2712, 2638.Google Scholar
Williams, D.B., Siewerdsen, J.H., Tward, D.J., et al. (2007). Optimal kVp selection for dual-energy imaging of the chest: evaluation by task-specific observer preference tests. Med Phys, 34, 39163925.Google Scholar
Zhang, Y., Pham, B.T., Eckstein, M.P. (2004). Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms. IEEE Trans Med Imag, 23, 459474.Google Scholar
Zhang, Y., Pham, B.T., Eckstein, M.P. (2005). Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization. Acad Radiol, 12, 324336.Google Scholar
Zhang, Y., Pham, B.T., Eckstein, M.P. (2007). Evaluation of internal noise methods for Hotelling observer models. Med Phys, 34, 33123322.Google Scholar
Zhou, L., Oldan, J., Fisher, P., Gindi, G. (2006). Low contrast lesion detection in tomosynthetic breast imaging using a realistic breast phantom. Proc SPIE, 6142, 61425A.Google Scholar

References

Albert, M., Maidment, A.D.A. (2000). Linear response theory for detectors consisting of discrete arrays. Med Phys, 27, 24172434.Google Scholar
Barger, A.V., Block, W.F., Toropov, Y., Grist, T.M., Mistretta, C.A. (2002). Time-resolved contrast-enhanced imaging with isotropic resolution and broad coverage using an undersampled 3D projection trajectory. Magn Reson Med, 48, 297305.Google Scholar
Barrett, H.H., Myers, K.J. (2004). Foundations of Image Science. Hoboken, NJ: John Wiley.Google Scholar
Barrett, H.H., Yao, J., Rolland, J.P., Myers, K.J. (1993). Model observers for assessment of image quality. Proc Natl Acad Sci, 90, 97589765.Google Scholar
Barrett, H.H., Denny, J.L., Wagner, R.F., Myers, K.J. (1995). Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance. J Opt Soc Am, A12, 834852.Google Scholar
Barrett, H.H., Wagner, R.F., Myers, K.J. (1997). Correlated point processes in radiological imaging. Proc SPIE Med Imag, 3032, 110124.Google Scholar
Beam, C., Layde, P.M., Sullivan, D.C. (1996). Variability in the interpretation of screening mammograms by US radiologists. Arch Intern Med, 156, 209213.Google Scholar
Beiden, S.V., Wagner, R.F., Campbell, G., Metz, C.E., Jiang, Y. (2001). Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across modalities. Acad Radiol, 8, 605615.Google Scholar
Burgess, A.E. (1999). The Rose model revisited. J Opt Soc Am, A16, 633646.Google Scholar
Burgess, A.E., Shaw, R., Lubin, J. (1999). Noise in imaging systems and human vision. J Opt Soc Am, A16, 618.Google Scholar
Burgess, A.E., Jacobson, F.L., Judy, P.F. (2001). Human observer detection experiments with mammograms and power-law noise. Med Phys, 28, 419437.Google Scholar
Cunningham, I.A., Shaw, R. (1999). Signal-to-noise optimization of medical imaging systems. J Opt Soc Am, A16, 621632.Google Scholar
Dainty, J.C., Shaw, R. (1974). Image Science. New York, NY: Academic Press.Google Scholar
Gagne, R.M., Jafroudi, H., Jennings, R.J., et al. (1996). Digital mammography using storage phosphor plates and a computer-designed X-ray system. In: Doi, K., Giger, M.L., Nishikawa, R.M., Schmidt, R.A. (eds.) Digital Mammography ‘96. Amsterdam, Netherlands: Elsevier, pp. 133138.Google Scholar
International Commission on Radiation Units and Measurements (ICRU). (1996). Report #54. Medical Imaging: The Assessment of Image Quality. Bethesda, MD: International Commission on Radiation Units and Measurements.Google Scholar
Joseph, P.M., Schulz, R.A. (1980). View sampling requirements in fan beam computed tomography. Med Phys, 7, 692702.Google Scholar
Kundel, H.L. (2000). Visual search in medical images. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging. Vol. 1. Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 837858.Google Scholar
Lundqvist, M., Danielsson, M., Cederstrom, B., et al. (2003). Measurements on a full-field digital mammography system with a photon counting crystalline silicon detector. Proc SPIE Med Imag, 5030–5031, 547552.Google Scholar
Metz, C.E., Wagner, R.F., Doi, K., et al. (1995). Toward consensus on quantitative assessment of medical imaging systems. Med Phys, 22, 10571061.Google Scholar
Myers, K.J., Wagner, R.F., Hanson, K.M. (1993). Binary task performance on images reconstructed using MEMSYS 3: comparison of machine and human observers. In: Mohammad-Djafari, A., Demoment, G. (eds.) Maximum Entropy and Bayesian Methods. Dordrecht, Germany: Kluwer Academic, pp. 415421.Google Scholar
Peters, D.C., Grist, T.M., Korosec, F.R., et al. (2000). Undersampled projection reconstruction applied to MR angiography. Magn Reson Med, 43, 91101.Google Scholar
Rabbani, M., Shaw, R., Van Metter, R. (1987). Detective quantum efficiency of imaging systems with amplifying and scattering mechanisms. J Opt Soc Am, A4, 895901.Google Scholar
Rose, A. (1946). A unified approach to the performance of photographic film, television pickup tubes and the human eye. J Soc Motion Pict Eng, 47, 273294.Google Scholar
Rose, A. (1948). The sensitivity performance of the human eye on an absolute scale. J Opt Soc Am, 38, 196208.Google Scholar
Rose, A. (1953). Quantum and noise limitations of the visual process. J Opt Soc Am, 43, 715716.Google Scholar
Rose, A. (1973). Vision – Human and Electronic. New York, NY: Plenum Press.Google Scholar
Rose, A. (1976). The challenge of electronic photography. J Appl Photographic Engineering, 2, 7074.Google Scholar
Samei, E., Eyler, W., Baron, L. (2000). Effects of anatomical structure on signal detection. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging. Vol. 1. Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 655682.Google Scholar
Schade, O.H. (1975). Image Quality: A Comparison of Photographic and Television Systems. Princeton, NJ: RCA Laboratories.Google Scholar
Shaw, R. (2003). End-to-end linearity considerations for photon-limited detection and display systems. Proc SPIE Med Imag, 5030, 414421.Google Scholar
Tapiovaara, M.J., Wagner, R.F. (1985). SNR and DQE analysis of broad spectrum X-ray imaging. Phys Med Biol, 30, 519529.Google Scholar
Tingberg, A., Bath, M., Hakansson, M., et al. (2004). Comparison of two methods for evaluation of image quality of lumbar spine radiographs. Proc SPIE Med Imag, 5372, 251262.Google Scholar
Vigen, K.K., Peters, D.C., Grist, T.M., Block, W.F., Mistretta, C.A. (2000). Undersampled projection-reconstruction imaging for time-resolved contrast-enhanced imaging. Magn Reson Med, 43, 170176.Google Scholar
Wagner, R.F., Brown, D.G. (1985). Unified SNR analysis of medical imaging systems. Phys Med Biol, 30, 489518.Google Scholar
Wagner, R.F., Weaver, K.E. (1972). An assortment of image quality indexes for radiographic film-screen combinations – can they be resolved? Proc SPIE Med Imag, 35, 8394.Google Scholar
Wagner, R.F., Beiden, S.V., Campbell, G., Metz, C.E., Sacks, W.M. (2002). Assessment of medical imaging and computer-assist systems: lessons from recent experience. Acad Radiol, 9, 12641277.Google Scholar
Wagner, R.F., Beiden, S.V., Campbell, G., Metz, C.E., Sacks, W.M. (2003). Contemporary issues for experimental design in assessment of medical imaging and computer-assist systems. Proc SPIE Med Imag, 5034, 213224.Google Scholar
Wagner, R.F., Metz, C.E., Campbell, G. (2007). Assessment of medical imaging systems and computer aids: a tutorial review. Acad Radiol, 14, 723748.Google Scholar

References

Barrett, H.H., Myers, K.J. (2003). Foundations of Image Science. Hoboken, NJ: Wiley.Google Scholar
Barten, P.G.J. (1992). Physical model for the contrast sensitivity of the human eye. Proc SPIE: Human vision, visual processing, and digital display III, 1666, 5772.Google Scholar
Barten, P.G.J. (1999). Contrast Sensitivity of the Human Eye and its Effects on Image Quality. Bellingham, WA: SPIE Press.Google Scholar
Beam, C.A., Krupinski, E.A., Kundel, H.L., Sickles, E.A., Wagner, R.F. (2006). The place of medical image perception in 21st-century health care. J Am Coll Radiol, 3(6), 409412.Google Scholar
Birkelo, C.C., Chamberlain, W.E, Phelps, P.S. (1947). Tuberculosis case finding: comparison of effectiveness of various roentgenographic and photofluorographic methods. JAMA, 133, 359366.Google Scholar
Brook, O.R., O’Connell, A.M., Thornton, E., Eisenberg, R.L., Mendiratta- Lala, M., Kruskal, J.B. (2010). Quality initiatives: anatomy and pathophysiology of errors occurring in clinical radiology practice. RadioGraphics, 30, 14011410.Google Scholar
Chakraborty, D.P. (2013). A brief history of FROC paradigm data analysis. Acad Radiol, 20, 915919.Google Scholar
Chesters, M.S. (1992). Human visual perception and ROC methodology in medical imaging. Phys Med Biol, 37, 14331476.Google Scholar
Dong, L., Chen, Y., Gale, A., Phillips, P. (2016). Eye tracking method compatible with dual-screen mammography workstation. Procedia Comput Sci, 90, 206211.Google Scholar
Donovan, T., Manning, D.J., Crawford, T. (2008). Performance changes in lung nodule detection following perceptual feedback of eye movements. Proc SPIE Med Imag, 6917, 691703.Google Scholar
Evans, K.K., Birdwell, R.L., Wolfe, J.M. (2013). If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PLoS One, 8(5), e64366.Google Scholar
Garland, L.H. (1949). On the scientific evaluation of diagnostic procedures. Radiology, 52, 309328.Google Scholar
Graber, M.L. (2013). The incidence of diagnostic error in medicine. BMJ Qual Saf, Suppl 2, ii21–ii27.Google Scholar
Gur, D., Rockette, H.E., Armfield, D.R., Blachar, A., Bogan, J.K., Brancatelli, G., Britton, C.A., Brown, M.L., Davis, P.L., Ferris, J.V., Fuhrman, C.R., Golla, S.K., Katyal, S., Lacomis, J.M., McCook, B.M., Thaete, F.L., Warfel, T.E. (2003). Prevalence effect in a laboratory environment. Radiology, 228(1), 1014.Google Scholar
Hakim, C.M., Sena, L.C., Degnana, A., Delica, J., Paia, S., Sagreiyaa, H., Sparrowa, M., Thomasa, E., Yannesa, M., Gur, D. (2017). The effect of prevalence of disease on performance of residents and fellows during training for interpreting DBT in a test-train-test observer study. Proc SPIE Med Imag, 10136, 1013603.Google Scholar
Krupinski, E.A. (2010). Current perspectives in medical image perception. Atten Percept Psychophys, 72(5), 12051217.Google Scholar
Krupinski, E.A., Kallergi, M. (2007). Choosing a radiology workstation: technical and clinical considerations. Radiology, 242, 671682.Google Scholar
Krupinski, E.A., Shartz, K., Caldwell, R., Madsen, M., Berbaum, K. (2017). Does fatigue have any impact on satisfaction of search? Proc SPIE Med Imag, 10136, 1013605-1.Google Scholar
Kundel, H.L. (1979). Images, image quality and observer performance: new horizons in radiology lecture. Radiology, 132, 265271.Google Scholar
Kundel, H.L. (2006). History of research in medical image perception. J Am Coll Radiol, 3(6), 402408.Google Scholar
Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E. (2013). Cognitive and factors contributing to diagnostic errors in radiology. AJR, 201, 611617.Google Scholar
Mallet, S., Halligan, S., Thompson, M., Collins, G.S., Altman, D.G. (2012). Interpreting diagnostic accuracy studies for patient care. BMJ, 344, e3999.Google Scholar
Metz, C.E. (1978). Basic principles of ROC analysis. Semin Nucl Med, 8, 283298.Google Scholar
Nakhleh, R.E., Nosé, V., Colasacco, C., Fatheree, L.A., Lillemoe, T.J., McCrory, D.C., et al (2016). Interpretive diagnostic error reduction in surgical pathology and cytology. Guideline from the College of American Pathologists Pathology and Laboratory Quality Center and the Association of Directors of Anatomic and Surgical Pathology. Arch Pathol Lab Med, 140, 2940.Google Scholar
National Cancer Intelligence Network (2011). www.ncin.org.uk/publications/data_briefings/cervical_incidence_and_screening (accessed October 25, 2017).Google Scholar
Rossmann, K., Wiley, B. (1970). The central problem in the study of radiographic image quality. Radiology, 96, 113118.Google Scholar
Royal College of Radiologists. (2006). Standards for the reporting and interpretation of imaging investigations. www.rcr.ac.uk/publication/standards-reporting-and-interpretation-imaging-investigations (accessed October 25, 2017).Google Scholar
Saunders, R.S., Baker, J.A., Delong, D.M., Johnson, J.P., Samei, E. (2007). Does image quality matter? Impact of resolution and noise on mammographic task performance. Med Phys, 34, 39713981.Google Scholar
Szczepura, K.R., Manning, D.J. (2016). Validated novel software to measure the conspicuity index of lesions in DICOM images. Proc SPIE Med Imag, 9787, 978703.Google Scholar
Thompson, J.D., Chakraborty, D.P., Szczepura, K., Tootell, A.K., Vamvakas, I., Manning, D.J., Hogg, P. (2016). Effect of reconstruction methods and X-ray tube current–time product on nodule detection in an anthropomorphic thorax phantom: a crossed-modality JAFROC observer study. Med Phys, 43(3), 12651274.Google Scholar
Toomey, R.J., Ryan, J.T., McEntee, M.F., Evanoff, M.G., Chakraborty, D.P., McNulty, J.P., Manning, D.J., Thomas, E.M., Brennan, P.C. (2009). Diagnostic efficacy of handheld devices for emergency radiologic consultation. AJR, 194, 469474.Google Scholar
Wolfe, J.M., Horowitz, T.S., Van Wert, M.J., Kenner, N.M., Place, S.S., Kibbi, N. (2007). Low target prevalence is a stubborn source of errors in visual search tasks. J Exp Psychol Gen, 136(4), 623638.Google Scholar
World Bank. (2016). http://data.worldbank.org/indicator (accessed October 25, 2017).Google Scholar
Zuley, M. (2010). Perceptual issues in reading mammograms. In: Samei, E., Krupinski, E. (eds.) The Handbook of Medical Image Perception and Techniques. New York, NY: Cambridge University Press, pp. 364379.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×