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Part II - Science of Image Perception

Published online by Cambridge University Press:  20 December 2018

Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
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Print publication year: 2018

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References

Bashshur, R.L., Krupinski, E.A., Weinstein, R.S., Dunn, M.R., Bashshur, N. (2017). The empirical foundations of telepathology: evidence of feasibility and intermediate effects. Telemed e-Health, 23, 155191.Google Scholar
Beebe, H.G., Salles-Cunha, S.X., Scissons, R.P., et al. (1999). Carotid arterial ultrasound scan imaging: a direct approach to stenosis measurement. J Vasc Surg, 29, 838844.Google Scholar
Bertram, R., Helle, L., Kaakinen, J.K., Svedstrom, E. (2013). The effect of expertise on eye movement behavior in medical image perception. PLoS One, 8, e66169.Google Scholar
Bertram, R., Kaakinen, J., Bensch, F., Helle, L., Lantto, E., Niemi, P., Lundbom, N. (2016). Eye movements of radiologists reflect expertise in CT study interpretation: a potential tool to measure resident development. Radiology, 281, 805815.Google Scholar
Bird, R.E., Wallace, T.W., Yankaskas, B.C. (1992). Analysis of cancers missed at screening mammography. Radiology, 184, 613617.Google Scholar
Blaivas, M. (2002). Color Doppler in the diagnosis of ectopic pregnancy in the emergency department: is there anything beyond a mass and fluid? J Emerg Med, 22, 379384.Google Scholar
Blume, H. (1996). Members of ACR/NEMA Working Group XI: the ACR/NEMA proposal for grey-scale display function standard. Proc SPIE Med Imag, 2707, 344360.Google Scholar
Bruckheimer, E., Rotschild, C., Dagan, T., Amir, G., Kaufman, A., Gelman, S., Birk, E. (2016). Computer-generated real-time digital holography: first time use in clinical medical imaging. Eur Heart J Cardiovasc Imag, 17, 845849.CrossRefGoogle ScholarPubMed
Buetti, S., Cronin, D.A., Madison, A.M., Wang, Z., LLeras, A. (2016). Towards a better understanding of parallel visual processing in human vision: evidence for exhaustive analysis of visual information. J Exp Psychol Gen, 145, 672707.Google Scholar
Chaparro, A., Stromeyer, C.F., Huang, E.P., et al. (1993). Colour is what the eyes see best. Nature, 361, 348350.Google Scholar
Chesterman, F., Manssens, H., Morel, C., Serrell, G., Piepers, B., Kimpe, T. (2017). Interpretation of the rainbow color scale for quantitative medical imaging: perceptually linear color calibration (CSDF) versus DICOM GSDF. Proc SPIE Med Imag, 10136, 101360R.Google Scholar
Chi, C.F., Lin, F.T. (1998). A comparison of seven visual fatigue assessment techniques in three data-acquisition VDT tasks. Hum Factors, 40, 577590.Google Scholar
Clarke, E.L., Treanor, D. (2017). Colour in digital pathology: a review. Histopathol, 70, 153163.Google Scholar
Crowley, R.S., Naus, G.J., Stewart, J., et al. (2003). Development of visual diagnostic expertise in pathology: an information-processing study. J Am Med Inform Assoc, 10, 3951.Google Scholar
Czaja, S.J., Sharit, J. (1993). Age differences in the performance of computer-based work. Psychol Aging, 8, 5967.CrossRefGoogle ScholarPubMed
De Faria, J.W.V., Teixeira, M.J., de Moura Sousa, L., Otoch, J.P., Figueiredo, D.E.G. (2016). Virtual and stereoscopic anatomy: when virtual reality meets medical education. J Neurosurg, 125, 11051111.CrossRefGoogle ScholarPubMed
Douglas, D.B., Boone, J.M., Petricoin, E., Liotta, L., Wilson, E. (2016). Augmented reality imaging system: 3D viewing of a breast cancer. J Nat Sci, 2, e215.Google Scholar
Drew, T., Evans, K., Vo, M.L.H., Jacobson, F.L., Wolfe, J.M. (2013). Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images? RadioGraphics, 33, 263274.CrossRefGoogle Scholar
Emre, C.M., Alp, A.Y., Stoecker, W.V., et al. (2007). Unsupervised border detection in dermoscopy images. Skin Res Technol, 13, 454462.Google Scholar
Evanoff, M.G., Roehrig, H., Giffords, R.S., et al. (2001). Calibration of medium-resolution monochrome cathode ray tube displays for the purpose of board examinations. J Digit Imag, 14, 2733.CrossRefGoogle ScholarPubMed
Farahani, N., Post, R., Duboy, J., et al. (2016). Exploring virtual reality technology and the Oculus Rift for the examination of digital pathology slides. J Pathol Inform, 7, 22.CrossRefGoogle ScholarPubMed
Food and Drug Administration. (2017). FDA allows marketing of first whole slide imaging system for digital pathology. www.fda.gov/newsevents/newsroom/pressannouncements/ucm552742.htm (accessed June 3, 2018).Google Scholar
Forrester, J., Dick, A., McMenamin, P., et al. (1996). The Eye: Basic Sciences in Practice. Philadelphia, PA: W.B. Saunders.Google Scholar
Getty, D.J. (2007). Improved accuracy of lesion detection in breast cancer screening with stereoscopic digital mammography. Paper presented at the 93rd Annual Meeting of the Radiological Society of North America, November 25–30, Chicago, IL.Google Scholar
Granger, E.M., Heurtley, J.C. (1973). Visual chromaticity modulation transfer function. J Opt Soc Am, 63, 11731174.Google Scholar
Groth, D.S., Bernatz, S.N., Fetterly, K.A., et al. (2001). Cathode ray tube quality control and acceptance testing program: initial results for clinical PACS displays. Radiographics, 21, 719732.Google Scholar
Haber, R.N. (1969). Information-Processing Approaches to Visual Perception. New York, NY: Holt, Rinehart and Winston.Google Scholar
Hong, L., Burgess, A.E. (1997). Evaluation of signal detection performance with pseudocolor display and lumpy backgrounds. Proc SPIE Med Imag, 3036, 143149.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
Johnston, R.E., Zimmerman, J.B., Rogers, D.C., et al. (1985). Perceptual standardization. Proc SPIE Med Imag, 536, 4449.Google Scholar
Junk, A.K., Haskal, Z., Worgul, B.V. (2004). Cataract in interventional radiology – an occupational hazard? Invest Ophthal Visual Sci, 45, 388.Google Scholar
Kather, J.N., Weidner, A., Attenberger, U., et al. (2017). Color-coded visualization of magnetic resonance imaging multiparametric maps. Sci Rep, 7, 41107.Google Scholar
King, F., Jayender, J., Bhagavatula, S.K., et al. (2016). An immersive virtual reality environment for diagnostic imaging. J Med Robotics Res, 1, 1640003.Google Scholar
Kok, E.M., de Bruin, A.B.H., Leppnik, J., van Merrienboer, J.J.G., Robben, S.G.F. (2015). Case comparisons: an efficient way of learning radiology. Acad Radiol, 22, 12261235.Google Scholar
Komorowski, M., Celi, L.A. (2017). Will artificial intelligence contribute to overuse in healthcare? Crit Care Med, 45, 912913.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137144.Google Scholar
Krupinski, E.A. (2017). Diagnostic accuracy and visual search efficiency: single 8 MP vs. dual 5 MP displays. J Digit Imag, 30, 144147.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., Roehrig, H. (2000). The influence of a perceptually linearized display on observer performance and visual search. Acad Radiol, 7, 813.Google Scholar
Krupinski, E.A., Weinstein, R.S., Rozek, L.S. (1996). Experience-related differences in diagnosis from medical images displayed on monitors. Telemed J, 2, 101108.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
Krupinski, E.A., LeSueur, B., Ellsworth, L., et al. (1999a). Diagnostic accuracy and image quality using a digital camera for teledermatology. Telemed J, 5, 257263.CrossRefGoogle ScholarPubMed
Krupinski, E.A., Roehrig, H., Furukawa, T. (1999b). Influence of film and monitor display luminance on observer performance and visual search. Acad Radiol, 6, 411418.Google Scholar
Krupinski, E., Nypaver, M., Poropatich, R., et al. (2002). Telemedicine/telehealth: an international perspective. Clinical applications in telemedicine/telehealth. Telemed J E Health, 8, 1334.CrossRefGoogle ScholarPubMed
Krupinski, E.A., Tillack, A.A., Richter, L., et al. (2006). Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. Human Pathol, 37, 15431556.Google Scholar
Krupinski, E.A., Chao, J., Hofmann-Wellenhof, R., Morrison, L., Curiel-Lewandrowski, C. (2014). Understanding visual search patterns of dermatologists assessing pigmented skin lesions before and after online training. J Digit Imag, 27, 779785.Google Scholar
Kundel, H.L. (1975). Peripheral vision, structured noise and film reader error. Radiology, 114, 269273.Google Scholar
Kundel, H.L., Nodine, C.F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527532.Google Scholar
Kundel, H.L., Nodine, C.F., Carmody, D.P. (1978). Visual scanning, pattern recognition and decision-making in pulmonary tumor 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., Toto, L. (1991). Searching for lung nodules: the guidance of visual scanning. Invest Radiol, 26, 777781.Google Scholar
Larue, R.T.H.M., Defraene, G., De Ruysscher, D., Lambin, P., van Elmpt, W. (2016). Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol, 90, 20160665.Google Scholar
Lee, J.J., English, J.C. (2018). Teledermatology: a review and update. Am J Clin Dermatol, 19, 253260.Google Scholar
Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E. (2013). Cognitive and system factors contributing to diagnostic errors in radiology. Am J Roentgenol, 201, 611617.Google Scholar
Lee, J.G., Jun, S., Cho, Y.W., Lee, H., Kim, G.B., Seo, J.B., Kim, N. (2017) Deep learning in medical imaging: general overview. Korean J Radiol, 18, 570584.Google Scholar
Levkowitz, H., Herman, G.T. (1992). Color scales for image data. IEEE Comp Graphics and Applic, 12, 7280.Google Scholar
Li, Q., Nishikawa, R.M. (2015). Computer-Aided Detection and Diagnosis in Medical Imaging. New York, NY: CRC Press.Google Scholar
Llewellyn-Thomas, E., Lansdown, E.L. (1963). Visual search patterns of radiologists in training. Radiology, 81, 288291.Google Scholar
Locher, P., Krupinski, E.A., Mello-Thoms, C., Nodine, C.F. (2007). Visual interest in pictorial art during an aesthetic experience. Spat Vis, 21, 5577.Google Scholar
McKoy, K., Antoniotti, N.M., Armstrong, A., et al. (2016). Practice guidelines for teledermatology. Telemed eHealth, 22, 981990.Google Scholar
Mete, M., Xu, X., Fan, C.Y., et al. (2007). Automatic delineation of malignancy in histopathological head and neck slides. BMC Bioinformatics, 8, S17.Google Scholar
Miotto, R., Wang, F., Wang, S., Jiang, X, Dudley, J.T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, bbx044, https://doi.org/10.1093/bib/bbx044.Google Scholar
Moll, T., Douek, P., Finet, G., et al. (1998). Clinical assessment of a new stereoscopic digital angiography system. Cardiovasc Intervent Radiol, 21, 1116.Google Scholar
Muhm, J.R., Miller, W.E., Fontana, R.S., et al. (1983). Lung cancer detection during a screening program using four-month chest radiographs. Radiology, 148, 609615.Google Scholar
Mullen, K.T. (1985). The contrast sensitivity of human colour vision to red-green and blue-yellow chromatic gratings. J Physiol, 359, 381400.Google Scholar
Murakami, S., Verdonschot, R.G., Kreiborg, S., Kakimoto, N., Kawaguchi, A. (2017). Stereoscopy in dental education: an investigation. J Dent Educ, 81, 450457.Google Scholar
Murata, A., Uetake, A., Otsuka, M., et al. (2001). Proposal of an index to evaluate visual fatigue induced during visual display terminal tasks. Int J Hum Comput Interact, 13, 305321.Google Scholar
Mutti, D.O., Zadnik, K. (1996). Is computer use a risk factor for myopia? J Am Optom Assoc, 67, 521530.Google Scholar
Nodine, C.F., Kundel, H.L. (1987). Using eye movements to study visual search and to improve tumor detection. Radiographics, 7, 12411250.CrossRefGoogle ScholarPubMed
Nodine, C.F., Kundel, H.L., Toto, L.C., et al. (1992). Recording and analyzing eye-position data using a microcomputer workstation. Behav Res Methods Instrum Comput, 24, 475485.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., et al. (2002). Time course of perception and decision making during mammographic interpretation. AJR Am J Roentgenol, 179, 917923.Google Scholar
Ogura, A., Kakamura, A., Kaneko, Y., Kitaoka, T., Hayashi, N., Taniguchi, A. (2017). Comparison of grayscale and color-scale renderings of digital medical images for diagnostic interpretation. Radiol Physics Tech, 10, 359363.Google Scholar
OSHA (2017). www.osha.gov/SLTC/computerworkstation/index.html (accessed September 14, 2017).Google Scholar
Parr, L.F., Anderson, A.L., Glennon, B.K., et al. (2001). Quality-control issues on high-resolution diagnostic monitors. J Digit Imag, 14, 2226.Google Scholar
Pizer, S.M. (1981a). Intensity mappings to linearized displays. Comput Graphics Image Process, 17, 262268.Google Scholar
Pizer, S.M. (1981b). Intensity mapping: linearization, image-based, user-controlled. Proc SPIE Med Imag, 271, 2127.Google Scholar
Quaghebeur, G., Bhattacharya, J.J., Murfitt, J. (1997). Radiologists and visual acuity. Eur Radiol, 7, 4143.Google Scholar
Rathi, S., Tsui, E., Mehta, N., Zahid, S., Schuman, J.S. (2017). The current state of teleophthalmology in the United States. Ophthalmology, 124, 17291734.Google Scholar
Rechichi, C., Demoja, C.A., Scullica, L. (1996). Psychology of computer use: XXXVI. Visual discomfort and different types of work at videodisplay terminals. Percept Mot Skills, 82, 935938.Google Scholar
Recht, M., Bryan, R.N. (2017). Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol, 14, 1476–1480.Google Scholar
Rehm, K., Strother, S.C., Anderson, J.R., et al. (1994). Display of merged multimodality brain images using interleaved pixels with independent color scales. J Nucl Med, 35, 18151821.Google Scholar
Reinhold, J., Wen, G., Lo, J.Y., Markey, M.K. (2017). Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: a model observer study with anthropomorphic computational breast phantoms. Proc SPIE Med Imag, 10136, 101360W.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
Rodriguez, J.H., Fraile, F.J.C., Conde, M.J.R., Llorente, P.L.G. (2016). Computer aided detection and diagnosis in medical imaging: a review of clinical and educational applications. Proc TEEM ‘16 Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, 517524.Google Scholar
Rosenbaum, A.E., Huda, W., Lieberman, K.A., et al. (2000). Binocular three-dimensional perception through stereoscopic generation from rotating images. Acad Radiol, 7, 2126.Google Scholar
Saito, K., Hosokawa, T. (1991). Basic study of the VRT (visual reaction time): the effects of illumination and luminance. Int J Hum Comput Interact, 3, 311316.Google Scholar
Sanchez-Roman, F.R., Perez-Lucio, C., Juarez-Ruiz, C., et al. (1996). Risk factors for asthenopia among computer terminal operators. Salud Publica Mex, 38, 189196.Google Scholar
Shen, D., Wu, G., Suk, H.I. (2017). Deep learning in medical image analysis. Annu Rev Med Image Anal, 19, 221248.Google Scholar
Siegal, D., Stratchko, L.M., DeRoo, C. (2017). The role of radiology in diagnostic error: a medical malpractice claims review. Diagnosis, 4, 125131.Google Scholar
Siegel, E.L., Reiner, B.I., Hooper, F., et al. (2001). The effect of monitor image quality on the soft-copy interpretation of chest CR images. Proc SPIE Med Imag, 4323, 4246.Google Scholar
Sotoyama, M., Jonai, H., Saito, S., et al. (1996). Analysis of ocular surface area for comfortable VDT workstation layout. Ergonomics, 39, 877884.Google Scholar
Sowden, P.T., Davies, I.R., Roling, P. (2000). Perceptual learning of the detection of features in X-ray images: a functional role for improvements in adults’ visual sensitivity? J Exp Psychol Hum Percept Perform, 26, 379390.Google Scholar
Spoehr, K.T., Lehmkuhle, S.W. (1982). Visual Information Processing. San Francisco, CA: WH Freeman.Google Scholar
Takahashi, K., Sasaki, H., Saito, T., et al. (2001). Combined effects of working environmental conditions in VDT work. Ergonomics, 44, 562570.Google Scholar
Taylor, G.A. (2017). Perceptual errors in pediatric radiology. Diagnosis, 4, 141147.Google Scholar
Taylor, C.R., Merin, L.M., Salunga, A.M., et al. (2007). Improving diabetic retinopathy screening ratios using telemedicine-based digital retinal imaging technology: the Vine Hill study. Diabetes Care, 30, 574578.Google Scholar
Tuddenham, W.J., Calvert, W.P. (1961). Visual search patterns in roentgen diagnosis. Radiology, 76, 255256.Google Scholar
Turville, K., Psihogios, J., Ulmer, T., et al. (1998). The effects of video display terminal height on the operator: a comparison of the 15” and 40” recommendation. Appl Ergon, 29, 239246.Google Scholar
Tyrrell, R.A., Leibowitz, H.W. (1990). The relation of vergence effort to reports of visual fatigue following prolonged near work. Hum Factors, 32, 341357.Google Scholar
Van der Gijp, A., Ravesloot, C.J., Jarodzka, H., van der Schaaf, M.F., van der Schaik, J.P.J., ten Cate, T.J. (2017). How visual search relates to diagnostic performance: a narrative systematic review of eye-tracking research in radiology. Adv Health Sci Ed, 22, 765787.Google Scholar
Waite, S., Scott, J., Legasto, A., Kolla, S., Gale, B., Krupinski, E.A. (2017a). Systematic error in radiology. Am J Roentgenol, 209, 629639.Google Scholar
Waite, S., Scott, J., Gale, B., Fuchs, T., Kolla, S., Reede, D. (2017b). Interpretive error in radiology. Am J Roentgenol, 208, 739749.Google Scholar
Watten, R.G., Lie, I., Birketvedt, O. (1994). The influence of long-term visual near-work on accommodation and vergence: a field study. J Hum Ergol, 23, 2739.Google Scholar
Weinstein, R.S., Descour, M.R., Liang, C., et al. (2004). An array microscope for ultrarapid virtual slide processing and telepathology. Design, fabrication, and validation study. Hum Pathol, 35, 13031314.Google Scholar
Weinstein, R.S., Graham, A.R., Richter, L.C., et al. (2009). Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum Pathol, 40, 10571069.Google Scholar
Wolfe, J.M., Horowitz, T.S. (2017). Five factors that guide attention in visual search. Nature Hum Behav, 1, 0058.Google Scholar
Yankeelov, T.E., Mankoff, D.A., Schwartz, L.H., et al. (2016). Quantitative imaging in cancer clinical trials. Clin Cancer Res, 22, 284290.Google Scholar
Yunfang, L., Wenjing, W., Bingshuang, H., et al. (2000). Visual strain and working capacity in computer operators. Homeost Health Dis, 40, 2729.Google Scholar

References

Berbaum, K.S., Caldwell, R.T., Schartz, K.M., Thompson, B.H., Franken, E.A. (2007). Does CAD for lung tumors change satisfaction of search in chest radiography? Acad Radiol, 14, 10691076.Google Scholar
Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) (2000). Handbook of Medical Imaging. Bellingham, WA: SPIE Press.Google Scholar
Bracamonte, E., Gibson, B.A., Klein, R., Krupinski, E.A., Weinstein, R.S. (2016). Communicating uncertainty in surgical pathology reports: a survey of staff physicians and residents at an academic medical center. Acad Pathol, 3, 17.Google Scholar
Bryant, G., Norman, G. (1980). Expressions of probability. Words and numbers. N Engl J Med, 302, 411.Google Scholar
Chang, P.J. (1989) Bayesian analysis revisited: a radiologist’s survival guide. AJR, 152, 721727.Google Scholar
Chen, P., Mohan, S., Cook, T., Nasrallah, I., Bryan, R., Botzolakis, E. (2016). Development of a novel Bayesian network interface for radiology diagnosis support and education. Radiological Society of North America 2016 Scientific Assembly and Annual Meeting, November 27 - December 2, 2016, Chicago IL. www.archive.rsna.org/2016/16010460.html (accessed December 1, 2016).Google Scholar
Donovan, T.., Manning, D. (2007). The radiology task: Bayesian theory and perception. Br J Radiol, 80, 389391.Google Scholar
Donovan, T., Manning, D., Phillips, P., Higham, S., Crawford, T. (2005). The role of peripheral vision in a fracture detection task. Paper presented at the Medical Image Perception Society XI Conference, Lake Windermere, England.Google Scholar
Eddy, D. (1982). Probabilistic reasoning in clinical medicine. In: Kahneman, D., Slovic, P., Tversky, A. (eds.) Judgment Under Uncertainty: Heuristics and Biases. Cambridge, England: Cambridge University Press, pp. 231259.Google Scholar
Elkins, J. (1996). The Object Stares Back. On the Nature of Seeing. New York, NY: Harcourt.Google Scholar
Ellis, A., Young, A. (1996). Human Cognitive Neuropsychology: A Textbook with Readings. New York, NY: Psychology Press.Google Scholar
Esserman, L., Cowley, H., Eberle, C., Kirkpatrick, A., Chang, S., Berbaum, K., Gale, A. (2002). Improving the accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst, 94, 369375.Google Scholar
Evans, K.K., Haygood, T.M., Cooper, J., Culpand, A.M., Wolfe, J.M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast. Proc Natl Acad Sci, 113, 1029210297.Google Scholar
Frith, C. (2007). Making up the Mind – How the Brain Creates our Mental World. Oxford, England: Blackwell.Google Scholar
Graber, M.L., Franklin, N., Gordon, R. (2005). Diagnostic error in internal medicine. Arch Intern Med, 165, 14931499.Google Scholar
Gunderman, R. (2009). Biases in radiologic reasoning. AJR, 192, 561564.Google Scholar
Gunderman, R., Williamson, K., Fraley, R., Steele, J. (2001). Expertise: implications for radiological education. Acad Radiol, 8, 12521256.Google Scholar
Herring, W. (2007). Learning Radiology. Recognizing the Basics. Philadelphia, PA: Mosby Elsevier.Google Scholar
Hobby, J., Tom, B., Todd, C., Bearcroft, P., Dixon, A. (2000). Communication of doubt and certainty in radiological reports. Br J Radiol, 73, 9991001.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux.Google Scholar
Kundel, H. (2000). Visual search in medical images. In: Buetel, J., Kundel, H., Van Metter, R. (eds.) Handbook of Medical Imaging. Bellingham, WA: SPIE Press, pp. 129138.Google Scholar
Kundel, H. (2006). History of research in medical image perception. J Am Coll Radiol, 3, 402408.Google Scholar
Kundel, H., Nodine, C. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527532.Google Scholar
Kundel, H., Nodine, C. (1983). A visual concept shapes image perception. Radiology, 146, 363368.Google Scholar
Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E. (2013). Cognitive and system factors contributing to diagnostic errors in radiology. AJR, 201, 611–617.Google Scholar
Lesgold, A.M., Rubinson, H., Feltovitch, P., Glaser, R., Klopfer, D., Wang, Y. (1988). Expertise in a complex skill: diagnosing X-ray pictures. In: Chi, M.T.H., Glaser, R., Farr, M.J. (eds.) The Nature of Expertise. Hillsdale, NJ: Lawrence Erlbaum, pp. 311342.Google Scholar
Litchfield, D., Donovan, T. (2016). Worth a quick look? Initial scene previews can guide eye movements as a function of domain-specific expertise but can also have unforeseen costs. J Exp Psych: Human Percep Perfor, 7, 982994.Google Scholar
Manning, D., Ethell, S., Donovan, T. (2004). Detection or decision errors? Missed lung cancer from the PA chest radiograph. Br J Radiol, 77, 231235.Google Scholar
Manning, D., Gale, A., Krupinski, E. (2005). Perception research in medical imaging. Br J Radiol, 78, 683685.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W.H. Freeman.Google Scholar
Maxwell, C. (1978). Sensitivity and accuracy of the visual analogue scale: a psycho-physics classroom experiment. Br J Clin Pharm, 6, 1524.Google Scholar
McCormack, H., Horne, D., Sheather, S. (1988). Clinical applications of visual analogue scales: a critical review. Psychol Med, 18, 10071019.Google Scholar
Nodine, C., Krupinski, E.A. (1998). Perceptual skill, radiology expertise and visual test performance with NINA and WALDO. Acad Radiol, 5, 603612.Google Scholar
Nodine, C.F., Kundel, H.L. (1987). Using eye movements to study visual search and to improve tumor detection. RadioGraphics, 7, 12411250.Google Scholar
Nodine, C., Mello-Thoms, C. (2000). The nature of expertise in radiology. In: Buetel, J., Kundel, H., Van Metter, R. (eds.) Handbook of Medical Imaging. Bellingham, WA: SPIE Press, pp. 859894.Google Scholar
Philips, P.W. (2010). Eye-tracking the interpretation of CT colonography. PhD thesis, Lancaster University, Lancaster, UK.Google Scholar
Phillips, C. (1995). Logic in Medicine. London, England: BMJ Publishing.Google Scholar
Pinto, A., Brunese, L. (2010). Spectrum of diagnostic errors in radiology. World J Radiol, 2, 377383.Google Scholar
Ramachandran, V. (2003). BBC Reith lectures. The emerging mind. www.bbc.co.uk/radio4/reith2003/ (accessed December 1, 2016).Google Scholar
Ratwani, R.M., Trafton, J.G., Boehm-Davis, D.A. (2008). Thinking graphically: connecting vision and cognition during graph comprehension. J Exp Psychol Appl, 1, 3649.Google Scholar
Robinson, P. (1989). Lung scintigraphy – doubt and uncertainty in the diagnosis of pulmonary embolism. Clin Radiol, 40, 557560.Google Scholar
Robinson, P. (1997). Radiology’s Achilles’ heel: error and variation in the interpretation of the roentgen image. Br J Radiol, 70, 1085–1098.Google Scholar
Samei, E. (2006). Why medical image perception? Editorial. J Am Coll Radiol, 3, 400401.Google Scholar
Smoker, W., Berbaum, K.S., Luebke, N., Jacoby, C. (1984). Spatial perception testing in diagnostic radiology. Am J Radiol, 143, 11051109.Google Scholar
Suleiman, W.I., Lewis, S.J., Georgian-Smith, D., Evanoff, M.G., McEntee, M.F. (2014). Number of mammography cases read per year is a strong predictor of sensitivity. J Med Imag, 1, 015503.Google Scholar
Swenson, R., Hessel, S., Herman, P. (1985). The value of searching films without specific preconceptions. Invest Radiol, 20, 100107.Google Scholar
Van Der Helm, P.A. (2000). Simplicity versus likelihood in visual perception: from surprisals to precisals. Psych Bull, 126, 770800.Google Scholar
Weaver, S.J., Newman-Toker, D.E., Rosen, M.A. (2010). Cognitive skill decay and diagnostic error: best practices for continuing education in healthcare. J Contin Educ Health Prof, 30, 208220.Google Scholar
Wood, B. (1999). Decision making in radiology. Radiology, 211, 601603.Google Scholar

References

Agresti, A. (1990). Categorical Data Analysis. New York: John Wiley, pp. 244–245, 249250.Google Scholar
Anbari, M.M., West, O.C. (1997). Cervical spine trauma radiography: sources of false-negative diagnoses. Emerg Radiol, 4, 218224.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, 409412.Google Scholar
Berbaum, K.S., Franken, E.A., Jr. (2006). Commentary: does clinical history affect perception? Acad Radiol, 13, 402403.Google Scholar
Berbaum, K.S., Schartz, K.M. (2013). One parameter contaminated binormal model (CBM) for analysis of difficult-to-fit ROC data. Proc SPIE, 8673, 86730C.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., et al. (1986). Tentative diagnoses facilitate the detection of diverse lesions in chest radiographs. Invest Radiol, 21, 532539.Google Scholar
Berbaum, K.S., El-Khoury, G.Y., Franken, E.A., Jr., et al. (1988a). Impact of clinical history on fracture detection with radiography. Radiology, 168, 507511.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., Barloon, T.J. (1988b). Influence of clinical history upon detection of nodules and other lesions. Invest Radiol, 23, 4855.Google Scholar
Berbaum, K.S., Dorfman, D.D., Franken, E.A., Jr. (1989a). Measuring observer performance by ROC analysis: indications and complications. Invest Radiol, 24, 228233.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., El-Khoury, G.Y. (1989b). Impact of clinical history on radiographic detection of fractures: a comparison of radiologists and orthopedists. Am J Roentgenol, 153, 12211224.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., Franken, E.A., Jr., Anderson, K.L., et al. (1993). The influence of clinical history on visual search with single and multiple abnormalities. Invest Radiol, 28, 191201.Google Scholar
Berbaum, K.S., El-Khoury, G.Y., Franken, E.A., Jr., et al. (1994a). Missed fractures resulting from satisfaction of search effect. Emerg Radiol, 1, 242249.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., Lueben, K.R. (1994b). Influence of clinical history on perception of abnormalities in pediatric radiographs. Acad Radiol, 1, 217223.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., et al. (1996). The cause of satisfaction of search effects in contrast studies of the abdomen. Acad Radiol, 3, 815826.Google Scholar
Berbaum, K.S., Franken, E.A. Jr., Dorfman, D.D., et al. (1998). Role of faulty visual search in the satisfaction of search effect in chest radiology. Acad Radiol, 5, 919.Google Scholar
Berbaum, K.S., Dorfman, D.D., Franken, E.A., Jr., Caldwell, R.T. (2000a). Proper ROC analysis and joint ROC analysis of the satisfaction of search effect in chest radiography. Acad Radiol, 7, 945958.Google Scholar
Berbaum, K.S., Franken, E.A. Jr., Dorfman, D.D., Caldwell, R.T., Krupinski, E.A. (2000b). 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., Jr., et al. (2001). Gaze dwell times on acute trauma injuries missed because of satisfaction of search. Acad Radiol, 8, 304314.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Dorfman, D.D., Caldwell, R.T., Lu, C.H. (2005). Can order of report prevent satisfaction of search in abdominal contrast studies? Acad Radiol, 12, 7484.Google Scholar
Berbaum, K.S., Franken, E.A., Jr., Caldwell, R.T., Schartz, K.M. (2006). Can a checklist reduce SOS errors in chest radiography? Acad Radiol, 13, 296304.Google Scholar
Berbaum, K.S., El-Khoury, G.Y., Ohashi, K., et al. (2007a). Satisfaction of search in multi-trauma patients: severity of detected fractures. Acad Radiol, 14, 711722.Google Scholar
Berbaum, K.S., Caldwell, R.T., Schartz, K.M., Thompson, B.H., Franken, E.A., Jr. (2007b). Does computer-aided diagnosis for lung tumors change satisfaction of search in chest radiography? Acad Radiol, 14, 10691076.Google Scholar
Berbaum, K.S., Schartz, K.M., Caldwell, R.T., et al. (2012). Satisfaction of search for subtle skeletal fractures may not be induced by more serious skeletal injury. J Am Coll Radiol, 9, 344351.Google Scholar
Berbaum, K.S., Schartz, K.M., Caldwell, R.T., et al. (2013). Satisfaction of search from detection of pulmonary nodules in computed tomography of the chest. Acad Radiol, 20, 194201.Google Scholar
Berbaum, K.S., Krupinski, E.A., Schartz, K.M., et al. (2015). Satisfaction of search in chest radiography 2015. Acad Radiol, 22, 14571465.Google Scholar
Berbaum, K.S., Krupinski, E.A., Schartz, K.M., et al. (2016). The influence of a vocalized checklist on detection of multiple abnormalities in chest radiography. Acad Radiol, 23, 413420.Google Scholar
Berlin, L. (1996). Malpractice issues in radiology: perceptual errors. Am J Roentgenol, 167, 587590.Google Scholar
BMDP2V release: 8.0. (1993). BMDP Statistical Software, Inc. Statistical Solutions, Cork, Ireland (www.statsol.ie).Google Scholar
Bruning, J.L., Kintz, B.L. (1987). Computational Handbook of Statistics, 3rd edn. Glenview, IL: Harper Collins, pp. 272275.Google Scholar
Chakraborty, D.P., Winter, L.H.L. (1990). Free-response methodology: alternative analysis and a new observer-performance experiment. Radiology, 174, 873881.Google Scholar
Christensen, E.E., Murry, R.C., Holland, K., et al. (1981). The effect of search time on perception. Radiology, 138, 361365.Google Scholar
Craik, K.J.W. (1943). The Nature of Explanation. London: Cambridge University Press, p. 81.Google Scholar
Dixon, W.J. (1992). BMDP Statistical Software Manual, Vol 1. Berkeley, CA: University of California Press, pp. 155174; 201227; 521564.Google Scholar
Dorfman, D.D., Alf, E., Jr. (1969). Maximum likelihood estimation of parameters of signal detection theory and determination of confidence intervals – rating method data. J Math Psychol, 6, 487496.Google Scholar
Dorfman, D.D., Berbaum, K.S. (1986). RSCORE-J: pooled rating method data: a computer program for analyzing pooled ROC curves. Behav Res Methods Instrument, 18, 452462.Google Scholar
Dorfman, D.D., Berbaum, K.S. (1995). Degeneracy and discrete ROC rating data. Acad Radiol, 2, 907915.Google Scholar
Dorfman, D.D., Berbaum, K.S. (2000a). A contaminated binormal model for ROC data. II. A formal model. Acad Radiol, 7, 427437.Google Scholar
Dorfman, D.D., Berbaum, K.S. (2000b). A contaminated binormal model for ROC data. III. Initial evaluation with detection ROC data. Acad Radiol, 7, 438447.Google Scholar
Dorfman, D.D., Berbaum, K.S., Metz, C.E. (1992). Receiver operating characteristic rating analysis: generalization to the population of readers and patients with the jackknife method. Invest Radiol, 27, 723731.Google Scholar
Dorfman, D.D., Berbaum, K.S., Metz, C.E., et al. (1997). Proper receiver operating characteristic analysis: the bigamma model. Acad Radiol, 4, 138149.Google Scholar
Ericcson, K.A., Simon, H.A. (1980). Verbal reports as data. Psychol Rev, 87, 215251.Google Scholar
Ericcson, K.A., Simon, H.A. (1984). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.Google Scholar
Fidler, J.L., Fletcher, J.G., Johnson, C.D., et al. (2004). Understanding interpretive errors in radiologists learning computed tomography colonography. Acad Radiol, 11, 750756.Google Scholar
Franken, E.A., Jr., Berbaum, K.S., Lu, C.H., et al. (1994). Satisfaction of search in detection of plain film abnormalities in abdominal contrast examinations. Invest Radiol, 29, 403409.Google Scholar
Gale, A.G., Worthington, B.S. (1983). The utility of scanning strategies in radiology. In: Eye Movements and Psychological Functions: International Views. Hillsdale, NJ: Lawrence Erlbaum, pp. 169191.Google Scholar
Gale, A.G., Johnson, F., Worthington, B.S. (1979). Psychology and radiology. In: Research in Psychology and Medicine, Vol 1. London: Academic Press.Google Scholar
Green, D.M., Swets, J.A. (1962). Signal Detection Theory and Psychophysics. New York, NY: John Wiley, pp. 86116.Google Scholar
Halsted, M.J., Kumar, H., Paquin, J.J., et al. (2004). Diagnostic errors by radiology residents in interpreting pediatric radiographs in an emergency setting. Pediatr Radiol, 34, 331336.Google Scholar
Hillis, S.L., Berbaum, K.S., Metz, C.E. (2008). Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Acad Radiol, 15, 647661.Google Scholar
Hochberg, J. (1970). Attention, organization and consciousness. In: Attention: Contemporary Theory and Analysis. New York, NY: Appleton-Century Crofts.Google Scholar
Johnson, N.L., Kotz, S. (1970). Continuous Univariate Distributions. 1: Distributions in Statistics. New York, NY: John Wiley, pp. 186187.Google Scholar
Kinard, R.E., Orrison, W.W., Brogdon, B.G. (1986). The value of a worksheet in reporting body-CT examinations. Am J Roentgenol, 147, 848849.Google Scholar
Kirk, R.E. (1982). Experimental Design, 2nd edn. Belmont, CA: Wadsworth, pp. 75; 429455; 531548.Google Scholar
Krupinski, E.A., Berbaum, K.S., Schartz, K.M., Caldwell, R.T., Madsen, M.T. (2017a). The impact of fatigue on satisfaction of search in chest radiography. Acad Radiol, 24, 10581063.Google Scholar
Krupinski, E.A., Schartz, K.M., Van Tassell, M.S., Madsen, M.T., Caldwell, R.T., Berbaum, K.S. (2017b). Effect of fatigue on reading computed tomography examination of the multiply injured patient. J Med Imag, 4, 035504.Google Scholar
Kuhn, G.J. (2002). Diagnostic errors. Acad Emerg Med, 9, 740750.Google Scholar
Kundel, H.L. (2006). History of research in medical image perception. J Am Coll 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., Carmody, D. (1978). Visual scanning, pattern recognition and decision making in pulmonary nodule detection. Invest Radiol, 13, 175181.Google Scholar
Kundel, H.L., Nodine, C.F., Thickman, D., Toto, L. (1987). Searching for lung nodules: a comparison of human performance with random and systematic scanning models. Invest Radiol, 22, 417422.Google Scholar
Lev, M.H., Rhea, J.T., Bramson, R.T. (1999). Avoidance of variability and error in radiology. Lancet, 354, 272.Google Scholar
MacMahon, H., Engelmann, R., Behlen, F.M., et al. (1999). Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. Radiology, 213, 723726.Google Scholar
Madsen, M.T., Berbaum, K.S., Ellingson, A.N., Thompson, B.H., Mullan, B.F., Caldwell, R.T. (2006). A new software tool for removing, storing, and adding abnormalities to medical images for perception research studies. Acad Radiol, 13, 305312.Google Scholar
McNemar, Q. (1969). Psychological Statistics, 4th edn. New York, NY: Wiley, pp. 5458.Google Scholar
Metz, C.E. (1987). Current problems in ROC analysis. In: Proceedings of the Chest Imaging Conference 1987. Madison, WI: University of Wisconsin, pp. 315336.Google Scholar
Most, S.B., Scholl, B.J., Clifford, E.R., Simons, D.J. (2005). What you see is what you set: sustained inattentional blindness and the capture of awareness. Psychol Rev, 112, 217242.Google Scholar
Nakamura, K., Yoshida, H., Engelmann, R., et al. (2000). Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology, 214, 823830.Google Scholar
Neisser, U. (1976). Cognition and Reality. San Francisco, CA: W.H. Freeman.Google Scholar
Newell, A., Simon, H.A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Nodine, C.F., Kundel, H.L. (1987). Using eye movements to study visual search and to improve tumor detection. RadioGraphics, 7, 12411250.Google Scholar
Nodine, C.F., Mello-Thoms, C., Weinstein, S.P., et al. (2001). Blinded review of retrospectively visible unreported breast cancers: an eye-position analysis. Radiology, 221, 122129.Google Scholar
Nuñez, D.B. Jr., Quencer, R.M. (1998). The role of helical CT in the assessment of cervical spine injuries. Am J Roentgenol, 171, 951957.Google Scholar
Pesce, L.L., Metz, C.E. (2007). Reliable and computationally efficient maximum-likelihood estimation of “proper” binormal ROC curves. Acad Radiol, 14, 814829.Google Scholar
Rasband, W.S. (1997–2006). ImageJ. Bethesda, MD: US National Institutes of Health.Google Scholar
Renfrew, R.L., Franken, E.A., Berbaum, K.S., Weigelt, F.H., AbuYousef, M.M. (1992). Error in radiology: classification and lessons in 182 cases presented at a problem case conference. Radiology, 183, 145150.Google Scholar
Rogers, LF. (1982). Radiology of Skeletal Trauma. New York, NY: Churchill-Livingstone, p. 1.Google Scholar
Rogers, L.F. (1984). Common oversights in the evaluation of the patient with multiple injuries. Skelet Radiol, 12, 103111.Google Scholar
Rogers, L.F., Hendrix, R.W. (1990). Evaluating the multiply injured patient radiologically. Orthop Clin North Am, 21, 437447.Google Scholar
Samei, E. (2006). Why medical image perception? J Am Coll Radiol, 3, 400401.Google Scholar
Samuel, S., Kundel, H.L., Nodine, C.F., Toto, L.C. (1995). Mechanism of satisfaction of search: eye position recordings in the reading of chest radiographs. Radiology, 94, 895902.Google Scholar
Samuelson, F. (2011). The single-parameter power law for modeling data from observer experiments in medical imaging. Medical Image Perception Conference XIV, Dublin, Ireland.Google Scholar
Schartz, K.M., Berbaum, K.S., Caldwell, R.T., Madsen, M.T. (2009). WorkstationJ as ImageJ plugin for medical image studies. Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM) – 9th Annual SIIM Research and Development Symposium, Charlotte, NC, June 6, 2009. www.siimweb.org/assets/FCBE219A-C30B-4003-9892-FACA9230AB91.pdf.Google Scholar
Schartz, K.M., Berbaum, K.M., Madsen, M.T., et al. (2013). Multiple diagnostic task performance in CT examination of the chest. Br J Radiol, 86, 20110799, Erratum in: Br J Radiol, 86, 20139001.Google Scholar
Schartz, K.M., Madsen, M.T., Kim, J., et al. (2016). Trauma in CT: the role of severe injury on satisfaction of search revised. J Am Coll Radiol, 13, 973978.Google Scholar
Simon, H.A. (1971). Designing organizations for an information-rich world. In: Computers, Communications, and the Public Interest. Baltimore, MD: Johns Hopkins Press, pp. 3752.Google Scholar
Sistrom, C. (2006). Radiology checklists, satisfaction of search, and the talking template concept. Acad Radiol, 13, 922923.Google Scholar
Sistrom, C.L., Langlotz, C.P. (2005) A framework for improved radiology reporting. J Am Coll Radiol, 2, 6167.Google Scholar
Smith, M.J. (1967). Error and Variation in Diagnostic Radiology. Springfield, IL: Charles C. Thomas, p. 27.Google Scholar
Snedecor, G.W., Cochran, W.G. (1989). Statistical Methods, 8th edn. Ames, IA: Iowa State University Press, pp. 146147.Google Scholar
Swensson, R.G. (1988). The effects of clinical information on film interpretation: another perspective. Invest Radiol, 23, 5661.Google Scholar
Swensson, R.G. (1996). Unified measurement of observer performance in detecting and localizing target objects on images. Med Phys, 23, 17091725.Google Scholar
Swensson, R.G., Theodore, G.H. (1990). Search and nonsearch protocols for radiographic consultation. Radiology, 177, 851856.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1977). Omissions in radiology: faulty search or stringent reporting criteria? Radiology, 123, 563567.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1979). Detection performance and the nature of the radiologist’s search task. In Symposium on the Optimization of Chest Radiography (Bureau of Radiological Health). Washington, DC: US Government Printing Office.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1982). Radiographic interpretation with and without search: visual search aids the recognition of chest pathology. Invest Radiol, 17, 145151.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1985). The value of searching films without specific preconceptions. Invest Radiol, 20, 100114.Google Scholar
Swensson, R.G., King, J.L., Gur, D. (2001). A constrained formulation for the receiver operating characteristic (ROC) curve based on probability summation. Med Phys, 28, 15971609.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
Tuddenham, W.J. (1962). Visual search, image organization, and reader error in roentgen diagnosis: studies of the psychophysiology of roentgen image perception. Radiology, 78, 694704.Google Scholar
Tuddenham, W.J. (1963). Problems of perception in chest roentgenology: facts and fallacies. Radiol Clin N Am, 1, 227–289.Google Scholar
von Helmholtz, H. (1867, 1963). Handbook of Physiological Optics. New York, NY: Dover.Google Scholar
Voytovich, A., Rippey, R., Suffredini, A. (1985). Premature conclusions in diagnostic reasoning. J Med Educ, 60, 302307.Google Scholar
Xu, X.W., Doi, K., Kobayashi, T., MacMahon, H., Giger, M.L. (1997). Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Med Phys, 24, 13951403.Google Scholar

References

Ahissar, M., Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci, 8, 457464.Google Scholar
Anderson, J.R. (1995). Cognitive Psychology and its Implications, 4th ed. New York: W.H. Freeman.Google Scholar
Antes, J.R., Penland, J.G. (1981). Picture context effects on eye movement patterns. In: Fisher, D.F., Monty, R.A., Senders, J.W. (eds.) Eye Movements: Cognition and Visual Perception. Hillsdale, NJ: Lawrence Erlbaum, pp. 157170.Google Scholar
Baghdady, M., Carnahan, H., Lam, E.W.N., Woods, N.N. (2014). Dental and dental hygiene students diagnostic accuracy in oral radiology: effect of diagnostic strategy and instructional method. J Dent Educ, 78, 12791285.Google Scholar
Bar, M., Kassam, K.S., Ghuman, A.S., et al. (2006). Top-down facilitation of visual recognition. Proc Natl Acad Sci, 103, 449454.Google Scholar
Barlow, W.E., Chi, C., Carney, P.A., et al. (2004). Accuracy of screening mammography interpretation by characteristics of radiologists. J Natl Cancer Inst, 96, 18401850.Google Scholar
Bass, J.C., Chiles, C. (1990). Visual skill: correlation with detection of solitary pulmonary nodules. Invest Radiol, 25, 994998.Google Scholar
Beam, C.A., Conant, E.F., Sickles, E.A. (2003). Association of volume and volume-independent factors with accuracy in screening mammogram interpretation. J Natl Cancer Inst, 95, 282290.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
Berlin, L. (2003). Malpractices issues in radiology – breast cancer, mammography, and malpractice litigation: the controversies continue. Am J Roentgenol, 180, 12291237.Google Scholar
Beutel, J., Van Metter, R., Kundel, H (eds.) (2001). Handbook of Medical Imaging. Vol. 1: Physics and Psychophysics. Bellingham, WA: SPIE Press.Google Scholar
Brennan, P.C., Trieu, P.D., Tapia, K., Ryan, J., Mello-Thoms, C., Lee, W. (2014) BREAST: a novel strategy to improve the detection of breast cancer. Proc 12th Int Workshop on Breast Imaging. Springer LNCS: 8539.Google Scholar
Carpenter, G.A., Grossberg, S. (1993). Normal and amnesic learning: recognition memory by a neural model of cortico-hyppocampal interactions. Trends Neurosci, 16, 131137.Google Scholar
Carpenter, G.A., Grossberg, S., Lesher, G.W. (1998). The what-and-where filter. A spatial mapping neural network for object recognition and image understanding. Comp Vis Image Underst, 69, 122.Google Scholar
Cave, K.R., Batty, M.J. (2006). From searching for features to searching for threat: drawing the boundary between preattentive and attentive vision. Vis Cogn, 14, 629646.Google Scholar
Charlin, B. (1998). Script questionnaires: their use for assessing diagnostic knowledge in radiology. Med Teach, 20, 567571.Google Scholar
Charness, N., Krampe, R., Mayr, U. (1996). The role of practice and coaching in entrepreneurial skill domains: an international comparison of life-span chess skill acquisition. In: Ericsson, K.A. (ed.) The Road to Excellence. Mahwah, NJ: Erlbaum, pp. 5180.Google Scholar
Charness, N., Reingold, E.M., Pomplun, M., et al. (2001). The perceptual aspects of skilled performance in chess: evidence from eye movements. Mem Cognit, 29, 11461152.Google Scholar
Chase, W.G., Simon, H. (1973a). The mind’s eye in chess. In: Chase, W.G. (ed.) Visual Information Processing. New York, NY: Academic Press, pp. 215281.Google Scholar
Chase, W.G., Simon, H.A. (1973b). Perception in chess. Cognit Psychol, 4, 86112.Google Scholar
Chi, M.T.H., Glaser, R., Rees, E. (1982). Expertise in problem solving. In: Sternberg, R. (ed.). Advances in the Psychology of Human Intelligence. Hillsdale, NJ: Lawrence Erlbaum, pp. 1776.Google Scholar
Christensen, E.E., Murry, R.C., Holland, K., et al. (1981). The effect of search time on perception. Radiology, 138, 361365.Google Scholar
Croskerry, P. (2009a). A universal model of diagnostic reasoning. Acad Med, 84, 12221228.Google Scholar
Croskerry, P. (2009b). Clinical cognition and diagnostic error: applications of a dual processing model of reasoning. Adv Health Sci Educ, 14, 2735.Google Scholar
Croskerry, P., Petrie, D.A., Reilly, J.B., Tait, G. (2014). Deciding about fast and slow decisions. Acad Med, 89, 197200.Google Scholar
Crowley, R.S., Naus, G.J., Steward, J. III, Friedman, C.P. (2003). Development of visual diagnostic expertise in pathology: an information-processing study. J Am Med Inform Assoc, 10, 3951.Google Scholar
Custers, E.J.F.M. (2015). Thirty years of illness scripts: theoretical origins and practical applications. Med Teach, 37, 457462.Google Scholar
de Groot, A.D. (1978). Thought and Choice in Chess. The Hague, The Netherlands: Mouton.Google Scholar
de Groot, A.D., Gobet, F. (1996). Perception and Memory in Chess. Assen, The Netherlands: Van Gorum.Google Scholar
Donovan, T., Litchfield, D. (2013). Looking for cancer: expertise related differences in searching and decision making. Appl Cognit Psychol, 27, 4349.Google Scholar
Drew, T., Vo, M.L.H., Olwal, A., Jacobson, F., Seltzer, S.E., Wolfe, J.M. (2013). Scanners and drillers: characterizing expert visual search through volumetric images. J Vision, 13, 113.Google Scholar
Dreyfus, H.L., Dreyfus, S.E. (1986). Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York, NY: The Free Press.Google Scholar
Dulaney, C.L., Marks, W. (2007). The effects of training and transfer on global/local processing. Acta Psychol, 125, 203220.Google Scholar
Ericsson, K.A. (1996). The acquisition of expert performance. In: Ericsson, K.A. (ed.) The Road to Excellence. Mahwah, NJ: Lawrence Erlbaum, pp. 150.Google Scholar
Ericsson, K.A., Kintsch, W. (1995). Long term working memory. Psychol Rev, 102, 211245.Google Scholar
Ericsson, K.A., Charness, N., Feltovich, P.J., et al. (2006). The Cambridge Handbook of Expertise and Expert Performance. New York, NY: Cambridge University Press.Google Scholar
Esserman, L., Cowley, H., Carey, E., et al. (2002). Improving accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst, 94, 369375.Google Scholar
Estes, W.K. (1994). Classification and Cognition. New York, NY: Oxford University Press.Google Scholar
Evans, K.K., Haygood, T.M., Cooper, J., Culpan, A.-M., Wolfe, J.M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases when viewing the mammogram of the opposite breast. Proc Natl Acad Sci, 113, 1029210297.Google Scholar
Evered, A., Walker, D., Watt, A.A., Perham, N. (2014). Untutored discrimination training on paired cell images influences visual learning in cytopathology. Cancer Cytopathol, 122, 200210.Google Scholar
Feltovitch, P.J., Barrows, H.S. (1984). Issues of generality in medical problem solving. In: Schmidt, H.G., de Volder, M.L. (eds.) Tutorials in Problem Based Learning. Assen/Maastrich, The Nertherlands: Van Gorcum, pp. 128142.Google Scholar
Fenton, J.J., Taplin, S.H., Carney, P.A., et al. (2007). Influence of computer-aided detection on performance of screening mammography. N Engl J Med, 356, 13991409.Google Scholar
Fink, G.R., Halligan, P.W., Marshall, J.C., et al. (1996). Where in the brain does visual attention select the forest and the trees? Nature, 382, 626628.Google Scholar
Gale, A.G., Walker, G.E. (1991). Design for performance: quality assessment in a national breast screening programme. In: Lovesay, E.J. (ed.) Ergonomics-Design for Performance. London, England: Taylor & Francis.Google Scholar
Gale, A.G., Vernon, J., Millar, K., Worthington, B.S. (1983). Interpreting radiographs in a single glance. Radiology, 149, 253.Google Scholar
Gibson, E.J. (1969). Principles of Perceptual Learning. New York, NY: Appleton-Century-Crofts.Google Scholar
Gibson, J.J. (1979). The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin.Google Scholar
Gibson, J.J., Gibson, E.J. (1955). Perceptual learning: differentiation or enrichment? Psychol Rev, 62, 3241.Google Scholar
Gobet, F. (2016). Understanding Expertise: A Multi-Disciplinary Approach. London, UK: Palgrave.Google Scholar
Goldstone, R.L., de Leeuw, J.R., Landy, D.H. (2015). Fitting perception in and to cognition. Cognition, 135, 2429.Google Scholar
Gordon, S.E. (2016). Implications of cognitive theory for knowledge acquisition. In: Hoffman, R.R. (ed.) The Psychology of Expertise: Cognitive Research and Empirical AI. New York, NY: Routledge, pp. 99120.Google Scholar
Gregory, R.L. (1970). The Intelligent Eye. New York, NY: McGraw-Hill.Google Scholar
Gregory, R.L. (2001). Analog or digital? In: Parks, T.E. (ed.) Looking at Looking. Thousand Oaks, CA: Sage, pp. 115118.Google Scholar
Gregory, R.L., Gombrich, E.H. (1973). The confounded eye. In: Gregory, R.L. (ed.) Illusion in Nature and Art. London, England: Duckworth, pp. 4995.Google Scholar
Grill-Spector, K., Kanwisher, N. (2005). Visual recognition: as soon as you know it is there, you know what it is. Psychol Sci, 16, 152160.Google Scholar
Gunderman, R.B. (2000). Illuminating the “black boxes” of learning and recall. Acad Radiol, 7, 641646.Google Scholar
Gunderman, R.B. (2001). Is technical school a good model for radiology residency? Am J Roentgenol, 177, 10051007.Google Scholar
Gunderman, R.B., Williamson, K.B., Frank, M., et al. (2003). Learner-centered education. Radiology, 227, 1517.Google Scholar
Gur, D., Sumkin, J.H., Rockette, H.E., et al. (2004). Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst, 96,185190.Google Scholar
Haider, H., Frensch, P.A. (1996). The role of information reduction in skill acquisition. Cogn Psychol, 30, 304–337.Google Scholar
Haider, H., Frensch, P.A. (1999). Eye movement during skill acquisition: more evidence for the information reduction hypothesis. J Exp Psychol: Learn Mem Cogn, 25, 172190.Google Scholar
Haller, S., Radue, E.W. (2005). What is different about a radiologist’s brain? Radiology, 236, 983989.Google Scholar
Harries, C., Evans, J.S.B.T., Dennis, I. (2000). Measuring doctors’ self-insight into their treatment decisions. Appl Cognit Psychol, 14, 455477.Google Scholar
Herzog, M.H., Cretenoud, A.F., Grzeczkowski, L. (2017). What is new in perceptual learning? J Vision, 17, 23.Google Scholar
Hmelo-Silver, C.E., Pfeffer, M.G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn Sci, 28, 127138.Google Scholar
Hochberg, J.E. (1978) Perception, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Jacobs, R.A. (2010) Integrated approaches to perceptual learning. Topics Cogn Sci, 2, 182188.Google Scholar
Johnson, A., Proctor, R.W. (2017). Skill Acquisition and Training: Achieving Expertise in Simple and Complex Tasks. New York, NY: Routledge.Google Scholar
Joseph, G-M., Patel, V.L. (1990). Domain knowledge and hypothesis generation in diagnostic reasoning. Med Dec Mak, 10, 3146.Google Scholar
Kahneman, D. (2011). Thinking Fast and Slow. New York, NY: Farra, Straus and Giroux.Google Scholar
Kalyuga, S., Ayres, P., Chandler, P., Sweller, J. (2003). The expertise reversal effect. Educ Psychol, 38, 2331.Google Scholar
Kellman, P.J. (2002) Perceptual learning. In: Gallistel, R. (ed.) Stevens’ Handbook of Experimental Psychology, Vol. 3, Learning, Motivation and Emotion. New York, NY: Wiley, pp. 259299.Google Scholar
Kellman, P.J. (2013). Adaptive and perceptual learning technologies in medical education and training. Milit Med, 178, 98106.Google Scholar
Kellman, P.J., Massey, C.M., Son, J. (2010). Perceptual learning modules in mathematics: enhancing students’ pattern recognition, structure extraction, and fluency. Topics Cogn Sci, 2, 285305.Google Scholar
Kok, E.M., de Bruin, A.B.H., Robben, S.G.F., van Merrienboer, J.J.G. (2012). Looking in the same manner but seeing it differently: bottom-up and expertise effects in radiology. Appl Cognit Psychol, 26, 854862.Google Scholar
Kok, E.M., van Geel, K., van Merrienboer, J.J.G., Robben, S.G.F. (2017). What we do and do not know about teaching medical image interpretation. Frontiers Psychol, 8, 309.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137144.Google Scholar
Kuhn, T. (1962). The structure of scientific revolutions. Int Encycloped Unif Sci, 2(2).Google Scholar
Kulatunga-Moruzi, C., Brooks, L.R., Norman, G.R. (2004). Using comprehensive feature lists to bias medical diagnosis. J Exp Psychol: Learn Mem Cog, 30, 563572.Google Scholar
Kundel, H.L. (2007). How to minimize perceptual error and maximize expertise in medical imaging. Proc SPIE Med Imag: Image Perc, Obs Perf Tech Assess, 6515, 651508-1–651508-11.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 chess radiographs without visual search. Radiology, 108, 527532.Google Scholar
Kundel, H.L., Wright, D.J. (1969). The influence of prior knowledge on visual search strategies during viewing of chest radiographs. Radiology, 2, 315320.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., Conant, E.F., Weinstein, S.P. (2007). Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology, 242, 396402.Google Scholar
Kundel, H.L., Nodine, C.F., Krupinski, E.A., Mello-Thoms, C. (2008). Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Acad Radiol, 15, 881886.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, pp. 311342.Google Scholar
Llewellyn-Thomas, E., Lansdown, E.L. (1963). Visual search patterns of radiologists in training. Radiology, 1, 288292.Google Scholar
Lu, Z-L., Lin, Z., Dosher, B.A. (2016). Translating perceptual learning from the laboratory to applications. Trends Cogn Sci, 20, 561563.Google Scholar
Mackworth, N.H., Morandi, A.J. (1967). The gaze selects informative details within pictures. Perc Psychophys, 2, 547552.Google Scholar
Mello-Thoms, C., Dunn, S., Nodine, C.F., Kundel, H.L., Weinstein, S.P. (2002). The perception of breast cancer: what differentiates missed from reported cancers in mammography? Acad Radiol, 9, 10041012.Google Scholar
Mello-Thoms, C., Hardesty, L., Sumkin, J., et al. (2005). Effects of lesion conspicuity on visual search in mammogram reading. Acad Radiol, 12, 830840.Google Scholar
Mello-Thoms, C., Ganott, M., Sumkin, J., et al. (2008). Different search patterns and similar decision outcomes: how can experts agree in the decisions they make when reading digital mammograms? In: Krupinski, E.A. (ed.) Int Work Digital Mammo, Lecture Notes on Computer Science 5116. Berlin, Germany: Springer-Verlag, pp. 212219.Google Scholar
Miller, G.A. (1956). The magical number seven, plus or minus two: some limits to our capacity for processing information. Psychol Rev, 63, 8197.Google Scholar
Monsky, W.L., Levine, D., Mehta, T.S., et al. (2002). Using a sonographic simulator to assess residents before overnight call. Am J Roentgenol, 178, 3539.Google Scholar
Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press.Google Scholar
Mueller, D., Georges, A., Vashow, D. (2007). Cooperative learning as applied to resident instruction in radiology reporting. Acad Radiol, 14, 15771583.Google Scholar
Myles-Worsley, M., Johnston, W.A., Simons, M.A. (1988). The influence of expertise on X-ray image processing. J Exp Psychol, 14, 553557.Google Scholar
Navon, D. (1977). Forest before trees. The precedence of global features in visual perception. Cogn Psychol, 9, 353383.Google Scholar
Neisser, U. (1967). Cognitive Psychology. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Neisser, U. (1976). Cognition and Reality. San Francisco, CA: W.H. Freeman.Google Scholar
Newell, A., Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Nodine, C.F., Krupinski, E.A. (1998). Perceptual skill, radiology expertise, and visual test performance with NINA and WALDO. Acad Radiol, 5, 603612.Google Scholar
Nodine, C.F., Mello-Thoms, C. (2000). The nature of expertise in radiology. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging, Volume 1: Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 859893.Google Scholar
Nodine, C.F., Kundel, H.L., Mello-Thoms, C., et al. (1999). How experience and training influence mammography expertise. Acad Radiol, 1, 575585.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., et al. (2001). Blinded review of retrospectively visible unreported breast cancers: an eye-position analysis. Radiology, 221, 122129.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., Weinstein, S.P. (2002). Time course of perception and decision making during mammographic interpretation. Am J Roentgenol, 179, 917923.Google Scholar
Norman, G.R., Brooks, L.R., Colle, C.L., Hatala, R.M. (2000). The benefit of diagnostic hypotheses in clinical reasoning: experimental study of an instructional intervention for forward and backward reasoning. Cogn Instruct, 17, 433448.Google Scholar
Norman, G., Eva, K., Brooks, L., Hamstra, S. (2006). Expertise in medicine and surgery. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance. New York, NY: Cambridge University Press, pp. 339353.Google Scholar
Norman, G., Sherbino, J., Dore, K., et al. (2014). The etiology of diagnostic errors: a controlled trial of system 1 versus system 2 reasoning. Acad Med, 89, 277284.Google Scholar
Olshausen, B.A., Field, D.J. (2000). Vision and the coding of natural images. Am Scientist, 88, 238245.Google Scholar
Peterson, M.A., Rhodes, G. (2003). Perception of Faces, Objects and Scenes: Analytic and Holistic Processes. New York, NY: Oxford University Press.Google Scholar
Pisano, E.D., Gatsonis, C., Hendrick, E., et al. (2005). Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med, 353, 17731783.Google Scholar
Puri, A.M., Wojciulik, E. (2008). Expectation both helps and hinders object perception. Vision Res, 48, 589597.Google Scholar
Rackow, P.L., Spitzer, V.M., Hendee, W.R. (1987). Detection of low-contrast signals: a comparison of observers with and without radiology training. Invest Radiol, 22, 311314.Google Scholar
Raufaste, E., Eyrolle, H., Marine, C. (1998). Pertinence generation in radiological diagnosis: spreading activation and the nature of expertise. Cogn Sci, 22, 517546.Google Scholar
Ross, P.E. (2006). The expert mind. Scientific Am, 295, 6471.Google Scholar
Sagi, D. (2011). Perceptual learning in vision research. Vision Res, 51, 15521566.Google Scholar
Schmidt, H.G., Rikers, R.M.J. (2007). How expertise develops in medicine: knowledge encapsulation and illness script formation. Med Educ, 41, 11331139.Google Scholar
Siamecki, N., Graf, P. (1978). The generation effect: delineation of a phenomenon. J Exp Psychol: Human Learn Mem, 4, 592604.Google Scholar
Smith-Bindman, R., Chu, P., Migloretti, D.L., et al. (2005). Physician predictors of mammographic accuracy. J Natl Cancer Inst, 97, 358367.Google Scholar
Sowden, P.T., Davies, I.R.L., Roling, P. (2000). Perceptual learning of the detection of features in X-ray images: a functional role for improvement in adults’ visual sensitivity? J Exp Psychol: Human Percep Perf, 26, 379390.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1982). Radiographic interpretation with and without search: visual search aids the recognition of chess pathology. Invest Radiol, 17, 145151.Google Scholar
Taylor, P.M. (2007). A review of research into the development of radiologic expertise: implications for computer-based training. Acad Radiol, 14, 12521263.Google Scholar
Tuddenham, W.J., Calvert, W.P. (1961). Visual search patterns in roentgen diagnosis. Radiology, 76, 255256.Google Scholar
van der Gijp, A., van der Schaaf, M.F., van der Schaaf, I.C., Huige, J.C.B.M., Ravesloot, C.J., Schaik, J.P.J., ten Cate, T.J. (2014). Interpretation of radiological images: towards a framework of knowledge and skills. Adv Health Sci Educ, 19, 565580.Google Scholar
van der Gijp, A., Ravesloot, C.J., van der Schaaf, M.F., et al. (2015). Volumetric and two-dimensional image interpretation show different cognitive processes in learners. Acad Radiol, 22, 632639.Google Scholar
van der Gijp, A., Webb, E.M., Naeger, D.M. (2017). How radiologists think: understanding fast and slow thought processing and how it can improve our teaching. Acad Radiol, 24, 768771.Google Scholar
Venjakob, A.C., Mello-Thoms, C. (2016). Review of prospects and challenges of eye tracking in volumetric imaging. J Med Imag, 3, 011002.Google Scholar
Venjakob, A.C., Marnitz, T., Phillips, P., Mello-Thoms, C. (2016). Image size influences visual search and perception of hemorrhages when reading cranial CT: an eye tracking study. Hum Factors, 58, 441451.Google Scholar
Wen, G., Aizenman, A., Drew, T., Wolfe, J.M., Haygood, T.M., Markey, M.K. (2016). Computational assessment of visual search strategies in volumetric medical images. J Med Imag, 3, 015501.Google Scholar
Wise, J.A., Kubose, T., Chang, N., Russell, A., Kellman, P.J. (2000). Perceptual learning modules and mathematics and science instruction. In: Hoffman, P., Lemke, D. (eds.) Teaching and Learning in a Network World: TechEd 2000 Proceedings. Amsterdam, The Netherlands: IOS Press, pp. 169176.Google Scholar
Wolfe, J.M., Cave, K.R., Franzel, S.L. (1989). Guided search: an alternative to the feature integration model for visual search. J Exp Psychol: Human Percep Perf, 15, 419433.Google Scholar
Wood, B.P. (1999) Visual expertise. Radiology, 211, 13.Google Scholar
Wood, G., Knapp, K.M., Rock, B., Cousens, C., Roobottom, C., Wilson, M.R. (2013). Visual expertise in detecting and diagnosing skeletal fractures. Skelet Radiol, 42, 165172.Google Scholar
Yang, G-Z., Dempere-Marco, L., Xiao-Peng, H., et al. (2002). Visual search: psychophysical models and practical applications. Image Vision Comp, 20, 291305.Google Scholar

References

Alvarez, G.A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends Cogn Sci, 15(3), 122131.Google Scholar
Ariely, D. (2001). Seeing sets: representation by statistical properties. Psychol Sci, 12(2), 157162.Google Scholar
Bertram, R., Helle, L., Kaakinen, J.K., Svedstrom, E. (2013). The effect of expertise on eye movement behaviour in medical image perception. PLoS One, 8(6), e66169.Google Scholar
Biederman, I. (1972). Perceiving real-world scenes. Science, 177(43), 7780.Google Scholar
Biederman, I., Glass, A.L., Stacy, E.W. (1973). Searching for objects in real-world scenes. J Exp Psychol Gen, 97, 2227.Google Scholar
Biederman, I., Mezzanotte, R.J., Rabinowitz, J.C. (1982). Scene perception: detecting and judging objects undergoing relational violations. Cogn Psychol, 14, 143177.Google Scholar
Brady, T.F., Shafer-Skelton, A., Alvarez, G.A. (2017). Global ensemble texture representations are critical to rapid scene perception. J Exp Psychol: Hum Percept Perform, 43(6), 11601176.Google Scholar
Carmody, D.P., Nodine, C.F., Kundel, H.L. (1981). Finding lung nodules with and without comparative visual scanning. Percept Psychophys, 29(6), 594598.Google Scholar
Castelhano, M.S., Henderson, J.M. (2007). Initial scene representations facilitate eye movement guidance in visual search. J Exp Psychol: Hum Percept Perform, 33(4), 753763.Google Scholar
Chong, S.C., Treisman, A. (2003). Representation of statistical properties. Vision Res, 43(4), 393404.Google Scholar
Crick, F. (1984). Function of the thalamic reticular complex: the searchlight hypothesis. Proc Natl Acad Sci USA, 81, 45864590.Google Scholar
Crick, F., Koch, C. (1990). Towards a neurobiological theory of consciousness. Semin Neurosci, 2, 263275.Google Scholar
Drew, T., Evans, K., Vo, M.L.-H., Jacobson, F.L., Wolfe, J.M. (2013). Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images? Radiographics, 33, 263274.Google Scholar
Drewes, J., Trommershauser, J., Gegenfurtner, K.R. (2011). Parallel visual search and rapid animal detection in natural scenes. J Vision, 11(2), 20.Google Scholar
Ebner, L., Tall, M., Roychoudhury, K., Ly, D.L., Roos, J.E., Napel, S., et al. (2016). Variations in the functional visual field for detection of lung nodules on chest computed tomography: impact of nodule size, distance, and local lung complexity. Med Phys, 44, 34833490.Google Scholar
Egeth, H.E., Virzi, R.A., Garbart, H. (1984). Searching for conjunctively defined targets. J Exp Psychol: Hum Percept Perform, 10, 3239.Google Scholar
Ehinger, K.A., Hidalgo-Sotelo, B., Torralba, A., Oliva, A. (2009). Modeling search for people in 900 scenes: a combined source model of eye guidance. Vis Cogn, 17(6), 945978.Google Scholar
Evans, K.K., Treisman, A. (2005). Perception of objects in natural scenes: is it really attention free? J Exp Psychol: Hum Percept Perform, 31(6), 14761492.Google Scholar
Evans, K.K., Georgian-Smith, D., Tambouret, R., Birdwell, R.L., Wolfe, J.M. (2013). The gist of the abnormal: above-chance medical decision making in the blink of an eye. Psychon Bull Rev, 20(6), 11701175.Google Scholar
Evans, K., Haygood, T.M., Cooper, J., Culpan, A.M., Wolfe, J.M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast Proc Natl Acad Sci USA, 113(37), 1029210297.Google Scholar
Fei-Fei, L., Iyer, A., Koch, C., Perona, P. (2007). What do we perceive in a glance of a real-world scene? J Vision, 7(1), 10.Google Scholar
Freedman, D.J., Riesenhuber, M., Poggio, T., Miller, E.K. (2002). Visual categorization and the primate prefrontal cortex: neurophysiology and behavior. J Neurophysiol, 88(2), 929941.Google Scholar
Gierach, G.L., Li, H., Loud, J.T., Greene, M.H., Chow, C.K., Lan, L., et al. (2014). Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res, 16(4), 424.Google Scholar
Greene, M.R., Oliva, A. (2008). Recognition of natural scenes from global properties: seeing the forest without representing the trees. Cogn Psychol, 58(2), 137176.Google Scholar
Greene, M.R., Oliva, A. (2009). The briefest of glances: the time course of natural scene understanding. Psychol Sci, 20(4), 464472.Google Scholar
Grindley, G.C., Townsend, V. (1968). Voluntary attention in peripheral vision and its effects on acuity and differential thresholds. Q J Exp Psychol, 20(1), 1119.Google Scholar
Helmholtz, H. v. (1924). Treatise on Physiological Optics (Southall, trans. from 3rd German ed. of 1909, ed.). Rochester, NY: Optical Society of America.Google Scholar
Henderson, J.M., Ferreira, F. (2004). Scene perception for psycholinguists. In: Henderson, J.M., Ferreira, F. (eds.) The Interface of Language, Vision, and Action: Eye Movements and the Visual World. New York, NY: Psychology Press, pp. 158.Google Scholar
Kallenberg, M., Petersen, K., Nielsen, M., Ng, A.Y., Pengfei, D., Igel, C., et al. (2016). Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imag, 35(5), 13221331.Google Scholar
Koehler, K., Eckstein, M.P. (2017). Beyond scene gist: objects guide search more than scene background. J Exp Psychol: Hum Percept Perform, 43(6), 11771193.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3(2), 137144.Google Scholar
Kundel, H.L. (2007). How to minimize perceptual error and maximize expertise in medical imaging. Proc SPIE Med Imag, 6515, 651508.Google Scholar
Kundel, H.L., La Follette, P.S., Jr. (1972). Visual search patterns and experience with radiological images. Radiology, 103(3), 523528.Google Scholar
Kundel, H.L., Nodine, C.F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527532.Google Scholar
Kundel, H.L., Nodine, C.F. (2004). Modeling visual search during mammogram viewing. Proc SPIE Med Imag, 5372, 538063.Google Scholar
Kundel, H.L., Nodine, C.F., Krupinski, E.A., Mello-Thoms, C. (2008). Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Acad Radiol, 15(7), 881886.Google Scholar
Li, F.F., VanRullen, R., Koch, C., Perona, P. (2002). Rapid natural scene categorization in the near absence of attention. Proc Natl Acad Sci USA, 99(14), 95969601.Google Scholar
Li, H., Giger, M.L., Lan, L., Janardanan, J., Sennett, C.A. (2014). Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imag, 1(3), 031009.Google Scholar
Maljkovic, V., Nakayama, K. (1994). Priming of popout: I. Role of features. Mem Cognit, 22(6), 657672.Google Scholar
Navon, D. (1977). Forest before the trees: the precedence of global features in visual perception. Cogn Psychol, 9, 353383.Google Scholar
Neisser, U. (1967). Cognitive Psychology. New York, NY: Appleton-Century-Crofts.Google Scholar
Nielsen, M., Vachon, C.M., Scott, C.G., Chernoff, K., Karemore, G., Karssemeijer, N., et al. (2014). Mammographic texture resemblance generalizes as an independent risk factor for breast cancer. Breast Cancer Res, 16(2), R37.Google Scholar
Nodine, C.F., Kundel, H.L., Lauver, S.C., Toto, L.C. (1996). Nature of expertise in searching mammograms for breast masses. Acad Radiol, 3(12), 10001006.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., Weinstein, S.P. (2002). Time course of perception and decision making during mammographic interpretation. AJR Am J Roentgenol, 179(4), 917923.Google Scholar
Nordfang, M., Wolfe, J.M. (2014). Guided search for triple conjunctions. Atten Percept Psychophys, 76(6), 15351559.Google Scholar
Oliva, A. (2005). Gist of the scene. In: Itti, L., Rees, G., Tsotsos, J. (eds.) Neurobiology of Attention. San Diego, CA: Academic Press / Elsevier, pp. 251257.Google Scholar
Oliva, A., Schyns, P.G. (1997). Coarse blobs or fine edges? Evidence that information diagnosticity changes the perception of complex visual stimuli. Cogn Psychol, 34(1), 72107.Google Scholar
Oliva, A., Torralba, A. (2001). Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis, 42(3), 145175.Google Scholar
Oliva, A., Torralba, A. (2006). Building the gist of a scene: the role of global image features in recognition. Prog Brain Res, 155, 2336.Google Scholar
O’Regan, K. (1992). Solving the “real” mysteries of visual perception. The world as an outside memory. Can J Psychol, 46, 461488.Google Scholar
Posner, M.I. (1980). Orienting of attention. Q J Exp Psychol, 32, 325.Google Scholar
Potter, M.C., Faulconer, B.A. (1975). Time to understand pictures and words. Nature, 253, 437438.Google Scholar
Roskies, A. (1999). The binding problem. Neuron, 24(1), 79.Google Scholar
Ross, M.G., Oliva, A. (2010). Estimating perception of scene layout properties from global image features. J Vision, 10(1), 125.Google Scholar
Sanders, A.F., Houtmans, M.J.M. (1985). Perceptual modes in the functional visual field. Acta Psychol, 58, 251261.Google Scholar
Sanocki, T., Epstein, W. (1997). Priming spatial layout of scenes. Psychol Sci, 8, 374378.Google Scholar
Schill, H., Culpan, A.-M., Wolfe, J.M., Evans, K.K. (2017). Detecting the “gist” of breast cancer in mammograms three years before the cancer appears. Paper presented at the Annual Meeting of the Vision Science Society.Google Scholar
Scutt, D., Lancaster, G.A., Manning, J.T. (2006). Breast asymmetry and predisposition to breast cancer. Breast Cancer Res, 8(2), R14.Google Scholar
Sigala, N., Logothetis, N.K. (2002). Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 415(6869), 318320.Google Scholar
Swensson, R.G. (1980). A two-stage detection model applied to skilled visual search by radiologists. Percept Psychophys, 27(1), 1116.Google Scholar
Thorpe, S.J., Gegenfurtner, K.R., Fabre-Thorpe, M., Bulthoff, H.H. (2001). Detection of animals in natural images using far peripheral vision. Eur J Neurosci, 14(5), 869876.Google Scholar
Treisman, A. (1985). Preattentive processing in vision. Comput Vision, Graphics Image Proc, 31, 156177.Google Scholar
Treisman, A. (1996). The binding problem. Curr Opin Neurobiol, 6, 171178.Google Scholar
Treisman, A. (1998). Feature binding, attention and object perception. Phil Trans R Soc Lond B Biol Sci, 353(1373), 12951306.Google Scholar
Treisman, A., Gelade, G. (1980). A feature-integration theory of attention. Cogn Psychol, 12, 97136.Google Scholar
Tversky, A., Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol Rev, 90(4), 293315.Google Scholar
Vo, M.L.-H., Wolfe, J.M. (2015). The role of memory for visual search in scenes. Ann NY Acad Sci, 1339, 7281.Google Scholar
von der Malsburg, C. (1981). The correlation theory of brain function. In: Domany, E., van Hemmen, J.L., Schulten, K. (eds.) Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany, Internal Report 81–2. Reprinted in Models of Neural Networks II (1994). Berlin, Germany: Springer.Google Scholar
Wagemans, J. (1997). Characteristics and models of human symmetry detection. Trends Cogn Sci, 1(9), 346352.Google Scholar
Wang, J., Shidfar, A., Ivancic, D., Ranjan, M., Liu, L., Choi, M.R., et al. (2017). Overexpression of lipid metabolism genes and PBX1 in the contralateral breasts of women with estrogen receptor-negative breast cancer. Int J Cancer, 140(11), 24842497.Google Scholar
Wolfe, J.M. (1994). Guided search 2.0: a revised model of visual search. Psychon Bull Rev, 1(2), 202238.Google Scholar
Wolfe, J.M. (2003). Moving towards solutions to some enduring controversies in visual search. Trends Cogn Sci, 7(2), 7076.Google Scholar
Wolfe, J.M. (2007). Guided search 4.0: current progress with a model of visual search. In: Gray, W. (ed.) Integrated Models of Cognitive Systems. New York, NY: Oxford Press, pp. 99119.Google Scholar
Wolfe, J.M., Bennett, S.C. (1997). Preattentive object files: shapeless bundles of basic features. Vision Res, 37(1), 2543.Google Scholar
Wolfe, J.M., Cave, K.R. (1999). The psychophysical evidence for a binding problem in human vision. Neuron, 24(1), 1117.Google Scholar
Wolfe, J.M., Horowitz, T.S. (2017). Five factors that guide attention in visual search. Nat Hum Behav, 1, 0058.Google Scholar
Wolfe, J.M., Cave, K.R., Franzel, S.L. (1989). Guided search: an alternative to the feature integration model for visual search. J Exp Psychol: Hum Percept Perform, 15, 419433.Google Scholar
Wolfe, J.M., Palmer, E.M., Horowitz, T.S. (2010). Reaction time distributions constrain models of visual search. Vision Res, 50, 13041311.Google Scholar
Wolfe, J.M., Vo, M.L.-H., Evans, K.K., Greene, M.R. (2011). Visual search in scenes involves selective and non-selective pathways. Trends Cogn Sci, 15(2), 7784.Google Scholar
Zheng, B., Sumkin, J.H., Zuley, M.L., Wang, X., Klym, A.H., Gur, D. (2012). Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol, 81(11), 32223228.Google Scholar

References

Arfken, G.B., Weber, H.J. (1995). Mathematical Methods for Physicists. San Diego, CA: 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 U S A, 90, 9758–9765.Google Scholar
Barrett, H.H., Myers, K.J., Hoeschen, C., Kupinski, M.A., Little, M.P. (2015). Task-based measures of image quality and their relation to radiation dose and patient risk. Phys Med Biol, 60, R1–R75.Google Scholar
Beister, M., Kolditz, D., Kalender, W.A. (2012). Iterative reconstruction methods in X-ray CT. Phys Med, 28, 94108.Google Scholar
Blackman, R.B., Tukey, J.W. (1959). The Measurement of Power Spectra, from the Point of View of Communications Engineering. New York, NY: Dover Publications.Google Scholar
Bradford, C.D., Peppler, W.W., Waidelich, J.M. (1999). Use of a slit camera for MTF measurements. Med Phys, 26, 22862294.Google Scholar
Brenner, D.J., Hall, E.J. (2007). Computed tomography – an increasing source of radiation exposure. N Engl J Med, 357, 22772284.Google Scholar
Dainty, J.C., Shaw, R. (1974). Image Science: Principles, Analysis and Evaluation of Photographic-Type Imaging Processes. New York, NY: Academic Press.Google Scholar
Dobbins, J.T. (2000). Image quality metrics for digital systems. In: Beutel, J., Van Metter, R., Kundel, H. (eds.) Handbook of Medical Imaging. Vol. 1: Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 163222.Google Scholar
Fujita, H., Doi, K., Giger, M.L. (1985). Investigation of basic imaging properties in digital radiography. 6. MTFs of II-TV digital imaging-systems. Med Phys, 12, 713720.Google Scholar
Giger, M.L., Doi, K. (1984) Investigation of basic imaging properties in digital radiography. 1. Modulation transfer-function. Med Phys, 11, 287295.Google Scholar
Giger, M.L., Doi, K., Metz, C.E. (1984). Investigation of basic imaging properties in digital radiography. 2. Noise Wiener spectrum. Med Phys, 11, 797805.Google Scholar
Giger, M.L., Doi, K., Fujita, H. (1986). Investigation of basic imaging properties in digital radiography. 7. Noise Wiener spectra of II-TV digital imaging-systems. Med Phys, 13, 131138.Google Scholar
Hwang, S.A., Seo, J.B., Choi, B.K., Do, K.H., Ko, S.M., Lee, S.H., Lee, J.S., Song, J.W., Song, K.S., Lim, T.H. (2003). Liquid-crystal display monitors and cathode-ray tube monitors: a comparison of observer performance in the detection of small solitary pulmonary nodules. Korean J Radiol, 4, 153156.Google Scholar
ICRU (1995). ICRU report 54: medical imaging – the assessment of image quality. International Commission on Radiation Units and Measurements. J Int Commission Rad Units Meas, 28.Google Scholar
Krupinski, E.A., Johnson, J., Roehrig, H., Nafziger, J., Lubin, J. (2005). On-axis and off-axis viewing of images on CRT displays and LCDs: observer performance and vision model predictions. Acad Radiol, 12, 957964.Google Scholar
Metz, C.E. (2000). Fundamental ROC analysis. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging. Bellingham, WA: SPIE Press, pp. 751–770.Google Scholar
Oschatz, E., Prokop, M., Scharitzer, M., Weber, M., Balassy, C., Schaefer-Prokop, C. (2005). Comparison of liquid crystal versus cathode ray tube display for the detection of simulated chest lesions. Eur Radiol, 15, 14721476.Google Scholar
Samei, E., Saunders, R.S., Jr., 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., Samei, E. (2006a). Improving mammographic decision accuracy by incorporating observer ratings with interpretation time. Br J Radiol, 79 Spec No 2, S117–S122.Google Scholar
Saunders, R.S., Samei, E. (2006b). Resolution and noise measurements of five CRT and LCD medical displays. Med Phys, 33, 308319.Google Scholar
Saunders, R., Samei, E., Hoeschen, C. (2003). Impact of resolution and noise characteristics of digital radiographic detectors on the detectability of lung nodules. SPIE Proc, 5030, 1625.Google Scholar
Saunders, R.S., Samei, E., Hoeschen, C. (2004). Impact of resolution and noise characteristics of digital radiographic detectors on the detectability of lung nodules. Med Phys, 31, 16031613.Google Scholar
Saunders, R.S., Samei, E., Baker, J., Delong, D., Soo, M.S., Walsh, R., Pisano, E., Kuzmiak, C.M., Pavic, D. (2006) Comparison of LCD and CRT displays based on efficacy for digital mammography. Acad Radiol, 13, 1317.Google Scholar
Solomon, J., Marin, D., Choudhury, K., Patel, B., Samei, E. (2017). Effect of radiation dose reduction and reconstruction algorithm on image noise, contrast, resolution, and detectability of subtle hypoattenuating liver lesions at multidetector CT: filtered back projection versus a commercial model-based iterative reconstruction algorithm. Radiology, 284, 777787.Google Scholar
Workman, A., Brettle, D.S. (1997). Physical performance measures of radiographic imaging systems. Dentomaxillofac Rad, 26, 139146.Google Scholar

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