Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-02T22:00:40.009Z Has data issue: false hasContentIssue false

8 - Perceptual Factors in Reading Medical Images

from Part II - Science of Image Perception

Published online by Cambridge University Press:  20 December 2018

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

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Handbook of Medical Image Perception and Techniques , pp. 95 - 106
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

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, 155–191.CrossRefGoogle ScholarPubMed
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, 838–844.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.CrossRefGoogle ScholarPubMed
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, 805–815.Google Scholar
Bird, R.E., Wallace, T.W., Yankaskas, B.C. (1992). Analysis of cancers missed at screening mammography. Radiology, 184, 613–617.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, 379–384.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, 344–360.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, 845–849.Google Scholar
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, 672–707.Google Scholar
Chaparro, A., Stromeyer, C.F., Huang, E.P., et al. (1993). Colour is what the eyes see best. Nature, 361, 348–350.CrossRefGoogle 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, 577–590.Google Scholar
Clarke, E.L., Treanor, D. (2017). Colour in digital pathology: a review. Histopathol, 70, 153–163.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, 39–51.Google Scholar
Czaja, S.J., Sharit, J. (1993). Age differences in the performance of computer-based work. Psychol Aging, 8, 59–67.Google Scholar
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, 1105–1111.Google Scholar
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, 263–274.CrossRefGoogle Scholar
Emre, C.M., Alp, A.Y., Stoecker, W.V., et al. (2007). Unsupervised border detection in dermoscopy images. Skin Res Technol, 13, 454–462.CrossRefGoogle 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, 27–33.Google Scholar
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.Google Scholar
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, 1173–1174.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, 719–732.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, 143–149.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, 25–32.CrossRefGoogle ScholarPubMed
Johnston, R.E., Zimmerman, J.B., Rogers, D.C., et al. (1985). Perceptual standardization. Proc SPIE Med Imag, 536, 44–49.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.CrossRefGoogle ScholarPubMed
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, 1226–1235.Google Scholar
Komorowski, M., Celi, L.A. (2017). Will artificial intelligence contribute to overuse in healthcare? Crit Care Med, 45, 912–913.CrossRefGoogle ScholarPubMed
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137–144.CrossRefGoogle ScholarPubMed
Krupinski, E.A. (2017). Diagnostic accuracy and visual search efficiency: single 8 MP vs. dual 5 MP displays. J Digit Imag, 30, 144–147.CrossRefGoogle 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, 177–182.CrossRefGoogle ScholarPubMed
Krupinski, E.A., Roehrig, H. (2000). The influence of a perceptually linearized display on observer performance and visual search. Acad Radiol, 7, 8–13.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, 101–108.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, 813–818.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, 257–263.Google Scholar
Krupinski, E.A., Roehrig, H., Furukawa, T. (1999b). Influence of film and monitor display luminance on observer performance and visual search. Acad Radiol, 6, 411–418.CrossRefGoogle ScholarPubMed
Krupinski, E., Nypaver, M., Poropatich, R., et al. (2002). Telemedicine/telehealth: an international perspective. Clinical applications in telemedicine/telehealth. Telemed J E Health, 8, 13–34.Google Scholar
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, 1543–1556.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, 779–785.CrossRefGoogle ScholarPubMed
Kundel, H.L. (1975). Peripheral vision, structured noise and film reader error. Radiology, 114, 269–273.Google Scholar
Kundel, H.L., Nodine, C.F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527–532.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, 175–181.CrossRefGoogle 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, 472–478.Google Scholar
Kundel, H.L., Nodine, C.F., Toto, L. (1991). Searching for lung nodules: the guidance of visual scanning. Invest Radiol, 26, 777–781.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, 253–260.CrossRefGoogle ScholarPubMed
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, 611–617.CrossRefGoogle ScholarPubMed
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, 570–584.Google Scholar
Levkowitz, H., Herman, G.T. (1992). Color scales for image data. IEEE Comp Graphics and Applic, 12, 72–80.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, 288–291.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, 55–77.Google Scholar
McKoy, K., Antoniotti, N.M., Armstrong, A., et al. (2016). Practice guidelines for teledermatology. Telemed eHealth, 22, 981–990.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.CrossRefGoogle ScholarPubMed
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, 11–16.CrossRefGoogle ScholarPubMed
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, 609–615.Google Scholar
Mullen, K.T. (1985). The contrast sensitivity of human colour vision to red-green and blue-yellow chromatic gratings. J Physiol, 359, 381–400.CrossRefGoogle ScholarPubMed
Murakami, S., Verdonschot, R.G., Kreiborg, S., Kakimoto, N., Kawaguchi, A. (2017). Stereoscopy in dental education: an investigation. J Dent Educ, 81, 450–457.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, 305–321.Google Scholar
Mutti, D.O., Zadnik, K. (1996). Is computer use a risk factor for myopia? J Am Optom Assoc, 67, 521–530.Google Scholar
Nodine, C.F., Kundel, H.L. (1987). Using eye movements to study visual search and to improve tumor detection. Radiographics, 7, 1241–1250.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, 475–485.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, 917–923.CrossRefGoogle ScholarPubMed
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, 359–363.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, 22–26.Google Scholar
Pizer, S.M. (1981a). Intensity mappings to linearized displays. Comput Graphics Image Process, 17, 262–268.Google Scholar
Pizer, S.M. (1981b). Intensity mapping: linearization, image-based, user-controlled. Proc SPIE Med Imag, 271, 21–27.Google Scholar
Quaghebeur, G., Bhattacharya, J.J., Murfitt, J. (1997). Radiologists and visual acuity. Eur Radiol, 7, 41–43.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, 1729–1734.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, 935–938.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, 1815–1821.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, 1085–1098.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, 517–524.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, 21–26.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, 311–316.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, 189–196.Google Scholar
Shen, D., Wu, G., Suk, H.I. (2017). Deep learning in medical image analysis. Annu Rev Med Image Anal, 19, 221–248.Google ScholarPubMed
Siegal, D., Stratchko, L.M., DeRoo, C. (2017). The role of radiology in diagnostic error: a medical malpractice claims review. Diagnosis, 4, 125–131.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, 42–46.Google Scholar
Sotoyama, M., Jonai, H., Saito, S., et al. (1996). Analysis of ocular surface area for comfortable VDT workstation layout. Ergonomics, 39, 877–884.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, 379–390.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, 562–570.CrossRefGoogle ScholarPubMed
Taylor, G.A. (2017). Perceptual errors in pediatric radiology. Diagnosis, 4, 141–147.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, 574–578.Google Scholar
Tuddenham, W.J., Calvert, W.P. (1961). Visual search patterns in roentgen diagnosis. Radiology, 76, 255–256.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, 239–246.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, 341–357.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, 765–787.Google Scholar
Waite, S., Scott, J., Legasto, A., Kolla, S., Gale, B., Krupinski, E.A. (2017a). Systematic error in radiology. Am J Roentgenol, 209, 629–639.Google Scholar
Waite, S., Scott, J., Gale, B., Fuchs, T., Kolla, S., Reede, D. (2017b). Interpretive error in radiology. Am J Roentgenol, 208, 739–749.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, 27–39.Google ScholarPubMed
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, 1303–1314.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, 1057–1069.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, 284–290.Google Scholar
Yunfang, L., Wenjing, W., Bingshuang, H., et al. (2000). Visual strain and working capacity in computer operators. Homeost Health Dis, 40, 27–29.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
×