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-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-23T05:29:25.207Z Has data issue: false hasContentIssue false

20 - Value and Limitations of Observer Models

from Part III - Perception Metrology

Published online by Cambridge University Press:  20 December 2018

By
Lucretiu M. Popescu
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. 300 - 304
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

Adler, R.J. (2000). On excursion sets, tube formulas and maxima of random fields. Ann Appl Probab, 10(1), 1–74.Google Scholar
Barrett, H.H., Yao, J., Rolland, J.P., Myers, K.J. (1993). Observer models for assessment of image quality. Proc Natl Acad Sci USA, 90(21), 9758–9765.Google Scholar
Bochud, F.O., Valley, J.-F., Verdun, F.R., Hessler, C., Schnyder, P. (1999). Estimation of the noisy component of anatomical backgrounds. Med Phys, 26(7), 1365–1370.Google Scholar
Chakraborty, D.P. (2006). A search model and figure of merit for observer data acquired according to the free-response paradigm. Phys Med Biol, 51, 3449–3462.Google Scholar
Chakraborty, D.P. (2013). A brief history of free-response receiver operating characteristic paradigm data analysis. Acad Radiol, 20(7), 915–919.Google Scholar
Gifford, H.C. (2014). Efficient visual-search observer models for pet. Br J Radiol, 87(1039), 20140017.Google Scholar
Hand, D.J. (1996). Statistics and the theory of measurement. J R Statist Soc A, 159, 445–492.Google Scholar
Hoeschen, C., Tischenko, O., Buhr, E., Illers, H. (2005). Comparison of technical and anatomical noise in digital thorax X-ray images. Radiat Protect Dosim, 114(1–3), 75–80.Google Scholar
Kohli, M., Prevedello, L.M., Filice, R.W., Geis, J.R. (2017). Implementing machine learning in radiology practice and research. Am J Roentgenol, 208(4), 754–760.CrossRefGoogle ScholarPubMed
Kundel, H.L., Nodine, C.F., Krupinski, E.A. (1989). Searching for lung nodules: visual dwell indicates location of false-positive and false negative decisions. Invest Radiol, 4, 472–478.Google Scholar
Myers, K.J., Barrett, H.H. (1987). Addition of a channel mechanism to the ideal-observer model. J Opt Soc Am A, 4(12), 2447–2457.Google Scholar
Popescu, L.M. (2008). Model for the detection of signals in images with multiple suspicious locations. Med Phys, 35(12), 5565–5574.Google Scholar
Popescu, L.M. (2011). Nonparametric signal detectability evaluation using an exponential transformation of FROC curve. Med Phys, 38(10), 5690–5702.Google Scholar
Popescu, L.M., Myers, K.J. (2013). CT image assessment by low contrast signal detectability evaluation with unknown signal location. Med Phys, 40(11), 111908–111910.Google Scholar
Rolland, J.P., Barrett, H.H. (1992). Effect of random background inhomogeneity on observer detection performance. J Opt Soc Am A, 9(5), 649–658.Google Scholar
Sharp, P., Barber, D.C., Brown, D.G., Burgess, A.E., Metz, C.E., Myers, K.J., Taylor, C.J., Wagner, R.F., Brooks, R., Hill, C.R., Kuhl, D.E., Smith, M.A., Wells, P., Worthington, B. (1996). Medical Imaging: The Assessment of Image Quality. ICRU Report 54. Bethesda, MD: International Commission on Radiological Units and Measurements.Google Scholar
Suppes, P., Zinnes, J.L. (1963). Basic measurement theory. In: Luce, R.L., Bush, R.R., Galanter, E. (eds.) Handbook of Mathematical Psychology, Vol. 1. New York, NY: Wiley, pp. 1–76.Google Scholar
Swets, J.A. (1973). The relative operating characteristic in psychology. Science, 183, 990–1000.Google Scholar
Wagner, R.F., Brown, D.G. (1985). Unified SNR analysis of medical imaging systems. Phys Med Biol, 30(6), 489–518.Google Scholar
Wang, S., Summers, R.M. (2012). Machine learning and radiology. Med Image Analysis, 16(5), 933–951.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
×