Book contents
- The Handbook of Medical Image Perception and Techniques, Second Edition
- Reviews
- The Handbook of Medical Image Perception and Techniques
- Copyright page
- Dedication
- Contents
- Contributors
- 1 Medical Image Perception
- Part I Historical Reflections and Theoretical Foundations
- Part II Science of Image Perception
- Part III Perception Metrology
- 14 Designing Perception Experiments
- 15 Receiver Operating Characteristic Analysis: Basic Concepts and Practical Applications
- 16 Multireader ROC Analysis
- 17 Memory Effects and Experimental Design
- 18 Observer Models as a Surrogate to Perception Experiments
- 19 Implementation of Observer Models
- 20 Value and Limitations of Observer Models
- Part IV Clinical Performance Assessment
- Part V Computational Perception
- Part VI Applied Perception
- Index
- References
19 - Implementation of Observer Models
from Part III - Perception Metrology
Published online by Cambridge University Press: 20 December 2018
Book contents
- The Handbook of Medical Image Perception and Techniques, Second Edition
- Reviews
- The Handbook of Medical Image Perception and Techniques
- Copyright page
- Dedication
- Contents
- Contributors
- 1 Medical Image Perception
- Part I Historical Reflections and Theoretical Foundations
- Part II Science of Image Perception
- Part III Perception Metrology
- 14 Designing Perception Experiments
- 15 Receiver Operating Characteristic Analysis: Basic Concepts and Practical Applications
- 16 Multireader ROC Analysis
- 17 Memory Effects and Experimental Design
- 18 Observer Models as a Surrogate to Perception Experiments
- 19 Implementation of Observer Models
- 20 Value and Limitations of Observer Models
- Part IV Clinical Performance Assessment
- Part V Computational Perception
- Part VI Applied Perception
- Index
- References
Summary
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- Information
- The Handbook of Medical Image Perception and Techniques , pp. 289 - 299Publisher: Cambridge University PressPrint publication year: 2018
References
Abbey, C.K., Barrett, H.H. (2001). Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. J Opt Soc Am A, 18, 473–488.Google Scholar
Barrett, H.H. (1990). Objective assessment of image quality: effects of quantum noise and object variability. J. Opt Soc Am A, 7(7), 1266–1278.Google Scholar
Barrett, H.H., Myers, K.M. (2004). Foundations of Image Science. Hoboken, NJ: John Wiley.Google Scholar
Barrett, H.H., Wilson, D.W., Tsui, B.M.W. (1994). Noise properties of the EM algorithm I: theory. Phys Med Biol, 39, 833–846.Google Scholar
Barrett, H.H., Abbey, C., Gallas, B., Eckstein, M. (1998). Stabilized estimates of Hotelling-observer detection performance in patient-structured noise. Proc SPIE Med Imag, 3340, 27–43.Google Scholar
Barrett, H.H., Myers, K.J., Gallas, B.D., Clarkson, E., Zhang, H. (2001) Megalopinakophobia: its symptoms and cures. Proc SPIE Med Imag, 4320, 299–307.CrossRefGoogle Scholar
Barrett, H.H., Myers, K.J., Devaney, N., Dainty, C. (2006). Objective assessment of image quality. IV. Application to adaptive optics. J Opt Soc Am A, 23(12), 3080–3105.Google Scholar
Brankov, J.G. (2013). Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection. Phys Med Biol, 58(20), 7159.CrossRefGoogle Scholar
Chan, H.-P., Sahiner, B., Wagner, R.F., Petrick, N. (1999). Classifier design for computer-aided diagnosis: effects of finite sample size on the measured performance of classical and neural-network classifiers, Med Phys, 26(12), 2654–2669.CrossRefGoogle Scholar
Clarkson, E. (2007). The estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks, J Opt Soc Am A, 24(12), B91–B98.Google Scholar
Ferrero, A., Favazza, C.P., Yu, L., Leng, S., McCollough, C.H. (2017). Practical implementation of channelized Hotelling observers: effect of ROI size. Proc SPIE Med Imag, 10132, 101320G.Google Scholar
Gifford, H.C. (2014). Efficient visual-search model observers for PET. Br J Radiol, 87(1039), 20140017.Google Scholar
Gifford, H.C., King, M.A., Wells, R.G. (2000). Single-photon emission computed tomography: LROC analysis of detector-response compensation in SPECT. IEEE Trans Med Imag, 19, 463–473.Google Scholar
Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (1996). Markov Chain Monte Carlo in Practice. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Gross, K., Kupinski, M.A., Peterson, T., Clarkson, E. (2003). Optimizing a multiple-pinhole SPECT system using the ideal observer. Proc SPIE Med Imag, 5034, 314–322.Google Scholar
He, X., Caffo, B.S., Frey, E.C. (2008). Toward realistic and practical ideal observer estimation for the optimization of medical imaging systems. IEEE Trans Med Imag, 27(10), 1535–1543.Google Scholar
Henkelman, R.M., Kay, I., Bronskill, M.J. (1990). Receiver operator characteristic (ROC) analysis without truth. Med Decis Making, 10, 24–29.Google Scholar
Hernandez-Giron, I., Calzado, A., Geleijns, J., Joemai, R.M.S., Veldkamp, W.J.H. (2011) Automated assessment of low contrast sensitivity for CT systems using a model observer. Med Phys, 38, S1.CrossRefGoogle ScholarPubMed
Hoppin, J.W., Kupinski, M.A., Kastis, G., Clarkson, E., Barrett, H.H. (2002). Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Trans Med Imag, 21, 441–449.Google Scholar
Khurd, P., Gindi, G. (2005). Decision strategies maximizing the area under the LROC curve SPIE Proc Med Imag, 5749, 150–161.Google Scholar
Kupinski, M.A., Hoppin, J.W., Clarkson, E., Barrett, H.H. (2002). Estimation in medical imaging without a gold standard. Acad Radiol, 9, 290–297.Google Scholar
Kupinski, M.A., Hoppin, J.W., Clarkson, E., Barrett, H.H. (2003). Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques. J Opt Soc Am A, 20(3), 430–438.CrossRefGoogle ScholarPubMed
Kupinski, M.A., Clarkson, E., Hesterman, J.Y. (2007). Bias in Hotelling observer performance computed from finite data. Proc SPIE Med Imag, 6515, 65150S-1-7.CrossRefGoogle ScholarPubMed
Lehovich, A., Gifford, H.C., King, M.A. (2008). Model observer to predict human performance in LROC studies of SPECT reconstruction using anatomical priors. Proc SPIE Med Imag, 6917, 67170R-1–7.CrossRefGoogle Scholar
McCollough, C.H. (2012). Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology, 264(2), 567–580.Google Scholar
Park, S., Kupinski, M.A., Clarkson, E. (2003). Ideal-observer performance under signal and background uncertainty. Proc Inform Process Med Imag, 18, 342–353.Google Scholar
Park, S., Clarkson, E., Kupinski, M.A., Barrett, H.H. (2005). Efficiency of the human observer detection random signal in random backgrounds J Opt Soc Am A, 22(1), 3–16.Google Scholar
Platiša, L., Goossens, B., Vansteenkiste, E., Park, S., Gallas, B.D., Badano, A., Philips, W. (2011). Channelized Hotelling observers for the assessment of volumetric imaging data sets. J Opt Soc Am A 28(6) 1145–1163.Google Scholar
Rusinek, H., Naidich, D.P., McGuinness, G., Leitman, B.S., McCauley, D.I., Krinsky, G.A., Clayton, K., Cohen, H. (1998). Pulmonary nodule detection: low-dose versus conventional CT. Radiology, 209(1), 243–249.Google Scholar
Schindera, S.T., Oddra, D., Raza, S.A., Kim, T.K., Jang, H.J., Szucs-Farkas, Z., Rogalla, P. (2013). Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved? Radiology, 269(2), 511–518.Google Scholar
Sen, A., Gifford, H. C. (2016). Accounting for anatomical noise in search-capable model observers for planar nuclear imaging. J Med Imag, 3(1), 015502.Google Scholar
Tseng, H.-W., Fan, J., Kupinski, M.A., Sainath, P., Hsieh, J. (2014). Assessing image quality and dose reduction of a new X-ray computed tomography iterative reconstruction algorithm using model observers. Med Phys, 41(7), 071910.Google Scholar
Tseng, H.-W., Fan, J., Kupinski, M.A. (2015). Combination of detection and estimation tasks using channelized scanning linear observer for CT imaging systems. Proc SPIE Med Imag, 9416, 94160H.Google Scholar
Whitaker, M.K., Clarkson, E., Barrett, H.H. (2008). Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods. Optics Express, 16(11), 8150–8173.Google Scholar
Wilson, D.W., Tsui, B.M., Barrett, H.H. (1994). Noise properties of the EM algorithm: II. Monte Carlo simulations, Phys Med Biol, 39(5), 847–871.CrossRefGoogle ScholarPubMed
Woodbury, M.A. (1950). Inverting modified matrices. In: Statistical Research Group. Princeton, NJ: Princeton University Press.Google Scholar
Wunderlich, A., Goossens, B. (2014). Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks. J Med Imag, 1(3), 031002.Google Scholar