Abstract

We consider detection of a nodule signal profile in noisy images meant to roughly simulate the statistical properties of tomographic image reconstructions in nuclear medicine. The images have two sources of variability arising from quantum noise from the imaging process and anatomical variability in the ensemble of objects being imaged. Both of these sources of variability are simulated by a stationary Gaussian random process. Sample images from this process are generated by filtering white-noise images. Human-observer performance in several signal-known-exactly detection tasks is evaluated through psychophysical studies by using the two-alternative forced-choice method. The tasks considered investigate parameters of the images that influence both the signal profile and pixel-to-pixel correlations in the images. The effect of low-pass filtering is investigated as an approximation to regularization implemented by image-reconstruction algorithms. The relative magnitudes of the quantum and the anatomical variability are investigated as an approximation to the effects of exposure time. Finally, we study the effect of the anatomical correlations in the form of an anatomical slope as an approximation to the effects of different tissue types. Human-observer performance is compared with the performance of a number of model observers computed directly from the ensemble statistics of the images used in the experiments for the purpose of finding predictive models. The model observers investigated include a number of nonprewhitening observers, the Hotelling observer (which is equivalent to the ideal observer for these studies), and six implementations of channelized-Hotelling observers. The human observers demonstrate large effects across the experimental parameters investigated. In the regularization study, performance exhibits a mild peak at intermediate levels of regularization before degrading at higher levels. The exposure-time study shows that human observers are able to detect ever more subtle lesions at increased exposure times. The anatomical slope study shows that human-observer performance degrades as anatomical variability extends into higher spatial frequencies. Of the observers tested, the channelized-Hotelling observers best capture the features of the human data.

© 2001 Optical Society of America

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    [CrossRef]

2000 (1)

1999 (2)

1998 (2)

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
[CrossRef]

H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998).
[CrossRef]

1997 (2)

1995 (1)

M. P. Eckstein, J. S. Whiting, “Lesion detection in structured noise,” Acad. Radiol. 2, 249–253 (1995).
[CrossRef] [PubMed]

1994 (1)

1993 (1)

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

1992 (3)

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
[CrossRef] [PubMed]

J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

1990 (1)

1988 (1)

1987 (5)

1985 (3)

L-N. Loo, K. Doi, C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” Br. J. Radiol. 58, 137–145 (1985).
[CrossRef] [PubMed]

1984 (3)

L-N. Loo, K. Doi, C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
[CrossRef] [PubMed]

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

A. E. Burgess, H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900–905 (1984).
[CrossRef] [PubMed]

1979 (1)

H. Wilson, J. Bergen, “A four mechanism model for threshold spatial vision,” Vision Res. 19, 19–32 (1979).
[CrossRef] [PubMed]

1978 (1)

S. J. Riederer, N. J. Pelc, D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
[CrossRef] [PubMed]

1977 (1)

H. R. Wilson, S. C. Giese, “Threshold visibility of frequency gradient patterns,” Vision Res. 17, 1177–1190 (1977).
[CrossRef] [PubMed]

1976 (1)

H. H. Barrett, S. K. Gordon, R. S. Hershel, “Statistical limitations in transaxial tomography,” Comput. Biol. Med. 6, 307–323 (1976).
[CrossRef] [PubMed]

1971 (1)

1968 (1)

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

Abbey, C. K.

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193–205 (2000).
[CrossRef]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
[CrossRef]

H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998).
[CrossRef]

A. E. Burgess, X. Li, C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
[CrossRef]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Further investigation of the phase spectrum on visual detection in structured backgrounds,” in Medical Imaging: Image Perception and Performance, E. Krupinski, ed., Proc. SPIE3663, 273–281 (1999).
[CrossRef]

A. E. Burgess, X. Li, C. K. Abbey, “Nodule detection in two component noise: toward patient structure,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3036, 2–13 (1997).
[CrossRef]

C. K. Abbey, “Assessment of reconstructed images,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1998).

C. K. Abbey, F. O. Bochud, “Modeling visual detection tasks in correlated noise with linear model observers,” in Handbook of Medical Imaging, J. Beutel, H. L. Kundel, R. L. Van Metter, eds. (SPIE Press, Bellingham, Wash., 2000), Vol. 1, pp. 629–654.

H. H. Barrett, C. K. Abbey, B. Gallas, M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998).
[CrossRef]

C. K. Abbey, H. H. Barrett, “Linear iterative reconstruction algorithms: study of observer performance,” in Proceedings of the 14th International Conference on Information Processing in Medical Imaging, Y. Bizais, C. Barrilot, R. Di Paola, eds. (Kluwer Academic, Dordrecht, The Netherlands, 1995), pp. 65–76.

C. K. Abbey, H. H. Barrett, D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 47–58 (1996).
[CrossRef]

C. K. Abbey, M. P. Eckstein, F. O. Bochud, “Estimation of human-observer templates for 2 alternative forced choice tasks,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3663, 284–295 (1999).
[CrossRef]

C. K. Abbey, M. P. Eckstein, “Estimates of human-observer templates for simple detection tasks in correlated noise,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3981, 70–77 (2000).
[CrossRef]

Adler, D. D.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

Ahumada, A. J.

Barrett, H. H.

H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998).
[CrossRef]

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266–1278 (1990).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, “The addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457 (1987).
[CrossRef] [PubMed]

R. D. Fiete, H. H. Barrett, W. E. Smith, K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

H. H. Barrett, S. K. Gordon, R. S. Hershel, “Statistical limitations in transaxial tomography,” Comput. Biol. Med. 6, 307–323 (1976).
[CrossRef] [PubMed]

H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

H. H. Barrett, W. Swindell, Radiological Imaging: The Theory of Image Formation, Detection, and Processing (Academic, New York, 1981).

C. K. Abbey, H. H. Barrett, “Linear iterative reconstruction algorithms: study of observer performance,” in Proceedings of the 14th International Conference on Information Processing in Medical Imaging, Y. Bizais, C. Barrilot, R. Di Paola, eds. (Kluwer Academic, Dordrecht, The Netherlands, 1995), pp. 65–76.

H. H. Barrett, C. K. Abbey, B. Gallas, M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998).
[CrossRef]

C. K. Abbey, H. H. Barrett, D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 47–58 (1996).
[CrossRef]

Barten, P. G. J.

P. G. J. Barten, “The SQRI method: a new method for the evaluation of visible resolution on a display,” Proc. Soc. Inf. Disp. 28, 253–262 (1987).

Bergen, J.

H. Wilson, J. Bergen, “A four mechanism model for threshold spatial vision,” Vision Res. 19, 19–32 (1979).
[CrossRef] [PubMed]

Beue, G. H.

E. Samei, M. J. Flynn, G. H. Beue, E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 60–70 (1996).
[CrossRef]

Bibby, J. M.

K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate Analysis (Academic, San Diego, Calif., 1979), pp. 62–66.

Bochud, F. O.

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193–205 (2000).
[CrossRef]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
[CrossRef]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Further investigation of the phase spectrum on visual detection in structured backgrounds,” in Medical Imaging: Image Perception and Performance, E. Krupinski, ed., Proc. SPIE3663, 273–281 (1999).
[CrossRef]

C. K. Abbey, F. O. Bochud, “Modeling visual detection tasks in correlated noise with linear model observers,” in Handbook of Medical Imaging, J. Beutel, H. L. Kundel, R. L. Van Metter, eds. (SPIE Press, Bellingham, Wash., 2000), Vol. 1, pp. 629–654.

C. K. Abbey, M. P. Eckstein, F. O. Bochud, “Estimation of human-observer templates for 2 alternative forced choice tasks,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3663, 284–295 (1999).
[CrossRef]

Borgstrom, M. C.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
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K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

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K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

E. B. Cargill, “A mathematical liver model and its application to system optimization and texture analysis,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1989).

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D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
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Chan, K. H.

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
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Chessler, D. A.

S. J. Riederer, N. J. Pelc, D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
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Clarke, L. P.

Clarkson, E.

Clough, A. V.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

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Creelman, C. D.

N. A. Macmillan, C. D. Creelman, Detection Theory: A Users Guide, (Cambridge U. Press, New York, 1991).

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S. Daly, “The visual differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision, A. B. Watson, ed. (MIT Press, Cambridges, Mass., 1993), pp. 179–206.

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Doi, K.

L-N. Loo, K. Doi, C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
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M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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L-N. Loo, K. Doi, C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
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F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193–205 (2000).
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F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
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M. P. Eckstein, A. J. Ahumada, A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 13, 1777–1787 (1997).
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M. P. Eckstein, J. S. Whiting, “Lesion detection in structured noise,” Acad. Radiol. 2, 249–253 (1995).
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H. H. Barrett, C. K. Abbey, B. Gallas, M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998).
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C. K. Abbey, M. P. Eckstein, F. O. Bochud, “Estimation of human-observer templates for 2 alternative forced choice tasks,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3663, 284–295 (1999).
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F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Further investigation of the phase spectrum on visual detection in structured backgrounds,” in Medical Imaging: Image Perception and Performance, E. Krupinski, ed., Proc. SPIE3663, 273–281 (1999).
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R. D. Fiete, H. H. Barrett, W. E. Smith, K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
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H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Flynn, M. J.

E. Samei, M. J. Flynn, G. H. Beue, E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 60–70 (1996).
[CrossRef]

Gallas, B.

H. H. Barrett, C. K. Abbey, B. Gallas, M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998).
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H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

Goodsitt, M. M.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

Gooley, T. A.

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
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Heine, J. J.

Helvie, M. A.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
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Hershel, R. S.

H. H. Barrett, S. K. Gordon, R. S. Hershel, “Statistical limitations in transaxial tomography,” Comput. Biol. Med. 6, 307–323 (1976).
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Ishida, M.

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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Judy, P. F.

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
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M. F. Kijewski, P. F. Judy, “The noise power spectrum of CT images,” Phys. Med. Biol. 32, 565–575 (1987).
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P. F. Judy, R. G. Swensson, “Display thresholding of images and observer detection performance,” J. Opt. Soc. Am. A 4, 954–965 (1987).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” Br. J. Radiol. 58, 137–145 (1985).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Size discrimination of features on CT images,” in Application of Optical Instrumentation in Medicine XIV, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE626, 225–230 (1986).
[CrossRef]

P. F. Judy, R. G. Swensson, “Detectability of lesions of various sizes on CT images,” in Application of Optical Instrumentation in Medicine XIII , S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 38–42 (1985).
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K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate Analysis (Academic, San Diego, Calif., 1979), pp. 62–66.

Kijewski, M. F.

M. F. Kijewski, P. F. Judy, “The noise power spectrum of CT images,” Phys. Med. Biol. 32, 565–575 (1987).
[CrossRef] [PubMed]

Lehr, J. L.

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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Li, X.

A. E. Burgess, X. Li, C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
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A. E. Burgess, X. Li, C. K. Abbey, “Nodule detection in two component noise: toward patient structure,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3036, 2–13 (1997).
[CrossRef]

Loo, L-N.

L-N. Loo, K. Doi, C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
[CrossRef]

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

L-N. Loo, K. Doi, C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
[CrossRef] [PubMed]

Macmillan, N. A.

N. A. Macmillan, C. D. Creelman, Detection Theory: A Users Guide, (Cambridge U. Press, New York, 1991).

Mardia, K. V.

K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate Analysis (Academic, San Diego, Calif., 1979), pp. 62–66.

Metz, C. E.

L-N. Loo, K. Doi, C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
[CrossRef]

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

L-N. Loo, K. Doi, C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
[CrossRef] [PubMed]

Milster, T. D.

H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Myers, K. J.

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

K. J. Myers, H. H. Barrett, “The addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457 (1987).
[CrossRef] [PubMed]

R. D. Fiete, H. H. Barrett, W. E. Smith, K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

K. J. Myers, “Visual perception in correlated noise,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1985).

H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Nachmias, J.

Nawfel, R. D.

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
[CrossRef] [PubMed]

Patton, D. D.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Paxman, R. G.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Pelc, N. J.

S. J. Riederer, N. J. Pelc, D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
[CrossRef] [PubMed]

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D. Pelli, “Effects of visual noise,” Ph.D. dissertation (Cambridge U. Press, Cambridge, UK, 1981).

Peterson, E.

E. Samei, M. J. Flynn, G. H. Beue, E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 60–70 (1996).
[CrossRef]

Petrick, N.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

Riederer, S. J.

S. J. Riederer, N. J. Pelc, D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
[CrossRef] [PubMed]

Robson, J. G.

M. B. Sachs, J. Nachmias, J. G. Robson, “Spatial-frequency channels in human vision,” J. Opt. Soc. Am. 61, 1176–1186 (1971).
[CrossRef] [PubMed]

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

Rolland, J. P.

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

Sachs, M. B.

Sahiner, B.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

Samei, E.

E. Samei, M. J. Flynn, G. H. Beue, E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 60–70 (1996).
[CrossRef]

Seeley, G. W.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Seltzer, S. E.

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
[CrossRef] [PubMed]

Smith, W. E.

R. D. Fiete, H. H. Barrett, W. E. Smith, K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

Stempski, M. O.

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

Swensson, R. G.

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Display thresholding of images and observer detection performance,” J. Opt. Soc. Am. A 4, 954–965 (1987).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” Br. J. Radiol. 58, 137–145 (1985).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, “Detectability of lesions of various sizes on CT images,” in Application of Optical Instrumentation in Medicine XIII , S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 38–42 (1985).
[CrossRef]

P. F. Judy, R. G. Swensson, “Size discrimination of features on CT images,” in Application of Optical Instrumentation in Medicine XIV, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE626, 225–230 (1986).
[CrossRef]

Swets, J. A.

D. M. Green, J. A. Swets, Signal Detection Theory and Psychophysics (Wiley, New York, 1966).

Swindell, W.

H. H. Barrett, W. Swindell, Radiological Imaging: The Theory of Image Formation, Detection, and Processing (Academic, New York, 1981).

Voss, R. F.

R. F. Voss, “Fractals in nature: from characterization to simulation,” in The Science of Fractal Images, M. F. Barnsley, R. L. Devaney, B. B. Mandelbrot, eds. (Springer-Verlag, New York, 1988).

Watson, A. B.

M. P. Eckstein, A. J. Ahumada, A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 13, 1777–1787 (1997).
[CrossRef]

A. B. Watson, “Detection and recognition of simple spatial forms,” in Physical and Biological Processing of Images, O. J. Sander, A. J. Sleigh, eds. (Springer-Verlag, Berlin, 1983).

Wei, D.

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

White, T. A.

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

Whiting, J. S.

M. P. Eckstein, J. S. Whiting, “Lesion detection in structured noise,” Acad. Radiol. 2, 249–253 (1995).
[CrossRef] [PubMed]

Wilson, D. W.

C. K. Abbey, H. H. Barrett, D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 47–58 (1996).
[CrossRef]

Wilson, H.

H. Wilson, J. Bergen, “A four mechanism model for threshold spatial vision,” Vision Res. 19, 19–32 (1979).
[CrossRef] [PubMed]

Wilson, H. R.

H. R. Wilson, S. C. Giese, “Threshold visibility of frequency gradient patterns,” Vision Res. 17, 1177–1190 (1977).
[CrossRef] [PubMed]

Yao, J.

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

Acad. Radiol. (1)

M. P. Eckstein, J. S. Whiting, “Lesion detection in structured noise,” Acad. Radiol. 2, 249–253 (1995).
[CrossRef] [PubMed]

Br. J. Radiol. (1)

P. F. Judy, R. G. Swensson, “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” Br. J. Radiol. 58, 137–145 (1985).
[CrossRef] [PubMed]

Comput. Biol. Med. (1)

H. H. Barrett, S. K. Gordon, R. S. Hershel, “Statistical limitations in transaxial tomography,” Comput. Biol. Med. 6, 307–323 (1976).
[CrossRef] [PubMed]

Image Vis. Comput. (1)

H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
[CrossRef]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (15)

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

R. D. Fiete, H. H. Barrett, W. E. Smith, K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
[CrossRef] [PubMed]

A. E. Burgess, H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900–905 (1984).
[CrossRef] [PubMed]

A. E. Burgess, B. Colborne, “Visual signal detection. IV. Observer inconsistency,” J. Opt. Soc. Am. A 5, 617–627 (1988).
[CrossRef] [PubMed]

M. P. Eckstein, A. J. Ahumada, A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 13, 1777–1787 (1997).
[CrossRef]

P. F. Judy, R. G. Swensson, “Display thresholding of images and observer detection performance,” J. Opt. Soc. Am. A 4, 954–965 (1987).
[CrossRef] [PubMed]

K. J. Myers, H. H. Barrett, “The addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457 (1987).
[CrossRef] [PubMed]

A. E. Burgess, “The Rose model revisited,” J. Opt. Soc. Am. A 16, 633–646 (1999).
[CrossRef]

H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998).
[CrossRef]

H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266–1278 (1990).
[CrossRef] [PubMed]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193–205 (2000).
[CrossRef]

A. E. Burgess, X. Li, C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
[CrossRef]

J. J. Heine, S. R. Deans, L. P. Clarke, “Multiresolution probability analysis of random fields,” J. Opt. Soc. Am. A 16, 6–16 (1999).
[CrossRef]

J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

A. E. Burgess, “Statistically defined backgrounds: performance of a modified nonprewhitening matched filter model,” J. Opt. Soc. Am. A 11, 1237–1242 (1994).
[CrossRef]

J. Physiol. (London) (1)

F. W. Campbell, J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).

Med. Phys. (2)

L-N. Loo, K. Doi, C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
[CrossRef]

P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
[CrossRef] [PubMed]

Opt. Expr. (1)

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
[CrossRef]

Phys. Med. Biol. (3)

S. J. Riederer, N. J. Pelc, D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
[CrossRef] [PubMed]

M. F. Kijewski, P. F. Judy, “The noise power spectrum of CT images,” Phys. Med. Biol. 32, 565–575 (1987).
[CrossRef] [PubMed]

L-N. Loo, K. Doi, C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
[CrossRef] [PubMed]

Proc. Natl. Acad. Sci. (1)

H. H. Barrett, J. Yao, J. P. Rolland, K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
[CrossRef]

Proc. Soc. Inf. Disp. (1)

P. G. J. Barten, “The SQRI method: a new method for the evaluation of visible resolution on a display,” Proc. Soc. Inf. Disp. 28, 253–262 (1987).

Radiology (1)

M. Ishida, K. Doi, L-N. Loo, C. E. Metz, J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

Vision Res. (2)

H. R. Wilson, S. C. Giese, “Threshold visibility of frequency gradient patterns,” Vision Res. 17, 1177–1190 (1977).
[CrossRef] [PubMed]

H. Wilson, J. Bergen, “A four mechanism model for threshold spatial vision,” Vision Res. 19, 19–32 (1979).
[CrossRef] [PubMed]

Other (28)

A. B. Watson, “Detection and recognition of simple spatial forms,” in Physical and Biological Processing of Images, O. J. Sander, A. J. Sleigh, eds. (Springer-Verlag, Berlin, 1983).

S. Daly, “The visual differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision, A. B. Watson, ed. (MIT Press, Cambridges, Mass., 1993), pp. 179–206.

C. K. Abbey, M. P. Eckstein, F. O. Bochud, “Estimation of human-observer templates for 2 alternative forced choice tasks,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3663, 284–295 (1999).
[CrossRef]

C. K. Abbey, M. P. Eckstein, “Estimates of human-observer templates for simple detection tasks in correlated noise,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE3981, 70–77 (2000).
[CrossRef]

P. F. Judy, R. G. Swensson, “Detectability of lesions of various sizes on CT images,” in Application of Optical Instrumentation in Medicine XIII , S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 38–42 (1985).
[CrossRef]

P. F. Judy, R. G. Swensson, “Size discrimination of features on CT images,” in Application of Optical Instrumentation in Medicine XIV, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE626, 225–230 (1986).
[CrossRef]

N. Graham, “Complex channels, early nonlinearities, and normalization in texture segregation,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1990), pp. 273–290.

C. K. Abbey, “Assessment of reconstructed images,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1998).

C. K. Abbey, F. O. Bochud, “Modeling visual detection tasks in correlated noise with linear model observers,” in Handbook of Medical Imaging, J. Beutel, H. L. Kundel, R. L. Van Metter, eds. (SPIE Press, Bellingham, Wash., 2000), Vol. 1, pp. 629–654.

H. H. Barrett, C. K. Abbey, B. Gallas, M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998).
[CrossRef]

C. K. Abbey, H. H. Barrett, “Linear iterative reconstruction algorithms: study of observer performance,” in Proceedings of the 14th International Conference on Information Processing in Medical Imaging, Y. Bizais, C. Barrilot, R. Di Paola, eds. (Kluwer Academic, Dordrecht, The Netherlands, 1995), pp. 65–76.

C. K. Abbey, H. H. Barrett, D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 47–58 (1996).
[CrossRef]

K. J. Myers, “Visual perception in correlated noise,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1985).

D. M. Green, J. A. Swets, Signal Detection Theory and Psychophysics (Wiley, New York, 1966).

N. A. Macmillan, C. D. Creelman, Detection Theory: A Users Guide, (Cambridge U. Press, New York, 1991).

D. Pelli, “Effects of visual noise,” Ph.D. dissertation (Cambridge U. Press, Cambridge, UK, 1981).

R. F. Voss, “Fractals in nature: from characterization to simulation,” in The Science of Fractal Images, M. F. Barnsley, R. L. Devaney, B. B. Mandelbrot, eds. (Springer-Verlag, New York, 1988).

R. Bracewell, The Fourier Transform and Its Applications (McGraw-Hill, New York, 1965).

A. E. Burgess, X. Li, C. K. Abbey, “Nodule detection in two component noise: toward patient structure,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3036, 2–13 (1997).
[CrossRef]

E. Samei, M. J. Flynn, G. H. Beue, E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 60–70 (1996).
[CrossRef]

K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate Analysis (Academic, San Diego, Calif., 1979), pp. 62–66.

H. H. Barrett, W. Swindell, Radiological Imaging: The Theory of Image Formation, Detection, and Processing (Academic, New York, 1981).

E. B. Cargill, “A mathematical liver model and its application to system optimization and texture analysis,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1989).

D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE2434, 606–611 (1995).
[CrossRef]

A. E. Burgess, “Mammographic structure: data preparation and spatial statistics analysis,” in Medical Imaging: Image Processing, K. M. Hanson, ed., Proc. SPIE3661, 642–653 (1999).
[CrossRef]

F. O. Bochud, C. K. Abbey, M. P. Eckstein, “Further investigation of the phase spectrum on visual detection in structured backgrounds,” in Medical Imaging: Image Perception and Performance, E. Krupinski, ed., Proc. SPIE3663, 273–281 (1999).
[CrossRef]

H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer, R. H. Schneider, eds., Proc. SPIE535, 65–69 (1985).
[CrossRef]

K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S. L. Bacharach, ed. (Martinus Nijhoff, Dordrecht, The Netherlands, 1986), pp. 431–444.

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Figures (7)

Fig. 1
Fig. 1

Four steps in the image-generation process: (a) initial white-noise field, (b) effect of filtering to induce pixel correlations, (c) effect with a signal added (with artificially high contrast for the purposes of visibility in this figure), (d) effect of cropping the edges to the final 64×64 pixels used in the psychophysical studies.

Fig. 2
Fig. 2

Noise and signal power spectra. Plots of signal and noise power spectra as a function of radial frequencies (in pixels-1). (a) Components of the noise-power spectrum (NPS). The total NPS is the sum of the quantum and anatomical components multiplied by the filter spectrum. (b) Components of the signal spectrum. The total signal spectrum is the product of the signal spectrum and the filter spectrum. Note that the signal power is plotted on a logarithmic scale to capture the low-amplitude peaks at 0.28 and 0.40 pixels-1.

Fig. 3
Fig. 3

Channel profiles for channelized-Hotelling observers. The three plots illustrate the frequency response for the square (SQR), sparse difference-of-Gaussians (S-DOG), and dense difference-of-Gaussians (D-DOG) channel models used in this work.

Fig. 4
Fig. 4

Sample images used in the regularization study experiments. The successive smoothing of the image reflects the effect of a lower cutoff frequency. A. High-frequency cutoff in the regularizing filter (experiment 1: ρc=0.450). B. Midrange frequency cutoff in the regularizing filter (experiment 4: ρc=0.165). C. Low-frequency cutoff in the regularizing filter (experiment 7: ρc=0.060).

Fig. 5
Fig. 5

Sample images used in the exposure-time study. The higher-frequency quantum noise component is reduced as exposure time goes up. A. Low-exposure time (experiment 1: Wa/Wq=0.528). B. Midrange exposure time (experiment 3: Wa/Wq=29.70). C. High-exposure time (experiment 5: Wa/Wq=1223.7).

Fig. 6
Fig. 6

Sample images used in the anatomical-slope study. The anatomical noise extends into higher spatial frequencies as the slope is reduced. A. Steep slope (experiment 1: β=4.0). B. Midrange slope (experiment 3: β=3.0). C. Low slope (experiment 5: β=2.0).

Fig. 7
Fig. 7

Human-observer performance and model fits. Each row of plots corresponds to the named study (regularization, exposure time, and anatomical slope). Y-axis labels on the left apply across the entire row. The first column of plots shows the performance of all four subjects. For reference, average human-observer performance is plotted with 1-standard-deviation errorbars in all the model-observer plots as well. The second column gives the performance of the Hotelling observer, the nonprewhitening (NPW) observer, the region-of-interest (ROI) observer, and the eye-filtered nonprewhitening (NPW-eye) observer. The remaining columns show the performance of channelized-Hotelling observers with the square channels (SQR), the three-channel difference of Gaussians (S-DOG), and the ten-channel difference of Gaussians (D-DOG), both with and without internal noise (+noise).

Tables (3)

Tables Icon

Table 1 Parameter Settings for the Regularization Study a

Tables Icon

Table 2 Parameter Settings for the Exposure-Time Study a

Tables Icon

Table 3 Parameter Settings for the Anatomical-Slope Study a

Equations (49)

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fˆ=W(F-1ΛFu+b+δSPs),
F-1=F,
ΛB(Nq+Na)1/2,
[Nq]kk=WqρkifρkρqWqρqifρk<ρq,
[Na]kk=Wa1+(ρk/ρa)β,
[B]kk=11+(ρk/ρc)2ν.
[s0]m=As1-rm-rcR2nifrm-rcR0otherwise,
s=FBFs0.
y=Ax+c,
μy=Aμx+c,Ky=AKxA,
μfˆ=W(b+δSPs),
Kfˆ=WFΛFKu(WFΛF)
=WFΛ2FWt.
Δμfˆμfˆ|sp-μfˆ|sa
=W(b+s)-Wb
=Ws.
Δμfˆ=WFBFs0,
Kfˆ=WFB2(Nq+Na)FWt.
λ=w(fˆ)+.
oi=0ifλi+-λi-<01ifλi+-λi->0.
SNRw2=(μλ+-μλ-)212 (σλ+2+σλ-2),
dA=2Φ-1(PC),
λ=wtfˆ+.
μλ+-μλ-=wtΔμfˆ,
σλ+2=σλ-2=wtKfˆw+σ2,
SNRw2=(wtΔμfˆ)2wtKfˆw+σ2.
P^C=1Ntriali=1Ntrialoi.
d^A=2Φ-1(P^C).
[wROI]m=1if rmROI0if rmROI,
wNPW=Δμfˆ.
E(ρ)=ρηexp(-cρ2),
wNPWE=EtEΔμfˆ,
E=FΛeyeF,
[Λeye]kk=E(ρk).
(EΔμfˆ)tEfˆ=ΔμfˆtEtEfˆ=wNPWEtfˆ.
wHOT=Kfˆ-1Δμfˆ.
SNRwHOT=(ΔμfˆKfˆ-1Δμfˆ)1/2.
SNRwHOT(sfilttKufilt-1sfilt)1/2=[stF(Nq+Na)-1Fs]1/2.
u=Ttfˆ+,
wCH=T(TtKfˆT+K)-1TtΔμf˜.
σint2=ΔμfˆtT(TtKfˆT+K)-1K(TtKfˆT+K)-1TtΔμfˆ.
SNRwCH=[ΔμfˆtT(TtKfˆT+K)-1TtΔμfˆ]1/2.
tj=Fcj,
[cj]k=Cj(ρk).
Cj(ρ)=0forρρ0αj-11forρ0αj-1<ρρ0αj0for ρ>ρ0αj
Cj(ρ)=exp-12ρQσj2-exp-12ρσj2.
T=Wa/Wq.
[Na]kkWa(ρk/ρa)-β
K=cdiag(TtKfˆT),

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