Abstract

Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three- dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multislice models, and a volumetric model. Four different random background statistics are considered, both Gaussian (noncorrelated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multislice models, the second proposed CHO could most closely approach the volumetric model, whereas the first new CHO seems to be least affected by the number of training samples.

© 2011 Optical Society of America

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  1. American Association of Physicists in Medicine, “Specification and acceptance testing of computed tomography scanners,” Tech. Rep. 39 (American Association of Physicists in Medicine, 1993).
  2. National Electrical Manufacturers Association (NEMA), “Performance measurements of positron emission tomographs,” NEMA NU 2-2007 (NEMA, 2007).
  3. M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
    [CrossRef] [PubMed]
  4. P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
    [CrossRef] [PubMed]
  5. 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, and 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, 1986), pp. 431–444.
  6. 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]
  7. H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).
  8. B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).
  9. A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008).
    [CrossRef] [PubMed]
  10. I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
    [CrossRef] [PubMed]
  11. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90, 9758–9765 (1993).
    [CrossRef] [PubMed]
  12. H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995).
    [CrossRef]
  13. S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
    [CrossRef]
  14. M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003).
    [CrossRef]
  15. B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003).
    [CrossRef]
  16. M. P. Eckstein, C. K. Abbey, and J. S. Whiting, “Human vs. model observers in anatomic backgrounds,” Proc. SPIE 3340, 16–26 (1998).
    [CrossRef]
  17. C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001).
    [CrossRef]
  18. J. A. Swets and R. M. Pickett, Evaluation of Diagnostic Systems: Methods from Signal Detection Theory (Academic, 1982).
  19. C. E. Metz, “Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals,” Invest. Radiol. 28, 59–63 (1993).
    [CrossRef] [PubMed]
  20. H. H. Barrett, C. K. Abbey, and 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]
  21. D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).
  22. K. J. Myers and H. H. Barrett, “Addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457(1987).
    [CrossRef] [PubMed]
  23. H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
    [CrossRef]
  24. S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
    [CrossRef]
  25. S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009).
    [CrossRef]
  26. J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
    [CrossRef]
  27. S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
    [CrossRef]
  28. S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
    [CrossRef]
  29. K. Myers, H. Barrett, M. Borgstrom, D. Patton, and G. Seeley, “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759(1985).
    [CrossRef] [PubMed]
  30. J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
    [CrossRef]
  31. J. P. Rolland and H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
    [CrossRef] [PubMed]
  32. A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
    [CrossRef] [PubMed]
  33. S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
    [CrossRef]
  34. M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
    [CrossRef]
  35. C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
    [CrossRef] [PubMed]
  36. S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
    [CrossRef]
  37. S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
    [CrossRef]
  38. H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
    [CrossRef]
  39. S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
    [CrossRef]
  40. C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
    [CrossRef]
  41. H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
    [CrossRef] [PubMed]
  42. F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4, 33–43 (1999).
    [CrossRef] [PubMed]
  43. C. Castella, K. Kinkel, F. Descombes, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, “Mammographic texture synthesis: second-generation clustered lumpy backgrounds using agenetic algorithm,” Opt. Express 16, 7595–7607 (2008).
    [CrossRef] [PubMed]
  44. E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
    [CrossRef] [PubMed]
  45. B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006).
    [CrossRef] [PubMed]
  46. L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.
  47. R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
    [CrossRef]
  48. K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989).
    [CrossRef]
  49. C. J. van den Branden Lambrecht, “A working spatio-temporal model of the human visual system for image restoration and quality assessment applications,” in 1996 Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1996), pp. 2291–2294.
    [CrossRef]

2010

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
[CrossRef]

2009

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
[CrossRef]

M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
[CrossRef] [PubMed]

S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
[CrossRef]

S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009).
[CrossRef]

2008

A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008).
[CrossRef] [PubMed]

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

C. Castella, K. Kinkel, F. Descombes, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, “Mammographic texture synthesis: second-generation clustered lumpy backgrounds using agenetic algorithm,” Opt. Express 16, 7595–7607 (2008).
[CrossRef] [PubMed]

2007

2006

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
[CrossRef] [PubMed]

B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006).
[CrossRef] [PubMed]

2005

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

2004

C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
[CrossRef] [PubMed]

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

2003

2002

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

2001

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001).
[CrossRef]

2000

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

1999

1998

1995

1993

C. E. Metz, “Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals,” Invest. Radiol. 28, 59–63 (1993).
[CrossRef] [PubMed]

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

1992

1990

1989

K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989).
[CrossRef]

1987

1985

1981

P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
[CrossRef] [PubMed]

Abbey, C. K.

Anderson, S. K.

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

Andersson, I.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Badano, A.

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
[CrossRef]

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

Bakic, P.

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

Barrett, H.

Barrett, H. H.

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
[CrossRef] [PubMed]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003).
[CrossRef]

M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003).
[CrossRef]

C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001).
[CrossRef]

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

H. H. Barrett, C. K. Abbey, and 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. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995).
[CrossRef]

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

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

S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
[CrossRef]

H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).

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, and 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, 1986), pp. 431–444.

Baydush, A.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Bochud, F. O.

Borgstrom, M.

Borgstrom, M. C.

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, and 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, 1986), pp. 431–444.

Bowsher, J.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Burgess, A. E.

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

Cargill, E. B.

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, and 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, 1986), pp. 431–444.

Castella, C.

Chen, M.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Clarkson, E.

S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009).
[CrossRef]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
[CrossRef] [PubMed]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003).
[CrossRef]

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

H. H. Barrett, C. K. Abbey, and 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]

S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
[CrossRef]

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, and 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, 1986), pp. 431–444.

Comtat, C.

C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
[CrossRef] [PubMed]

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

Dahlke, A.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

DeLong, D.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Denny, J. L.

Descombes, F.

Eckstein, M. P.

Fiete, R. D.

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, and 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, 1986), pp. 431–444.

Fukunaga, K.

K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989).
[CrossRef]

Gallas, B.

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

Gallas, B. D.

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
[CrossRef]

B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006).
[CrossRef] [PubMed]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003).
[CrossRef]

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

Gifford, H.

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

Gilland, K.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Goossens, B.

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

Green, D. M.

D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).

Hayes, R. R.

K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989).
[CrossRef]

Hooper, F. J.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Hoppin, J. W.

Ikeda, D. M.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Jacobson, F. L.

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

Jaszczak, R.

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

Judy, P. F.

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
[CrossRef] [PubMed]

Kim, J. S.

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

Kinahan, P.

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

Kinahan, P. E.

C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
[CrossRef] [PubMed]

King, M.

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

Kinkel, K.

Kupinski, M. A.

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
[CrossRef] [PubMed]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003).
[CrossRef]

S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
[CrossRef]

Lartizien, C.

C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
[CrossRef] [PubMed]

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

Lewellen, T.

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

Liang, H.

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

Lodge, M. A.

M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
[CrossRef] [PubMed]

Metz, C. E.

C. E. Metz, “Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals,” Invest. Radiol. 28, 59–63 (1993).
[CrossRef] [PubMed]

Milster, T. D.

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, and 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, 1986), pp. 431–444.

Myers, K.

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

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

Myers, K. J.

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
[CrossRef]

S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
[CrossRef]

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
[CrossRef]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995).
[CrossRef]

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

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

H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).

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, and 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, 1986), pp. 431–444.

Park, S.

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
[CrossRef]

S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009).
[CrossRef]

S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
[CrossRef]

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
[CrossRef]

Patton, D.

Patton, D. D.

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, and 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, 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, and 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, 1986), pp. 431–444.

Petrick, N. A.

Philips, W.

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

Pickett, R. M.

J. A. Swets and R. M. Pickett, Evaluation of Diagnostic Systems: Methods from Signal Detection Theory (Academic, 1982).

Platisa, L.

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

Pomerantz, S.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Pretorius, P.

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

Rahmim, A.

M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
[CrossRef] [PubMed]

A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008).
[CrossRef] [PubMed]

Rallis, D.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Reiner, B. I.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Rolland, J. P.

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

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

Ruschin, M.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Samei, E.

Saunders, R. S.

Seeley, G.

Seeley, G. W.

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, and 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, 1986), pp. 431–444.

Siegel, E. L.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Smith, W. E.

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, and 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, 1986), pp. 431–444.

Sottas, P.-E.

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, and 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, 1986), pp. 431–444.

Svahn, T.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Swensson, R. G.

P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
[CrossRef] [PubMed]

Swets, J. A.

J. A. Swets and R. M. Pickett, Evaluation of Diagnostic Systems: Methods from Signal Detection Theory (Academic, 1982).

D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).

Szulc, M.

P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
[CrossRef] [PubMed]

Timberg, P.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Tingberg, A.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

van den Branden Lambrecht, C. J.

C. J. van den Branden Lambrecht, “A working spatio-temporal model of the human visual system for image restoration and quality assessment applications,” in 1996 Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1996), pp. 2291–2294.
[CrossRef]

Vansteenkiste, E.

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

Verdun, F. R.

Wagner, R. F.

Wahl, R. L.

M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
[CrossRef] [PubMed]

Wells, R.

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

Whiting, J. S.

M. P. Eckstein, C. K. Abbey, and J. S. Whiting, “Human vs. model observers in anatomic backgrounds,” Proc. SPIE 3340, 16–26 (1998).
[CrossRef]

Witten, J. M.

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
[CrossRef]

S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
[CrossRef]

Yao, J.

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

Young, S.

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

Zackrisson, S.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

Zaidi, H.

A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008).
[CrossRef] [PubMed]

Zhang, H.

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

Acad. Radiol.

C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004).
[CrossRef] [PubMed]

H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008).
[CrossRef] [PubMed]

E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006).
[CrossRef] [PubMed]

B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006).
[CrossRef] [PubMed]

Am. J. Roentgenol.

B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).

Eur. Radiol.

I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008).
[CrossRef] [PubMed]

IEEE Trans. Med. Imag.

S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009).
[CrossRef]

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010).
[CrossRef]

S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009).
[CrossRef]

H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005).
[CrossRef]

IEEE Trans. Nucl. Sci.

R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000).
[CrossRef]

M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002).
[CrossRef]

J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989).
[CrossRef]

Invest. Radiol.

C. E. Metz, “Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals,” Invest. Radiol. 28, 59–63 (1993).
[CrossRef] [PubMed]

J. Nucl. Med.

M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A

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]

H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995).
[CrossRef]

M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003).
[CrossRef]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003).
[CrossRef]

C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001).
[CrossRef]

H. H. Barrett, C. K. Abbey, and 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]

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

S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009).
[CrossRef]

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

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007).
[CrossRef]

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

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007).
[CrossRef]

S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005).
[CrossRef]

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
[CrossRef]

Med. Phys.

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981).
[CrossRef] [PubMed]

Nucl. Med. Commun.

A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008).
[CrossRef] [PubMed]

Opt. Express

Proc. Natl. Acad. Sci. USA

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

Proc. SPIE

M. P. Eckstein, C. K. Abbey, and J. S. Whiting, “Human vs. model observers in anatomic backgrounds,” Proc. SPIE 3340, 16–26 (1998).
[CrossRef]

S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009).
[CrossRef]

S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006).
[CrossRef]

H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001).
[CrossRef]

Other

D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).

J. A. Swets and R. M. Pickett, Evaluation of Diagnostic Systems: Methods from Signal Detection Theory (Academic, 1982).

S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353.
[CrossRef]

H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).

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, and 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, 1986), pp. 431–444.

American Association of Physicists in Medicine, “Specification and acceptance testing of computed tomography scanners,” Tech. Rep. 39 (American Association of Physicists in Medicine, 1993).

National Electrical Manufacturers Association (NEMA), “Performance measurements of positron emission tomographs,” NEMA NU 2-2007 (NEMA, 2007).

L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.

C. J. van den Branden Lambrecht, “A working spatio-temporal model of the human visual system for image restoration and quality assessment applications,” in 1996 Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1996), pp. 2291–2294.
[CrossRef]

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Figures (10)

Fig. 1
Fig. 1

Four image categories: (a) WNB, (b) CNB, (c) LB, and (d) CLB. In each case, a randomly selected slice from the image volume is presented. Detailed parameters of the background images are given in Table 1.

Fig. 2
Fig. 2

Sample signal image. (a) Central slice of the signal volume, size of the slice is 64   voxels × 64   voxels , (b) contrast profile in the central slice of a simulated 3D Gaussian signal.

Fig. 3
Fig. 3

First five LG channels with the spread parameter a u = 24 . Top: images illustrate 2D channels or central slices of 3D channels. Bottom: plots of the LG functions. For 3D channels, these plots are the same in the planar view ( x y plane) as in the z direction.

Fig. 4
Fig. 4

(a) ssCHO. The model is constrained to use only the information from one slice in the volume, the slice in which the signal is centered, g ( N / 2 ) . The g ( N / 2 ) from all images is first channelized using a set of 2D LG channels, u p , p = 1 , , P , where P is the total number of channels. The vector of channel outputs v of the size P is then processed by the template w CHO to estimate the test score of the ssCHO, t. (b) vCHO. The main difference from the ssCHO model is that vCHO exploits not only a single slice from the volume but also the image volume as a whole, g = [ g ( 1 ) g ( N ) ] . Here, the channels match the dimension of the image volume and are 3D LG functions in a 3D Cartesian space. In any other aspect, the vCHO model is the same as the ssCHO model.

Fig. 5
Fig. 5

msCHO. Processing slice data with 2D LG channels. The multislice image g is represented as an array of slices g ( 1 ) , , g ( N ) , where N is the number of slices in the image. Each slice in the array g ( n ) is channelized by the same set of P 2D channels, u ( p ) , p = 1 , , P , to obtain the channel outputs v ( n ) = [ v 1 ( n ) , , v P ( n ) ] , where v p ( n ) = u ( p ) t g ( n ) . The matrix of the channel outputs for all slices in the image is denoted v msCHO = [ v ( 1 ) , v ( 2 ) , , v ( N ) ] . The same procedure applies on both signal-present and signal-absent images. The concept of ROI is explained in Subsection 3C5.

Fig. 6
Fig. 6

Three different designs of the msCHO: (a)  msCHO a , (b)  msCHO b , and (c)  msCHO c . Each observer is applied on the ROI consisting of R consecutive slices where R N and R = N corresponds to the whole image sequence. (For details about ROI, see Subsection 3C5.) First, in the early preprocessing stage, the channelized slice data v ( n ) , , v ( n + R ) is obtained, as illustrated in Fig. 5. In two out of three msCHO designs, (a) and (b), this channelized data is used to calculate the vector of test statistics, t planar = [ t ( n ) , , t ( n + R ) ] , using different templates in Eq. (8): (a) a separate 2D template w ( n ) , , w ( n + R ) is used for each slice, (b) one 2D template w planar is used for all slices in the ROI. Next, either t planar , in the case of (a) and (b), or the channelized slice data directly, in the case of (c), is used as input to the integration stage. There, all three types of msCHO use 1D HO to estimate the final test statistic, t.

Fig. 7
Fig. 7

Ideal observer performances. Top: category of white noise images, WNB. Bottom: category of colored noise images, CNB. The two curves in each graph correspond to a 2D (2D IO) and a 3D problem (3D IO). The 2D images are of size M = 64 2 with 2D Gaussian signals inserted in the center of the image, while the 3D images are of size M = 64 3 with 3D spherically symmetric Gaussian signals inserted in the center of the volume. For both 2D and 3D Gaussian signals, the value of the signal spread parameter is σ s 1 = 8 . Further details about image parameters are given in Table 1. The AUC values are obtained using Eq. (16) to calculate SNR and then Eq. (15) to calculate the AUC of the IO.

Fig. 8
Fig. 8

Plots of estimated AUC as a function of the number of channels, P = 1 , , 30 . Right: AUC for an ssCHO design using 2D LG channels applied on the central slice of the image stack. Left: AUC for a vCHO design using 3D LG channels. For both model designs, a set of different spread parameters is considered, a u = { 7 , 12 , 18 , 24 , 32 } . Top to bottom: results for WNB ( a s = 0.035 ), CNB ( a s 1 = 0.75 ), LB ( a s = 12 ), and CLB ( a s = 12 ). The plots are obtained for N tr = 2000 trainer image pairs and N ts = 1000 test image pairs. Selected channel parameters are listed in Table 2: channel spread parameter a u and number of channels P 2 D for ssCHO and P 3 D for vCHO.

Fig. 9
Fig. 9

Average AUC for the five model observer designs: ssCHO run on the central slice in the image; msCHO a , msCHO b , and msCHO c each applied on the ROI comprised of R = 11 adjacent slices centered on the central signal slice; vCHO applied on the whole image volume. Each graph corresponds to one of the four background categories (left to right, top to bottom): WNB, CNB, LB, and CLB. The value of the signal spread parameter is σ s 1 = 8 and the related signal magnitudes a s correspond to those defined in Table 1. Number of trainer image pairs per reader N tr = 2000 and number of tester pairs N ts = 1000 . Number of readers N rd corresponds to the applicable study configurations from Table 3. Error bars are ± 2 standard deviations estimated by the one-shot method.

Fig. 10
Fig. 10

For CNB image category, average AUC of the five CHO model designs and the ideal 3D IO when the value of the signal spread parameter is (top) σ s 2 = 5 and (bottom) σ s 3 = 3 . The related signal magnitudes a s correspond to those defined in Table 1. The three msCHO models are applied on the ROI comprised of R = 11 adjacent slices centered on the central signal slice. Number of trainer image pairs per reader N tr = 2000 and number of tester pairs N ts = 1000 . Number of readers N rd = 5 . Error bars are ± 2 standard deviations estimated by the one-shot method.

Tables (7)

Tables Icon

Table 1 Signal and Background Parameters a

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Table 2 Parameters of the LG Channels a

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Table 3 MRMC Study Configurations a

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Table 4 Terms of Eq. (17) for Three Different Types of Model Observer Efficiency, η

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Table 5 Efficiency of CHO Models Applied on CNB Images with Different Spread of the Signal: Efficiency of the CHO Model Relative to the IO Performance ( η CHO ) and Efficiency of ssCHO Relative to the vCHO Performance ( η ss , v ) a

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Table 6 Efficiency of Five CHO Models for Different Levels of the Signal a s while the Number of Trainer Images Increase: η N tr | a s a

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Table 7 Efficiency of msCHO Models for Different-Sized ROIs while the Number of Trainer Images Increase: η N tr | R a

Equations (21)

Equations on this page are rendered with MathJax. Learn more.

H 1   :   g = b + n ,
H 2   :   g = b + n + s .
f LB ( r ) = k = 1 K l ( r r k ) ,
l ( r ) = a b exp ( | r | 2 2 σ b 2 ) .
f CLB ( r ) = k = 1 K n = 1 N k l ( r r k r k n ) ,
Λ ( g ) = pr ( g | H 2 ) pr ( g | H 1 ) .
t ( g ) = m = 1 M w m g m ,
t ( g ) = w t g .
w HO = K g 1 Δ g ¯ ,
K g = 1 2 ( K g , 1 + K g , 2 ) ,
v = U t g ,
w CHO = K v 1 Δ v ¯ ,
u p ( r ) = 2 a u exp ( π r 2 a u 2 ) L p ( 2 π r 2 a u 2 ) ,
L p ( x ) = k = 0 p ( 1 ) p ( p k ) x k k ! .
SNR AUC = 2 erf 1 ( 2 AUC 1 ) ,
SNR λ = ( s t K 1 s ) 1 / 2 .
η = SNR curr 2 SNR ref 2 .
w ( n ) = K v ( n ) 1 Δ v ¯ ( n ) , n = 1 , , N .
t ( n ) = w ( n ) t v ( n ) , n = 1 , , N .
t ( t planar ) = ( K planar 1 Δ t ¯ planar ) t t planar = w HO a t t planar .
t ( v msCHO ) = ( K msCHO 1 Δ v ¯ msCHO ) t v msCHO = w HO c t v msCHO .

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