Abstract

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size. We evaluate its estimation and detection performance using both synthesized (correlated Gaussian) backgrounds and real clinical (magnetic resonance) backgrounds. The results suggest that the CJO has good performance in the SKS detection–estimation task.

© 2013 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).
  2. J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
    [CrossRef]
  3. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for assessment of image quality,” Proc. Nat. Acad. Sci. USA 90, 9758–9765 (1993). .
  4. L. Platisa, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A 28, 1145–1163 (2011).
    [CrossRef]
  5. C. Abbey and J. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in Digital Mammography (Springer, 2010), pp. 393–400.
  6. R. F. W. Harrison, H. H. Barrett, J. L. Denny, 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]
  7. 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]
  8. 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]
  9. C. Lartizien, P. 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]
  10. 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]
  11. M. Whitaker, E. Clarkson, and H. H. Barrett, “Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods,” Opt. Express 16, 8150 (2008).
    [CrossRef]
  12. R. M. Manjeshwar and D. L. Wilson, “Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences,” J. Opt. Soc. Am. A 18, 78–85 (2001).
    [CrossRef]
  13. 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]
  14. M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91–102 (2001).
    [CrossRef]
  15. M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
    [CrossRef]
  16. M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
    [CrossRef]
  17. Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
    [CrossRef]
  18. H. C. Gifford and M. A. King, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imaging 24, 160–169 (2005).
    [CrossRef]
  19. H. Gifford and M. King, “Implementing visual search in human-model observers for emission tomography,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE (IEEE, 2009), pp. 2482–2485.
  20. H. Gifford and M. King, “EM clustering for holistic search in human-model observers,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE (IEEE, 2010), pp. 3584–3587.
  21. E. Clarkson, “Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks,” J. Opt. Soc. Am. A 24, B91–B98 (2007).
    [CrossRef]
  22. G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
    [CrossRef]
  23. B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
    [CrossRef]
  24. B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).
  25. L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
    [CrossRef]
  26. 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]
  27. 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 a genetic algorithm,” Opt. Express 16, 7595–7607 (2008).
    [CrossRef]
  28. L. Zhang, C. Cavaro-Ménard, and P. L. Callet, “Key issues and specificities for the objective medical image quality assessment,” in Sixth International Workshop on Video Processing and Quality Metrics (VPQM) (2012), Vol. 7966.
  29. B. Goossens, “Multiresolution image models and estimation techniques,” Ph.D. dissertation (Ghent University, 2010).
  30. R. Bellman, Adaptive Control Processes: A Guided Tour (Princeton University, 1961), Vol. 4.
  31. J. A. Swets, Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers (Lawrence Erlbaum Associates, 1996).
  32. A. Tihonov, “Solution of incorrectly formulated problems and the regularization method,” Sov. Math. Dokl. 4, 1035–1038 (1963).
  33. B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006).
    [CrossRef]
  34. M. Anderson and W. Woessner, Applied Groundwater Modeling: Simulation of Flow and Advective Transport, 2nd ed. (Academic, 1992).
  35. J. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: empirical comparisons,” Int. J. Forecasting 8, 69–80 (1992).
    [CrossRef]

2012 (1)

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

2011 (1)

2010 (1)

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[CrossRef]

2009 (2)

2008 (2)

2007 (1)

2006 (2)

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]

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

2005 (2)

H. C. Gifford and M. A. King, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imaging 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 (2)

C. Lartizien, P. 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]

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

2003 (2)

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (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]

2002 (1)

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
[CrossRef]

2001 (3)

2000 (1)

G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
[CrossRef]

1995 (1)

1993 (1)

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

1992 (1)

J. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: empirical comparisons,” Int. J. Forecasting 8, 69–80 (1992).
[CrossRef]

1963 (1)

A. Tihonov, “Solution of incorrectly formulated problems and the regularization method,” Sov. Math. Dokl. 4, 1035–1038 (1963).

Abbey, C.

C. Abbey and J. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in Digital Mammography (Springer, 2010), pp. 393–400.

Abbey, C. K.

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. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
[CrossRef]

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
[CrossRef]

M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91–102 (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]

Anderson, M.

M. Anderson and W. Woessner, Applied Groundwater Modeling: Simulation of Flow and Advective Transport, 2nd ed. (Academic, 1992).

Armstrong, J.

J. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: empirical comparisons,” Int. J. Forecasting 8, 69–80 (1992).
[CrossRef]

Badano, A.

Barbara, S.

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

Barrett, H. H.

M. Whitaker, E. Clarkson, and H. H. Barrett, “Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods,” Opt. Express 16, 8150 (2008).
[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]

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]

R. F. W. Harrison, H. H. Barrett, J. L. Denny, 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. Nat. Acad. Sci. USA 90, 9758–9765 (1993). .

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

Bellman, R.

R. Bellman, Adaptive Control Processes: A Guided Tour (Princeton University, 1961), Vol. 4.

Bochud, F. O.

Boone, J.

C. Abbey and J. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in Digital Mammography (Springer, 2010), pp. 393–400.

Brankov, J.

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Callet, P. L.

L. Zhang, C. Cavaro-Ménard, and P. L. Callet, “Key issues and specificities for the objective medical image quality assessment,” in Sixth International Workshop on Video Processing and Quality Metrics (VPQM) (2012), Vol. 7966.

Castella, C.

Cavaro-Ménard, C.

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

L. Zhang, C. Cavaro-Ménard, and P. L. Callet, “Key issues and specificities for the objective medical image quality assessment,” in Sixth International Workshop on Video Processing and Quality Metrics (VPQM) (2012), Vol. 7966.

Clarkson, E.

Collopy, F.

J. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: empirical comparisons,” Int. J. Forecasting 8, 69–80 (1992).
[CrossRef]

Comtat, C.

C. Lartizien, P. 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]

Denny, J. L.

Descombes, F.

Eckstein, M.

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

Eckstein, M. P.

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]

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 a genetic algorithm,” Opt. Express 16, 7595–7607 (2008).
[CrossRef]

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
[CrossRef]

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
[CrossRef]

M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91–102 (2001).
[CrossRef]

El Naqa, I.

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Gallas, B. D.

Gifford, H.

H. Gifford and M. King, “Implementing visual search in human-model observers for emission tomography,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE (IEEE, 2009), pp. 2482–2485.

H. Gifford and M. King, “EM clustering for holistic search in human-model observers,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE (IEEE, 2010), pp. 3584–3587.

Gifford, H. C.

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

Goossens, B.

L. Platisa, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A 28, 1145–1163 (2011).
[CrossRef]

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[CrossRef]

B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).

B. Goossens, “Multiresolution image models and estimation techniques,” Ph.D. dissertation (Ghent University, 2010).

Harrison, R. F. W.

Kinahan, P.

C. Lartizien, P. 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]

King, M.

H. Gifford and M. King, “Implementing visual search in human-model observers for emission tomography,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE (IEEE, 2009), pp. 2482–2485.

H. Gifford and M. King, “EM clustering for holistic search in human-model observers,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE (IEEE, 2010), pp. 3584–3587.

King, M. A.

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

Kinkel, K.

Kupinski, M. A.

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]

Lartizien, C.

C. Lartizien, P. 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]

Le Callet, P.

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

Magli, E.

G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
[CrossRef]

Manjeshwar, R. M.

Myers, K. J.

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]

R. F. W. Harrison, H. H. Barrett, J. L. Denny, 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. Nat. Acad. Sci. USA 90, 9758–9765 (1993). .

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

Olmo, G.

G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
[CrossRef]

Park, S.

Pham, B.

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
[CrossRef]

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
[CrossRef]

Pham, B. T.

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

Philips, E.

B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).

Philips, W.

L. Platisa, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A 28, 1145–1163 (2011).
[CrossRef]

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[CrossRef]

Platisa, L.

Platiša, L.

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[CrossRef]

B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).

Presti, L. L.

G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
[CrossRef]

Rolland, J. P.

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

Samei, E.

Saunders, R. S.

Sottas, P.-E.

Swets, J. A.

J. A. Swets, Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers (Lawrence Erlbaum Associates, 1996).

Tanguy, J.

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

Tihonov, A.

A. Tihonov, “Solution of incorrectly formulated problems and the regularization method,” Sov. Math. Dokl. 4, 1035–1038 (1963).

Vansteenkiste, E.

L. Platisa, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A 28, 1145–1163 (2011).
[CrossRef]

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[CrossRef]

Vansteenkiste, W.

B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).

Verdun, F. R.

Wei, L.

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Wernick, M.

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Whitaker, M.

Wilson, D. L.

Woessner, W.

M. Anderson and W. Woessner, Applied Groundwater Modeling: Simulation of Flow and Advective Transport, 2nd ed. (Academic, 1992).

Yang, Y.

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Yao, J.

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

Zhang, L.

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

L. Zhang, C. Cavaro-Ménard, and P. L. Callet, “Key issues and specificities for the objective medical image quality assessment,” in Sixth International Workshop on Video Processing and Quality Metrics (VPQM) (2012), Vol. 7966.

Zhang, Y.

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
[CrossRef]

Acad. Radiol. (2)

C. Lartizien, P. 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]

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

IEEE Trans. Med. Imaging (4)

L. Zhang, C. Cavaro-Ménard, P. Le Callet, and J. Tanguy, “A perceptually relevant channelized joint observer (PCJO) for the detection–localization of parametric signals,” IEEE Trans. Med. Imaging 31, 1875–1888 (2012).
[CrossRef]

J. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. Med. Imaging 28, 991–999 (2009).
[CrossRef]

Y. Zhang, B. T. Pham, M. Eckstein, and S. Barbara, “Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in x-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 613–632 (2004).
[CrossRef]

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

Int. J. Forecasting (1)

J. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: empirical comparisons,” Int. J. Forecasting 8, 69–80 (1992).
[CrossRef]

J. Opt. Soc. Am. A (8)

E. Clarkson, “Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks,” J. Opt. Soc. Am. A 24, B91–B98 (2007).
[CrossRef]

R. M. Manjeshwar and D. L. Wilson, “Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences,” J. Opt. Soc. Am. A 18, 78–85 (2001).
[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]

L. Platisa, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A 28, 1145–1163 (2011).
[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]

R. F. W. Harrison, H. H. Barrett, J. L. Denny, 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]

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]

Opt. Express (2)

Proc. Nat. Acad. Sci. USA (1)

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

Proc. SPIE (5)

M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91–102 (2001).
[CrossRef]

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13–24 (2002).
[CrossRef]

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123–134 (2003).
[CrossRef]

B. Goossens, L. Platiša, E. Vansteenkiste, and W. Philips, “The use of steerable channels for detecting asymmetrical signals with random orientations,” Proc. SPIE 7627, 76270S (2010).
[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]

Signal Process. (1)

G. Olmo, E. Magli, and L. L. Presti, “Joint statistical signal detection and estimation. Part I: theoretical aspects of the problem,” Signal Process. 80, 57–73 (2000).
[CrossRef]

Sov. Math. Dokl. (1)

A. Tihonov, “Solution of incorrectly formulated problems and the regularization method,” Sov. Math. Dokl. 4, 1035–1038 (1963).

Other (10)

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

M. Anderson and W. Woessner, Applied Groundwater Modeling: Simulation of Flow and Advective Transport, 2nd ed. (Academic, 1992).

B. Goossens, L. Platiša, W. Vansteenkiste, and E. Philips, “Design of model observers for joint detection and estimation of random parametric signals in images” (in preparation).

L. Zhang, C. Cavaro-Ménard, and P. L. Callet, “Key issues and specificities for the objective medical image quality assessment,” in Sixth International Workshop on Video Processing and Quality Metrics (VPQM) (2012), Vol. 7966.

B. Goossens, “Multiresolution image models and estimation techniques,” Ph.D. dissertation (Ghent University, 2010).

R. Bellman, Adaptive Control Processes: A Guided Tour (Princeton University, 1961), Vol. 4.

J. A. Swets, Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers (Lawrence Erlbaum Associates, 1996).

H. Gifford and M. King, “Implementing visual search in human-model observers for emission tomography,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE (IEEE, 2009), pp. 2482–2485.

H. Gifford and M. King, “EM clustering for holistic search in human-model observers,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE (IEEE, 2010), pp. 3584–3587.

C. Abbey and J. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in Digital Mammography (Springer, 2010), pp. 393–400.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1.
Fig. 1.

Example of MR image with MS lesions.

Fig. 2.
Fig. 2.

Example of the filters for the amplitude–orientation–scale-unknown case when the number of steerable channels K=3 and the number of scale-shiftable channels J=4.

Fig. 3.
Fig. 3.

AUCs and NRMSEs as a function of (K,J) for the CGB when b=1.5, 3, 4.

Fig. 4.
Fig. 4.

First row: examples of WMRs. Second row: WMRs plus simulated lesions.

Fig. 5.
Fig. 5.

AUCs and NRMSEs as a function of (K,J) for the real clinical background when b=1.5, 3, 4.

Equations (46)

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

Hk:g=b+n+kx,k=0,1,
bN(μb,Σb),
[x]p=aexp(12(pq)tBtD1B(pq)),
D=[bσ200σ2]B=[cosθsinθsinθcosθ].
α=[a,θ,b,σ,q].
[x]p=fα(p),
(α,Hk^)=argmaxα,HkP(α,Hk|g)=argmaxα,HkP(g|α,Hk)P(α)P(Hk)P(g)=argmaxα,HkP(g|α,Hk)P(α)P(Hk),
P(g|α,Hk)=1(2π)M|Σb|·exp{12(gkxα)tΣb1(gkxα)}.
(α,Hh^)=argmaxα,Hk{lnP(Hh)+lnP(α)12(ghxα)tΣb1(ghxα)}=argmaxα,Hh{lnP(Hh)+lnP(α)12(gtΣb1ghxαtΣb1ghgtΣb1xα+h2xαtΣb1xα)}=argmaxα,Hh{lnP(Hh)+lnP(α)12(hxαtΣb1ghxαtΣb1g+h2xαtΣb1xα)}=argmaxα,Hh{lnP(Hh)+lnP(α)+hxαtΣb1(g12hxα)}.
α^=argmaxαP(α|g)=argmaxαmax{P(g|xα,H1)P(α)P(H1),P(g|xα,H0)P(α)P(H0)}=argmaxαmax{P(g|xα,H1)P(α)P(H1),P(g|H0)P(α)P(H0)}.
α^=argmaxα{max{xαtΣb1(g12xα),0}},
αt=argmaxα{xαtΣb1(g12xα)};
α^={αtifxαttΣb1(g12xαt)>0any value in the space ofαelse.
P(Hk|α^,g)=P(Hk)P(α^)P(g|Hk,α^)P(g|α^)P(α^)P(Hk)P(g|Hk,α^)k=0,1,
P(H1|α^,g)>P(H0|α^,g)
lnP(H1)+lnP(g|H1,α^)H0H1lnP(H0)+lnP(g|H0,α^)12(gxα^)tΣb1(gxα^)+12gtΣb1gH0H1lnP(H0)P(H1)λ=xα^tΣb1(g12xα^)H0H1lnP(H0)P(H1).
αt=argmaxαxαtUαF2(Uα(Σb)1Uαt)(g12xα),
Uα=AαtU=U(Aα)t,
αt=argmaxα1U(Aα)tF2(x0)t(Σb)1(Aαg12x0)
α^=f(αt)
λ=1U(Aα^)tF2(x0)t(Σb)1(Aα^g12x0),
Aa=a1I.
fθk(φ)=(K1)!2K1K(2K2)!(cos(φθk))K1,
k=1K|fθk(φ)|2=1.
fσj(ω)=sinc(sign(ω)log2(|2σjω|π))|ω|<π,
j=+|fσj(ω)|2=1.
fθk,σj(ω,φ)=fθk(φ)fσj(ω);k=1,,K;j=1,,J.
[U]m,n=02π0+fθk(φ)fσj(ω)·exp{2πi[(m1M2)ωcosφ+(m2M2)ωsinφ]}ωdφdω
[Aθ]m,n=1Ksin(π(mn)θK)sin(π(mn)/Kθ)m,n=1,,K;
[Aσ]m,n=sinc((mn)(log2σ))m,n=1,,J;
sinc(t)={1t=0sin(πt)πtt0.
Aα=Aα,θ,σ=1aAσAθ.
(Aθ)tAθ=Aθ(Aθ)t=I.
U(Aα)tF2=1a2U(AσAθ)tF2,
U(AσAθ)tF2=trace((AσAθ)UtU(AσAθ)t)=trace(UtU(AσAθ)t(AσAθ))=trace(UtU((Aσ)t(Aθ)t)(AσAθ))=trace(UtU[((Aσ)tAσ)((Aθ)tAθ)])=trace(UtU[((Aσ)tAσ)I]).
i=1Jtrace(j=1JsjiTij)=i=1Jtrace(j=1JsijTij)=i=1Jj=1Jtrace(sjiTij)=i=1Jj=1Jsijtrace(Tij).
x^0=Aa,θ,σxα=Aa,θ,σ(Utxα)
w=(Σ^b)1x^0,
Σ^b=12(gg|H0)(gg|H0)t|H0+12[(ggα)(gxα)|H1]·[(gxα)(gxα)|H1]t|H1,
g=Utg.
λ=maxa,θ,σ(λa,θ,σ)=maxa,θ,σ(wtU(Aa,θ,σ)tF2(Aa,a,θ,σg12x^0)).
Σ^b·w=x^0,
minw{12Σ^b·wx^02+12ηw2},
Σ^b=VDVt,
w=Σx^0;
Σ=(Σ^tΣ^+ηI)1Σ^t=V[d1η+d12dpη+dp2]Vt,

Metrics