OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 18, Iss. 3 — Mar. 1, 2001
  • pp: 473–488

Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability

Craig K. Abbey and Harrison H. Barrett  »View Author Affiliations


JOSA A, Vol. 18, Issue 3, pp. 473-488 (2001)
http://dx.doi.org/10.1364/JOSAA.18.000473


View Full Text Article

Acrobat PDF (650 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

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

© 2001 Optical Society of America

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.5010) Image processing : Pattern recognition
(110.3000) Imaging systems : Image quality assessment
(330.1880) Vision, color, and visual optics : Detection
(330.4060) Vision, color, and visual optics : Vision modeling

Citation
Craig K. Abbey and Harrison 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)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-18-3-473


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. H. H. Barrett, W. E. Smith, K. J. Myers, T. D. Milster, and R. D. Fiete, “Quantifying the performance of imaging systems,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer and R. H. Schneider, eds., Proc. SPIE 535, 65–69 (1985).
  2. 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, Dordrecht, The Netherlands, 1986), pp. 431–444.
  3. H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266–1278 (1990).
  4. H. H. Barrett, S. K. Gordon, and R. S. Hershel, “Statistical limitations in transaxial tomography,” Comput. Biol. Med. 6, 307–323 (1976).
  5. S. J. Riederer, N. J. Pelc, and D. A. Chessler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446–454 (1978).
  6. H. H. Barrett and W. Swindell, Radiological Imaging: The Theory of Image Formation, Detection, and Processing (Academic, New York, 1981).
  7. E. B. Cargill, “A mathematical liver model and its application to system optimization and texture analysis,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1989).
  8. D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler, and M. M. Goodsitt, “Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms,” in Medical Imaging: Image Processing, M. H. Loew, ed., Proc. SPIE 2434, 606–611 (1995).
  9. F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Expr. 4, 33–43 (1998).
  10. A. E. Burgess, “Mammographic structure: data preparation and spatial statistics analysis,” in Medical Imaging: Image Processing, K. M. Hanson, ed., Proc. SPIE 3661, 642–653 (1999).
  11. F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Further investigation of the phase spectrum on visual detection in structured backgrounds,” in Medical Imaging: Image Perception and Performance, E. Krupinski, ed., Proc. SPIE 3663, 273–281 (1999).
  12. F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193–205 (2000).
  13. A. E. Burgess, X. Li, and C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
  14. R. Bracewell, The Fourier Transform and Its Applications (McGraw-Hill, New York, 1965).
  15. M. F. Kijewski and P. F. Judy, “The noise power spectrum of CT images,” Phys. Med. Biol. 32, 565–575 (1987).
  16. R. F. Voss, “Fractals in nature: from characterization to simulation,” in The Science of Fractal Images, M. F. Barnsley, R. L. Devaney, and B. B. Mandelbrot, eds. (Springer-Verlag, New York, 1988).
  17. J. J. Heine, S. R. Deans, and L. P. Clarke, “Multiresolution probability analysis of random fields,” J. Opt. Soc. Am. A 16, 6–16 (1999).
  18. 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).
  19. A. E. Burgess, “Statistically defined backgrounds: performance of a modified nonprewhitening matched filter model,” J. Opt. Soc. Am. A 11, 1237–1242 (1994).
  20. A. E. Burgess, X. Li, and C. K. Abbey, “Nodule detection in two component noise: toward patient structure,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE 3036, 2–13 (1997).
  21. E. Samei, M. J. Flynn, G. H. Beue, and E. Peterson, “Comparison of observer performance for real and simulated nodules in chest radiography,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE 2712, 60–70 (1996).
  22. K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis (Academic, San Diego, Calif., 1979), pp. 62–66.
  23. P. F. Judy and R. G. Swensson, “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” Br. J. Radiol. 58, 137–145 (1985).
  24. P. F. Judy and R. G. Swensson, “Display thresholding of images and observer detection performance,” J. Opt. Soc. Am. A 4, 954–965 (1987).
  25. P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, and S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167–1174 (1992).
  26. D. Pelli, “Effects of visual noise,” Ph.D. dissertation (Cambridge U. Press, Cambridge, UK, 1981).
  27. A. E. Burgess and B. Colborne, “Visual signal detection. IV. Observer inconsistency,” J. Opt. Soc. Am. A 5, 617–627 (1988).
  28. M. P. Eckstein, A. J. Ahumada, and A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 13, 1777–1787 (1997).
  29. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for the assessment of image quality,” Proc. Natl. Acad. Sci. 90, 9758–9765 (1993).
  30. H. H. Barrett, T. A. Gooley, K. A. Girodias, J. P. Rolland, T. A. White, and J. Yao, “Linear discriminants and image quality,” Image Vis. Comput. 10, 451–460 (1992).
  31. D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Wiley, New York, 1966).
  32. N. A. Macmillan and C. D. Creelman, Detection Theory: A Users Guide, (Cambridge U. Press, New York, 1991).
  33. 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).
  34. K. J. Myers, “Visual perception in correlated noise,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1985).
  35. K. J. Myers and H. H. Barrett, “The addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457 (1987).
  36. A. E. Burgess, “The Rose model revisited,” J. Opt. Soc. Am. A 16, 633–646 (1999).
  37. L-N. Loo, K. Doi, and C. E. Metz, “A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads,” Phys. Med. Biol. 29, 837–856 (1984).
  38. M. Ishida, K. Doi, L-N. Loo, C. E. Metz, and J. L. Lehr, “Digital image processing: effect on detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
  39. L-N. Loo, K. Doi, and C. E. Metz, “Investigation of basic imaging properties in digital radiography. 4. Effect of unsharp masking on the detectability of simple patterns,” Med. Phys. 29, 209–214 (1985).
  40. K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, and G. W. Seeley, “Effect of noise correlation on the detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
  41. P. F. Judy and R. G. Swensson, “Detectability of lesions of various sizes on CT images,” in Application of Optical Instrumentation in Medicine XIII, S. J. Dwyer and R. H. Schneider, eds., Proc. SPIE 535, 38–42 (1985).
  42. P. F. Judy and R. G. Swensson, “Size discrimination of features on CT images,” in Application of Optical Instrumentation in Medicine XIV, S. J. Dwyer and R. H. Schneider, eds., Proc. SPIE 626, 225–230 (1986).
  43. P. G. J. Barten, “The SQRI method: a new method for the evaluation of visible resolution on a display,” Proc. Soc. Inf. Disp. 28, 253–262 (1987).
  44. R. D. Fiete, H. H. Barrett, W. E. Smith, and K. J. Myers, “The Hotelling trace criterion and its correlation with human observer performance,” J. Opt. Soc. Am. A 4, 945–953 (1987).
  45. F. W. Campbell, and J. G. Robson, “Application of Fourier analysis to the visibility of gratings,” J. Physiol. (London) 197, 551–566 (1968).
  46. M. B. Sachs, J. Nachmias, and J. G. Robson, “Spatial-frequency channels in human vision,” J. Opt. Soc. Am. 61, 1176–1186 (1971).
  47. N. Graham, “Complex channels, early nonlinearities, and normalization in texture segregation,” in Computational Models of Visual Processing, M. S. Landy and J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1990), pp. 273–290.
  48. C. K. Abbey, “Assessment of reconstructed images,” Ph.D. dissertation (University of Arizona, Tucson, Ariz., 1998).
  49. C. K. Abbey and F. O. Bochud, “Modeling visual detection tasks in correlated noise with linear model observers,” in Handbook of Medical Imaging, J. Beutel, H. L. Kundel, and R. L. Van Metter, eds. (SPIE Press, Bellingham, Wash., 2000), Vol. 1, pp. 629–654.
  50. H. H. Barrett, C. K. Abbey, B. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling observer performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE 3340, 27–43 (1998).
  51. C. K. Abbey and H. H. Barrett, “Linear iterative reconstruction algorithms: study of observer performance,” in Proceedings of the 14th International Conference on Information Processing in Medical Imaging, Y. Bizais, C. Barrilot, and R. Di Paola, eds. (Kluwer Academic, Dordrecht, The Netherlands, 1995), pp. 65–76.
  52. C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE 2712, 47–58 (1996).
  53. H. R. Wilson and S. C. Giese, “Threshold visibility of frequency gradient patterns,” Vision Res. 17, 1177–1190 (1977).
  54. H. Wilson and J. Bergen, “A four mechanism model for threshold spatial vision,” Vision Res. 19, 19–32 (1979).
  55. A. B. Watson, “Detection and recognition of simple spatial forms,” in Physical and Biological Processing of Images, O. J. Sander and A. J. Sleigh, eds. (Springer-Verlag, Berlin, 1983).
  56. S. Daly, “The visual differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision, A. B. Watson, ed. (MIT Press, Cambridges, Mass., 1993), pp. 179–206.
  57. M. P. Eckstein and J. S. Whiting, “Lesion detection in structured noise,” Acad. Radiol. 2, 249–253 (1995).
  58. A. E. Burgess and H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900–905 (1984).
  59. C. K. Abbey, M. P. Eckstein, and F. O. Bochud, “Estimation of human-observer templates for 2 alternative forced choice tasks,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE 3663, 284–295 (1999).
  60. C. K. Abbey and M. P. Eckstein, “Estimates of human-observer templates for simple detection tasks in correlated noise,” in Medical Imaging: Image Perception and Performance, E. A. Krupinski, ed., Proc. SPIE 3981, 70–77 (2000).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited