Abstract

Realistic anatomical images are useful for assessment and improvement of medical image quality. The use of synthesized images has the advantage of providing the user with a large number of independent samples, in a controlled environment. We propose a method to generate medical textures that are fully defined by a set of adjustable parameters and a random number generator, and which statistical properties are analytically tractable. This method, called the “clustered lumpy background”, is a generalization of the original lumpy background described by Rolland and Barrett (1992). A detailed application of the method in the case of mammography is presented. It is shown that the synthesized images are visually very similar and that their first and second order statistics can be considered as being equivalent.

© 1999 Optical Society of America

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  1. A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
    [CrossRef] [PubMed]
  2. H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
    [CrossRef] [PubMed]
  3. P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
    [CrossRef] [PubMed]
  4. G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).
  5. J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
    [CrossRef]
  6. J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
    [PubMed]
  7. T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
    [PubMed]
  8. E. Samei, M. J. Flynn, and W. R. Eyler, “Simulation of subtle lung Nodules in projection chest radiography,” Radiology 202, 117–124 (1997).
    [PubMed]
  9. A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
    [CrossRef] [PubMed]
  10. W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
    [CrossRef] [PubMed]
  11. C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
    [CrossRef] [PubMed]
  12. R. F. Wagner and K. E. Weaver, “An assortment of image quality indices for radiographic film-screen combinations - can they be resolved?,” proceedings SPIE 35, 83–94 (1972).
    [CrossRef]
  13. A. E. Burgess, “Statistically defined backgrounds: Performance of a modified nonprewhitening observer model,” J. Opt. Soc. Am. A 11, 1237–1242 (1994).
    [CrossRef]
  14. M. P. Eckstein, C. K. Abbey, and J. S. Whiting, “Human versus model observers in anatomic backgrounds,” proceedings SPIE 3340, 16–26 (1998).
    [CrossRef]
  15. 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]
  16. A. E. Burgess and H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900–905 (1984).
    [CrossRef] [PubMed]
  17. K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, and G. W. Seeley, “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
    [CrossRef] [PubMed]
  18. M. P. Eckstein and J. S. Whiting, “Visual signal detection in structured backgrounds. I. Effect of number of possible spatial locations and signal contrast,” J. Opt. Soc. Am. A 13, 1777–1787 (1996).
    [CrossRef]
  19. 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]
  20. C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” proceedings SPIE 2712, 47–58 (1996).
    [CrossRef] [PubMed]
  21. J. P. Rolland and R. N. Strickland, “An approach to the synthesis of biological tissue,” Opt. Express 1, 414–423 (1997). http://epubs.osa.org/oearchive/source/2850.htm
    [CrossRef] [PubMed]
  22. E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).
  23. B. Picinbono, Random Signals and systems (Prentice Hall International, 1993), p.182.
  24. A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.453.
  25. A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.419.
  26. J. P. Rolland, Factors influencing lesion detection in medical imaging (Ph.D. dissertation, University of Arizona, 1990).
  27. F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
    [CrossRef]
  28. 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]
  29. 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).
    [CrossRef]
  30. J. C. Dainty and R. Shaw, Image Science (Academic, London, 1974).

1998 (2)

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

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

1997 (3)

1996 (4)

M. P. Eckstein and J. S. Whiting, “Visual signal detection in structured backgrounds. I. Effect of number of possible spatial locations and signal contrast,” J. Opt. Soc. Am. A 13, 1777–1787 (1996).
[CrossRef]

C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” proceedings SPIE 2712, 47–58 (1996).
[CrossRef] [PubMed]

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

1995 (4)

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[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]

1994 (5)

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

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
[CrossRef] [PubMed]

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

1992 (2)

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

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]

1990 (1)

1985 (1)

1984 (1)

1974 (1)

G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).

1972 (1)

R. F. Wagner and K. E. Weaver, “An assortment of image quality indices for radiographic film-screen combinations - can they be resolved?,” proceedings SPIE 35, 83–94 (1972).
[CrossRef]

Abbey, C. K.

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

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).
[CrossRef]

C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” proceedings SPIE 2712, 47–58 (1996).
[CrossRef] [PubMed]

Adelson, E. H.

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

Adler, D. D.

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

Adler, D.D.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

Allison, J. W.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Barr, L.L.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Barrett, H. H.

Bassett, L. W.

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

Berg, G. P.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Bochud, F. O.

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[CrossRef]

Borgstrom, M. C.

Burgess, A. E.

Caligiuri, P.

P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
[CrossRef] [PubMed]

Chan, H. P.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

Dainty, J. C.

J. C. Dainty and R. Shaw, Image Science (Academic, London, 1974).

Denny, J. L.

Doi, K.

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

Eckstein, M. P.

Eyler, W. R.

E. Samei, M. J. Flynn, and W. R. Eyler, “Simulation of subtle lung Nodules in projection chest radiography,” Radiology 202, 117–124 (1997).
[PubMed]

Favus, M.

P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
[CrossRef] [PubMed]

Flynn, M. J.

E. Samei, M. J. Flynn, and W. R. Eyler, “Simulation of subtle lung Nodules in projection chest radiography,” Radiology 202, 117–124 (1997).
[PubMed]

Freeman, W. T.

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

Garra, B. S.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Ghandeharian, H.

Giger, M. L.

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
[CrossRef] [PubMed]

Gold, R. H.

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

Goodsitt, M. M.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

Graber, M. A.

G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).

Heeger, D. J.

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

Helvie, M. A.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

Hessler, C.

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[CrossRef]

Katsuragawa, S.

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

Kimme-Smith, C.

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

Kobayashi, T.

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

Krasner, B. H.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Kundel, H. L.

G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).

Lado, M. J.

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

Li, X.

MacMahon, H.

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

Massoth, R. J.

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

McCombs, M.

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

Mendez, A. J.

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

Metz, C. E.

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

Monnier-Cholley, L.

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

Moshita, J.

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

Myers, K. J.

Nishikawa, R. M.

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

Papoulis, A.

A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.453.

A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.419.

Patton, D. D.

Petrick, N.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

Petrosian, A.

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

Picinbono, B.

B. Picinbono, Random Signals and systems (Prentice Hall International, 1993), p.182.

Revesz, G.

G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).

Rolland, J. P.

Sahiner, B.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

Samei, E.

E. Samei, M. J. Flynn, and W. R. Eyler, “Simulation of subtle lung Nodules in projection chest radiography,” Radiology 202, 117–124 (1997).
[PubMed]

Schmidt, R. A.

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

Seeley, G. W.

Shaw, R.

J. C. Dainty and R. Shaw, Image Science (Academic, London, 1974).

Simoncelli, E. P.

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

Souto, M.

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

Strickland, R. N.

Tahoces, P. G.

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

Valley, J. F.

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[CrossRef]

Verdun, F. R.

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[CrossRef]

Vidal, J. J.

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

Wagner, R. F.

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]

R. F. Wagner and K. E. Weaver, “An assortment of image quality indices for radiographic film-screen combinations - can they be resolved?,” proceedings SPIE 35, 83–94 (1972).
[CrossRef]

Weaver, K. E.

R. F. Wagner and K. E. Weaver, “An assortment of image quality indices for radiographic film-screen combinations - can they be resolved?,” proceedings SPIE 35, 83–94 (1972).
[CrossRef]

Wei, W.

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

Whiting, J. S.

Wilson, D. W.

C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” proceedings SPIE 2712, 47–58 (1996).
[CrossRef] [PubMed]

Wu, Y.

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

Xu, X. W.

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

Zhang, W.

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

Am. J. Roentgenol. (1)

G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Am. J. Roentgenol. 9, 479–486 (1974).

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

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

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

A. E. Burgess, 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).
[CrossRef]

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

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]

M. P. Eckstein and J. S. Whiting, “Visual signal detection in structured backgrounds. I. Effect of number of possible spatial locations and signal contrast,” J. Opt. Soc. Am. A 13, 1777–1787 (1996).
[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]

Med. Phys. (5)

P. Caligiuri, M. L. Giger, and M. Favus, “Multifractal radiographic analysis of osteoporosis,” Med. Phys. 21, 503–508 (1994).
[CrossRef] [PubMed]

J. Moshita, K. Doi, S. Katsuragawa, L. Monnier-Cholley, and H. MacMahon, “Computer aided diagnostic for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures,” Med. Phys. 22, 1515–1523 (1995).
[CrossRef]

A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964, (1998).
[CrossRef] [PubMed]

W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Med. Phys. 21, 517–524 (1994).
[CrossRef] [PubMed]

C. Kimme-Smith, M. McCombs, R. H. Gold, and L. W. Bassett, “Mammography fixed grid versus reciprocating grid: Evaluation using cadaveric breasts as test objects,” Med. Phys. 23, 141–147 (1996).
[CrossRef] [PubMed]

Opt. Express (1)

Phys. Med. Biol. (2)

A. Petrosian, H. P. Chan, M. A. Helvie, M. M. Goodsitt, and D. D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis,” Phys. Med. Biol. 39, 2273–2288 (1994).
[CrossRef] [PubMed]

H. P. Chan, W. Wei, M. A. Helvie, B. Sahiner, D.D. Adler, M. M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture space,” Phys. Med. Biol. 40, 857–876 (1995).
[CrossRef] [PubMed]

proceedings SPIE (4)

F. O. Bochud, F. R. Verdun, C. Hessler, and J. F. Valley, “Detectability on radiological images: The effect of the anatomical noise,” proceedings SPIE 2436, 156–164 (1995).
[CrossRef]

C. K. Abbey, H. H. Barrett, and D. W. Wilson, “Observer signal-to-noise ratios for the ML-EM algorithm,” proceedings SPIE 2712, 47–58 (1996).
[CrossRef] [PubMed]

R. F. Wagner and K. E. Weaver, “An assortment of image quality indices for radiographic film-screen combinations - can they be resolved?,” proceedings SPIE 35, 83–94 (1972).
[CrossRef]

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

RadioGraphics (1)

J. W. Allison, L.L. Barr, R. J. Massoth, G. P. Berg, B. H. Krasner, and B. S. Garra, “Understanding the process of quantitative ultrasonic tissue characterization,” RadioGraphics 14, 1099–1108 (1994).
[PubMed]

Radiology (2)

T. Kobayashi, X. W. Xu, H. MacMahon, C. E. Metz, and K. Doi, “Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs,” Radiology 199, 843–848 (1996).
[PubMed]

E. Samei, M. J. Flynn, and W. R. Eyler, “Simulation of subtle lung Nodules in projection chest radiography,” Radiology 202, 117–124 (1997).
[PubMed]

Trans. on Info. Theory, Special Issue on Wavelets (1)

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multi-scale transforms,” Trans. on Info. Theory, Special Issue on Wavelets 38, 587–607 (1992).

Other (5)

B. Picinbono, Random Signals and systems (Prentice Hall International, 1993), p.182.

A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.453.

A. Papoulis, Probability, random variables, and stochastic processes (McGraw-Hill, Inc, 1991), p.419.

J. P. Rolland, Factors influencing lesion detection in medical imaging (Ph.D. dissertation, University of Arizona, 1990).

J. C. Dainty and R. Shaw, Image Science (Academic, London, 1974).

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Figures (3)

Fig. 1.
Fig. 1.

Measured power spectra of real mammograms (extreme values I and II; as measured on images of pixel size = 0.04mm, image size = 1024 pixels) and of CLB images (as measured on images of pixel size = 0.3mm, image size = 128 pixels). These spectra are averaged over all angles and displayed versus radial spatial frequency

Fig. 2.
Fig. 2.

Definition of the chosen blob. (a) Profile of the blob defined by Eq. (8) with a rotation angle (θ) set to 45°. (b) The characteristic length L in Eq. (8) is equal to the “radius” of the ellipse having half-axes Lx and Ly.

Fig. 3.
Fig. 3.

Examples of simulated (row a) and real mammograms (row b). Pixel size: 0.3mm. 128×128 pixels.

Tables (3)

Tables Icon

Table 1. Definition of the variables involved in the description of the CLB.

Tables Icon

Table 2. Parameters of the clustered lumpy background for a 128×128 pixel image.

Tables Icon

Table 3. First order statistics of real and synthesized mammograms (the variation indicates ± one standard deviation).

Equations (27)

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h ( r ) = { 1 π sin π r r w + ( 1 r r w ) cos π r r w r r w 0 r > r w ,
W window ( f ) = h ˜ ( f f 1 ) 2 W true ( f 1 ) d f 1 ,
g ( r ) = k = 1 K b ( r r k ) ,
g ( r ) = k = 1 K n = 1 N k b ( 1 a kn ( r r k r kn ) , R θ kn ) ,
W ( f ) = K ¯ N ¯ A ( W b ( f ) + N ¯ W s ( f ) ) ,
W b ( f ) = 1 2 π 0 2 π b ˜ θ ( f ) 2 ,
W s ( f ) = 1 2 π 0 2 π ϕ ˜ ( f ) 2 b ˜ θ ( f ) 2 ,
b ( r , R θ ) = exp ( α R θ r β L ( R θ r ) ) ,
g ( r ) = k = 1 K n = 1 N k b ( r r k r kn , R θ k ) r k , r kn , θ k N K
= k = 1 K n = 1 1 2 πA N k 0 2 π k A d r k Θ d r kn ϕ ( r kn ) b ( r r k r kn , R θ k ) N K
= k = 1 K n = 1 N k I b A N K = K ¯ N ¯ I b A ,
c ( r , r′ ) = g ( r ) g ( r′ ) g ( r ) g ( r′ ) .
g ( r ) g ( r′ ) = k = 1 K n = 1 N k k′ = 1 K n′ = 1 N k′ b ( r r k r kn , R θ k ) b ( r′ r k′ r k′n′ , R θ k′ ) )
= k = 1 K n = 1 N k b ( r r k r kn , R θ k ) b ( r′ r k r kn , R θ k ) )
+ k = 1 K n = 1 N k n′ = 1 n′ n N k′ b ( r r k r kn , R θ k ) b ( r′ r k r kn′ , R θ k ) )
+ k = 1 K k′ = 1 k′ k K n = 1 K k n′ = 1 N k′ b ( r r k r kn , R θ k ) b ( r′ r k′ r k′n′ , R θ k′ ) ) .
T 1 = k = 1 K n = 1 N k b ( r r k r kn , R θ k ) b ( r′ r k r kn , R θ k ) r k , θ k r kn N k K
= k = 1 K n = 1 N k 1 2 πA 0 2 π k A d r k Θ d r kn ϕ ( r kn ) b ( r r k r kn , R θ k ) b ( r′ r k r kn , R θ k ) N k K
= k = 1 K n = 1 N k 1 2 πA 0 2 π k Θ d r kn ϕ ( r kn ) R b θ k ( r r′ ) N k K = K ¯ N ¯ A R b ( r r′ ) ,
T 2 = k = 1 K n = 1 N k n′ = 1 n′ n N k′ b ( r r k r kn , R θ k ) b ( r′ r k r kn , R θ k ) r k , θ k r kn , r kn′ N k K
= k = 1 K n = 1 N k n′ = 1 n′ n N k′ b ( r r k r kn , R θ k ) r kn b ( r′ r k r kn , R θ k ) r kn′ r k θ k N k K
= k = 1 K n = 1 N k n′ = 1 n′ n N k′ S θ k ( r r k ) S θ k ( r′ r k ) r k θ k N k K
= k = 1 K n = 1 N k n′ = 1 n′ n N k′ 1 A R s ( r r′ ) N k K
= KN ¯ 2 A R s ( r r′ ) ,
T 3 = k = 1 K k′ = 1 k′ k K n = 1 N k b ( r r k r kn , R θ k ) r k r kn θ k N n′ = 1 N k′ b ( r′ r k′ r k′n′ , R θ k′ ) r k′ r k′n′ θ k′ N K
= k = 1 K k′ = 1 k′ k K n = 1 N k I A N k n′ = 1 N k′ I A N k K = K ( K 1 ) ( N ¯ I A ) 2 K = ( K ¯ N ¯ I A ) 2 ,
c ( r , r′ ) = c ( r r′ ) = K ¯ N ¯ A ( R b ( r r′ ) + N ¯ R s ( r r′ ) ) .

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