Impact of Textured Background on Scoring of Simulated CDMAM Phantom

  • Bénédicte Grosjean
  • Serge Muller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)


CDMAM phantom scoring is widely used to assess the detectability performance of mammography systems. We propose to study the impact of structured background on this performance assessment, using simulated CDMAM phantom images with flat and textured backgrounds. Three dose levels have been investigated, ranging from -50% to +50% around the reference dose computed by the acquisition system. For textured backgrounds, the simulated projected breast corresponds to a 50mm thick, 60% glandular breast, with a texture generated by a power-law filtered noise model. Images have been scored by four image quality experts. For the smaller insert sizes, Image Quality Factor (IQF) scores obtained in textured backgrounds are lower than and well correlated with those obtained in flat backgrounds. IQF values increased with dose. For the larger insert sizes, detectability performance in textured background is even more degraded and is not as dose dependent as it is in flat backgrounds.


Detection Performance Insert Size Digital Mammography Structure Background Digital Mammogram 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Revesz, G., Kundel, H.L., Graber, M.A.: The influence of structured noise on the detection of radiological abnormalities. Invest. Radiol. 9(6), 479–486 (1974)CrossRefGoogle Scholar
  2. 2.
    Rolland, J.P., Barrett, H.H.: Effect of random background inhomogeneity on observer detection performance. J. Opt. Soc. Am. A(9), 649–658 (1992)CrossRefGoogle Scholar
  3. 3.
    Burgess, A.E., Jacobson, F.L., Judy, P.F.: Human observer detection experiments with mammograms and power-law noise. Med. Phys. 28(4), 419–437 (2001)CrossRefGoogle Scholar
  4. 4.
    Bochud, F.O., Valley, J.F., Verdun, F.R., Hessler, C., Schnyder, P.: Estimation of the noisy component of anatomical backgrounds. Med. Phys. 26(7), 1365–1370 (1999)CrossRefGoogle Scholar
  5. 5.
    Bijkerk, K., Thijssen, M., Arnoldussen, Th.: Manual CDMAM-phantom type 3.4. University Medical Centre, Nijmegen, The Netherlands (2000)Google Scholar
  6. 6.
    Grosjean, B., Muller, S., Souchay, H., Rico, R., Bouchevreau, X.: Automated scoring for CDMAM phantom from simulated images. In: Proceedings of IWDM 2004 (2004)Google Scholar
  7. 7.
    Burgess, A.E.: Bach, breasts, and power-law processes. In: Proceedings of SPIE, vol. 4324, pp. 103–113 (2001)Google Scholar
  8. 8.
    Heine, J.J., Velthuizen, R.P.: Spectral analysis of full filed digital mammography data. Med. Phys. 29(5), 647–661 (2002)CrossRefGoogle Scholar
  9. 9.
    Grosjean, B., Muller, S., Souchay, H.: Lesion detection using an a-contrario detector in simulated digital mammograms. In: Proceedings of SPIE (to be published, 2006)Google Scholar
  10. 10.
    Shramchenko, N., Blin, P., Mathey, C., Klausz, R.: Optimized exposure control in digital mammography. In: Proceedings of SPIE, vol. 5368, pp. 445–456 (2004)Google Scholar
  11. 11.
    Burgess, A.E., Jacobson, F.L., Judy, P.F.: Lesion detection in digital mammograms. In: Proceedings of SPIE, vol. 4320, pp. 555–560 (2001)Google Scholar
  12. 12.
    Burgess, A.E.: Evaluation of detection model performance in power-law noise. In: Proceedings of SPIE, vol. 4324, pp. 123–132 (2001)Google Scholar
  13. 13.
    Carton, A.K., Bosmans, H., Van Ongeval, C., Souverijns, G., Rogge, F., Van Steen, A., Marchal, G.: Development and validation of a simulation procedure to study the visibility of microcalcifications in digital mammograms. Med. Phys. 30(8), 2234–2240 (2003)CrossRefGoogle Scholar
  14. 14.
    Ruschin, M., Tingberg, A., Bath, M., Grahn, A., Hakansson, M., Hamdal, B., Andersson, I.: Using simple mathematical functions to simulate pathological structures – input for digital mammography clinical trial. Radiat. Prot. Dosimetry 114(1-3), 424–431 (2005)CrossRefGoogle Scholar
  15. 15.
    Van Metter, R., Health, M.D., Fletcher-Health, L.M.: Applying the European protocol for the quality control of the physical and technical aspect of mammography screening to digital systems. In: Proceeding of SPIE (to be published, 2006)Google Scholar
  16. 16.
    Young, K.C., Cook, J.J., Oduko, J.M., Bosmans, H.T.: Comparison of software and human observers in reading images of the CDMAM test object to assess digital mammography systems. In: Proceeding of SPIE (to be published, 2006)Google Scholar
  17. 17.
    Highnam, R.P., Brady, J.M., Shepstone, B.J.: Mammographic image analysis. Eur. J. Radiol. 24(1), 20–32 (1997)CrossRefGoogle Scholar
  18. 18.
    Gennaro, G., Katz, L., Souchay, H., Alberelli, C., di Maggio, C.: Are phantoms useful for predicting the potential of dose reduction in full-filed digital mammography? Phys. Med. Biol. 50, 1851–1870 (2005)CrossRefGoogle Scholar
  19. 19.
    Chawla, A.S., Saunders, R.S., Samei, E.: Effect of dose reduction on the detection of mammographic lesions based on mathematical observer models. In: Proceedings of SPIE (to be published, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bénédicte Grosjean
    • 1
  • Serge Muller
    • 1
  1. 1.Mammography DepartmentGE HealthcareFrance

Personalised recommendations