Automated and Human Determination of Threshold Contrast for Digital Mammography Systems

  • Kenneth C. Young
  • James J. H. Cook
  • Jennifer M. Oduko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)


European Guidelines for quality control in digital mammography specify minimum and achievable standards of image quality in terms of threshold contrast, based on readings of images of the CDMAM test object by human observers. However this is time-consuming and has large inter-observer error. To overcome these problems a software program (CDCOM) is available to automatically read CDMAM images and can be used to predict the threshold contrast for a typical observer. The results of threshold contrast determination by a panel of 3 human observers was compared in this study to predicted human readings for different types of digital mammography system to determine whether this provides a viable method of automated quality control and comparison with existing European Guidelines.


Human Observer European Guideline Digital Mammography Threshold Contrast Digital Mammography System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Van Engen, R., Young, K.C., Bosmans, H., Thijssen, M.: The European protocol for the quality control of the physical and technical aspects of mammography screening. Part B: Digital mammography. In: European Guidelines for Breast Cancer Screening, 4th edn., European Commission, Luxembourg (2006) (in press and available in draft online at Scholar
  2. 2.
    Bijkerk, K.R., Thijssen, M.A.O., Arnoldussen, Th.J.M.: Modification of the CDMAM contrast-detail phantom for image quality of Full Field Digital Mammography systems. In: Yaffe, M. (ed.) Proceedings of IWDM 2000, pp. 633–640. Medical Physics Publishing, Madison, WI, Toronto (2000)Google Scholar
  3. 3.
    Young, K.C., Johnson, B., Bosmans, H., Van Engen, R.: Development of minimum standards for image quality and dose in digital mammography. In: Proceedings of IWDM 2004, pp. 149–154 (2005)Google Scholar
  4. 4.
    Karssemeijer, N., Thijssen, M.A.O.: Determination of contrast-detail curves of mammography systems by automated image analysis. In: Digital Mammography 1996. Proceedings of the 3rd International Workshop on Digital Mammography, pp. 155–160 (1996)Google Scholar
  5. 5.
    Veldkamp, W.J.H., et al.: The value of scatter removal by a grid in full field digital mammography. Med. Phys. 30, 1712–1718 (2003)CrossRefGoogle Scholar
  6. 6.
    Visser, R., Karssemeijer, N.: CDCOM Manual: software for automated readout of CDMAM 3.4 images. (note: CDCOM software, manual and sample images are posted at
  7. 7.
    Fletcher-Heath, L., Van Metter, R.: Quantifying the performance of human and software CDMAM phantom image observers for the qualification of digital mammography systems. In: Proc SPIE Medical Imaging 2005: Physics of Medical Imaging, vol. 5745, pp. 486–498 (2005)Google Scholar
  8. 8.
    Young, K.C., Cook, J.J.H., Oduko, J.M., Bosmans, H.: Comparison of software and human observers in reading images of the CDMAM test object to assess digital mammography systems. In: Flynn, M.J., Hsieh, J. (eds.) Proceedings of SPIE Medical Imaging 2006, vol. 614206, pp. 1–13 (2006)Google Scholar
  9. 9.
    Dance, D.R., Skinner, C.L., Young, K.C., Beckett, J.R., Kotre, C.J.: Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol. Phys. Med. Biol. 45, 3225–3240 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kenneth C. Young
    • 1
  • James J. H. Cook
    • 1
  • Jennifer M. Oduko
    • 1
  1. 1.Royal Surrey County HospitalNational Coordinating Centre for the Physics of MammographyGuildfordUK

Personalised recommendations