Skip to main content

Towards a Quantitative Measure of Radiographic Masking by Dense Tissue in Mammography

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8539))

Abstract

The detection sensitivity of screening mammography is reduced for dense breasts where the appearance of fibroglandular tissue can mask suspicious lesions. A measure of the degree of masking expected for a mammogram could be useful for informing the decision to direct some women to supplemental imaging procedures not affected by density. Here, we present an adaptation of a model observer to estimate the detection task SNR, d local, of a lesion embedded in various portions of the breast to indicate the level of detection difficulty. Rank correlation of mean mammogram d local with density category is ρ=–0.58. Correlation of fractional area of mammograms with low d local < 2 versus density category is ρ=0.61. This suggests that a metric based on d local may be useful in quantifying masking effects of breast density.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boyd, N.F., Martin, L.J., Stone, J., Greenberg, C., Minkin, S., Yaffe, M.J.: Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention. Curr. Oncol. Rep. 3, 314–321 (2001)

    Article  Google Scholar 

  2. Byng, J.W., Yaffe, M.J., Jong, R.A., Shumak, R.S., Lockwood, G.A., Tritchler, D.L., Boyd, N.F.: Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 18, 1587–1598 (1998)

    Article  Google Scholar 

  3. Carney, P.A., Miglioretti, D.L., Yankaskas, B.C., Kerlikowske, K., Rosenberg, R., Rutter, C.M., Geller, B.M., Abraham, L.A., Taplin, S.H., Dignan, M., Cutter, G., Ballard-Barbash, R.: Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann. Intern. Med. 138, 168–175 (2003)

    Article  Google Scholar 

  4. Mainprize, J.G., Yaffe, M.J.: Cascaded analysis of signal and noise propagation through a heterogeneous breast model. Med. Phys. 37, 5243–5250 (2010)

    Article  Google Scholar 

  5. Alonzo-Proulx, O., Packard, N., Boone, J.M., Al-Mayah, A., Brock, K.K., Shen, S.Z., Yaffe, M.J.: Validation of a method for measuring the volumetric breast density from digital mammograms. Phys. Med. Biol. 55, 3027–3044 (2010)

    Article  Google Scholar 

  6. Boone, J.M., Cooper, V.N.: Scatter/primary in mammography: Monte Carlo validation. Med. Phys. 27, 1818–1831 (2000)

    Article  Google Scholar 

  7. Samei, E., Flynn, M.J., Reimann, D.A.: A method for measuring the presampled MTF of digital radiographic systems using an edge test device. Med. Phys. 25, 102–113 (1998)

    Article  Google Scholar 

  8. Reiser, I., Lee, S., Nishikawa, R.M.: On the orientation of mammographic structure. Med. Phys. 38, 5303–5306 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Mainprize, J.G., Wang, X., Ge, M., Yaffe, M.J. (2014). Towards a Quantitative Measure of Radiographic Masking by Dense Tissue in Mammography. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07887-8_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07886-1

  • Online ISBN: 978-3-319-07887-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics