Skip to main content

Relationship Between the Stroma Edge and Skin-Air Boundary for Generating a Dependency Approach to Skin-Line Estimation in Screening Mammograms

  • Conference paper
Pattern Recognition and Image Analysis (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

Included in the following conference series:

Abstract

Breast area segmentation or skin-line extraction in mammograms is very important in many aspects. Prior segmentation can reduce the effects of background noise and artifacts on the analysis of mammograms. In this paper, we investigate a novel method to estimate the breast skin-line in mammograms. Adaptive thresholding [1] yields a nearly perfect skin-line at the center of the image and around the nipple area with images from the MIAS database [2], but the upper and lower portions of the extracted boundary have been observed to be erroneous due to noise and artifacts. Because the distance from the edge of the stroma to the actual skin-line is usually uniform, we propose a method to estimate the skin-line from the edge of the stroma, with the information provided by the center portion around the nipple from adaptive thresholding. The results are compared with the ground-truth boundaries drawn by a radiologist [3] using polyline distance measure and shape smoothness measure. The results on 83 mammograms from the MIAS database are demonstrated. The proposed methods led to a decrease in a shape smoothness measure based upon curvature, on the average, from 65.6 to 20.0 over the 83 mammograms tested, resulting in an improvement of 69.5%.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ojala, T., Nappi, J., Nevalainen, O.: Accurate segmentation of the breast region from digitized mammograms. Computerized Medical Imaging and Graphics 25, 47–59 (2001)

    Article  Google Scholar 

  2. Suckling, J., Parker, J., Dance, D.R., Astley, S., Hutt, I.: The Mammographic Image Analysis Society digital mammogram database. In: 2nd International Workshop on Digital Mammography, York, England, pp. 375–378 (1994)

    Google Scholar 

  3. Ferrari, R.J., Rangayyan, R.M., Frere, A.F., Desautels, J.E.L., Borges, R.A.: Identification of the breast boundary in mammograms using active contour models. Med. Biol. Engr.Comput. 42, 201–208 (2004)

    Article  Google Scholar 

  4. Bick, U., Giger, M., Schmidt, R.A., Nishikawa, R., Doi, K.: Automated segmentation of digitized mammograms. Acad. Radiol. 2, 1–9 (1995)

    Article  Google Scholar 

  5. Abdel-Mottaleb, M., Carman, C.S., Hill, C.R., Vafai, S.: Locating the boundary between the breast skin edge and the background in digitized mammograms. In: 3rd International Workshop on Digital Mammography, Chicago, IL, pp. 467–470 (1996)

    Google Scholar 

  6. McLoughlin, K.J., Bones, P.J.: Segmentation of the breast-air boundary for a digital mammogram image. In: Proc. Image Vision Computing, New Zealand, Hamilton, pp. 228–233 (2000)

    Google Scholar 

  7. Wirth, M.A., Stapinski, A.: Segmentation of the breast region in mammograms using active contours. In: Proceedings of SPIE: Visual Communications and Image Processing, pp. 1995–2006 (2003)

    Google Scholar 

  8. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetcs 9, 62–66 (1979)

    Article  Google Scholar 

  9. Suri, J., Wilson, D., Laximinarayan, S.: Handbook of Medical Image Analysis: Advanced Segmentation and Registration Models. Springer, Heidelberg (2005)

    Google Scholar 

  10. Unser, M.: Splines: A perfect fit for signal and image processing. IEEE Signal Processing Magazine 16, 22–38 (1999)

    Article  Google Scholar 

  11. Casey, J.: Exploring Curvature. Vieweg, Wiesbaden (1996)

    MATH  Google Scholar 

  12. Suri, J., Haralick, R.M., Sheehan, F.H.: Greedy algorithm for error reduction in automatically produced boundaries from low contrast ventriculograms. International Journal of Pattern Analysis and Applications 3, 39–60 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sun, Y., Suri, J., Rangayyan, R., Janer, R. (2005). Relationship Between the Stroma Edge and Skin-Air Boundary for Generating a Dependency Approach to Skin-Line Estimation in Screening Mammograms. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_82

Download citation

  • DOI: https://doi.org/10.1007/11552499_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics