A new approach for breast skin-line estimation in mammograms

  • Yajie Sun
  • Jasjit S. Suri
  • J. E. Leo Desautels
  • Rangaraj M. Rangayyan
Theoretical Advances

Abstract

Accurate estimation of the breast skin-line is an important prerequisite for both enhancement and analysis of mammograms for computer-aided detection of breast cancer. In our proposed system, an initial estimate of the skin-line is first computed using a combination of adaptive thresholding and connected-component analysis. This skin-line is susceptible to errors in the top and bottom portions of the breast region. Using the observation that the Euclidean distance from the edge of the stroma to the actual skin-line is usually uniform, we develop a novel dependency approach for estimating the skin-line boundary of the breast. In the proposed dependency approach, the constraints are first developed between the stroma edge and the initial skin-line boundary using the Euclidean distance. These constraints are then propagated to estimate the upper and lower skin-line portions. We evaluated the performance of our skin-line estimation algorithm by comparing the estimated boundary with respect to the ground-truth boundary drawn by an expert radiologist. We adapted a metrics for error measurement: the polyline distance measure (PDM). As part of our protocol, we compared the results of our dependency approach methodology with those of a deformable model strategy (proposed by Ferrari et al. in Med Biol Eng Comput 42(2):210–208, 2004). On a dataset of 82 images from the MIAS database, the dependency approach yielded a mean error (μ) of 3.28 pixels with a standard deviation (σ) of 2.17 pixels using the PDM. In comparison, the deformable model strategy (Ferrari et al. in Med Biol Eng Comput 42(2):210–208, 2004) yielded μ = 4.92 pixels with σ = 1.91 pixels. The improvement is statistically significant. The results are clinically relevant, according to the radiologists who evaluated the results.

Keywords

Breast skin-line Dependency approach Stroma edge Greedy algorithm Polyline distance Active contours MIAS database 

References

  1. 1.
    Ferrari RJ, Rangayyan RM, Frere AF, Desautels JEL, Borges RA (2004) Identification of the breast boundary in mammograms using active contour models. Med Biol Eng Comput 42(2):201–208CrossRefPubMedGoogle Scholar
  2. 2.
    Bick U, Giger M, Schmidt RA, Nishikawa R, Doi K (1995) Automated segmentation of digitized mammograms. Acad Radiol 2:1–9CrossRefPubMedGoogle Scholar
  3. 3.
    Abdel-Mottaleb M, Carman CS, Hill CR, Vafai S (1996) Locating the boundary between the breast skin edge and the background in digitized mammograms. In: Proceedings of the 3rd international workshop on digital mammography, Chicago, IL pp 467–470Google Scholar
  4. 4.
    Ojala T, Nappi J, Nevalainen O (2001) Accurate segmentation of the breast region from digitized mammograms. Comput Med Imaging Graph 25(1):47–59CrossRefPubMedGoogle Scholar
  5. 5.
    McLoughlin KJ, Bones PJ (2000) Segmentation of the breast-air boundary for a digital mammogram image. In: Proc. Image Vision Computing New Zealand, Hamilton, Nov. 2000, pp 228–233Google Scholar
  6. 6.
    Wirth MA, Stapinski A (2003) Segmentation of the breast region in mammograms using active contours. In: Proceedings of SPIE: visual communications and image processing, pp 1995–2006Google Scholar
  7. 7.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66MathSciNetCrossRefGoogle Scholar
  8. 8.
    Heath M, Bowyer KW, Kopans D, Moore R, Kegelmeyer P (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography, June 11–14, 2000, Toronto, Canada, pp 212–218Google Scholar
  9. 9.
    Rosin PL (2001) Unimodal thresholding, Pattern Recognition. Vol. 34, pp. 2083–2096Google Scholar
  10. 10.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis I, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J (1994) The Mammographic Image Analysis Society digital mammogram database, 2nd international workshop on digital mammography, York, England, pp 375–378Google Scholar
  11. 11.
    Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28:129–137CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Gonzalez R, Woods R (1992) Digital image processing. Addison-Wesley Publishing Company, ReadingGoogle Scholar
  13. 13.
    Suri J, Wilson DL, Laxminarayan S (2005) Handbook of medical image analysis: advanced segmentation and registration models, vols I, II, III. Springer, Berlin Heidelberg New YorkGoogle Scholar
  14. 14.
    Suri J, Yuan C, Wilson DL, Laxminarayan S (2005) Plaque imaging: pixel to molecular. IOS Press, The NetherlandsGoogle Scholar
  15. 15.
    Casey J (1996) Exploring curvature. Vieweg, WiesbadenMATHGoogle Scholar
  16. 16.
    Kreyszig E (1991) Differential geometry. Dover, New YorkGoogle Scholar
  17. 17.
    Unser M (1999) Splines: a perfect fit for signal and image processing. IEEE Signal Process Mag 16(6):22–38CrossRefGoogle Scholar
  18. 18.
    Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: part I—theory. IEEE Trans Signal Process 41(2):821–832CrossRefMATHGoogle Scholar
  19. 19.
    Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: part II—efficient design and applications. IEEE Trans Signal Process 41(2):834–848CrossRefMATHGoogle Scholar
  20. 20.
    Suri J, Haralick RM, Sheehan FH (2000) Greedy algorithm for error reduction in automatically produced boundaries from low contrast ventriculograms. Int J Pattern Anal Appl 3(1):39–60CrossRefGoogle Scholar
  21. 21.
    Sun Y, Suri JS, Rangayyan RM, Janer R (2005) A microscopic look at breast skin-line metrics: a performance evaluation strategy for FFDM/screen-film projection mammograms, The third IASTED international conference on biomedical engineering, Feb 16–18, pp 174–180Google Scholar
  22. 22.

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Yajie Sun
    • 1
  • Jasjit S. Suri
    • 1
  • J. E. Leo Desautels
    • 2
    • 3
  • Rangaraj M. Rangayyan
    • 2
  1. 1.Fischer Imaging CorporationDenverUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada
  3. 3.Screen Test AlbertaCalgaryCanada

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