Advertisement

Embedded Geometric Active Contour with Shape Constraint for Mass Segmentation

  • Ying Wang
  • Xinbo Gao
  • Xuelong Li
  • Dacheng Tao
  • Bin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)

Abstract

Mass boundary segmentation plays an important role in computer aided diagnosis (CAD) system. Since the shape and boundary are crucial discriminant features in CAD, the active contour methods are more competitive in mass segmentation. However, the general active contour methods are not so effective for some cases, because most masses possess very blurry margin that easily induce the contour leaking. To the end, this paper presents an improved geometric active contour for mass segmentation. It firstly introduces the morphological concentric layer model for automatically initializing. Then an embedded level set is used to extract the adaptive shape constraints. For refining the boundary, a new shape constraint function and stopping function are designed for the enhanced geometric active contour method. The proposed method is tested on real mammograms containing masses, and the results suggest that the proposed method could effectively restrain the contour leaking and get better segmented results than general active contour methods.

Keywords

Mass segmentation active contour shape constraint embedded level set mammogram 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    American Cancer Society-Cancer facts and figures (2008), http://www.cancer.org/downloads/STT/008-CAFFfinalsecured.pdf
  2. 2.
    Li, H.D., Kallergi, M., Clarke, L.P., Jain, V.K., Clark, R.A.: Markov Random Field for Tumor Detection in Digital Mammography. IEEE Trans. Med. Imaging 14(3), 565–576 (1995)CrossRefGoogle Scholar
  3. 3.
    Kobatake, H., Murakami, M., Takeo, H., Nawano, S.: Computerized Detection of Malignant Tumors on Digital Mammograms. IEEE Trans. Med. Imaging 18(5), 369–378 (1999)CrossRefGoogle Scholar
  4. 4.
    Sahiner, B., Petrick, N., Chan, H.P., Hadjiiski, M., Paramagul, C., Helvie, M.A., Gurcan, M.N.: Computer-Aided Charaterization of Mammographic Masses: Accuracy of Mass Segmentation and Its Effects on Characterization. IEEE Trans. Med. Imaging 20(12), 1275–1284 (2001)CrossRefGoogle Scholar
  5. 5.
    Timp, S., Karssemeijer, N.: A New 2D Segmentation Method Based on Dynamic Programming Applied to Computer Aided Detection in Mammography. IEEE Trans. Med. Imaging 31(5), 958–971 (2004)Google Scholar
  6. 6.
    Ball, J.E., Bruce, L.M.: Digital Mammogram Spiculated Mass Detection and Spicule Segmentation Using Level Sets. In: IEEE International Conference on Engineering in Medicine and Biology Society, pp. 4979–4984. IEEE Press, Lyon (2007)Google Scholar
  7. 7.
    Petrick, N., Chan, H.P., Sahiner, B., Helvie, M.A.: Combined Adaptive Enhancement and Region-Growing Segmentation of Breast Masses on Digitized Mammograms. Med. Phys. 26(8), 1642–1654 (1999)CrossRefGoogle Scholar
  8. 8.
    Rousson, M., Paragios, N.: Shape Priors for Level Set Representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Ersoy, I., Bunyak, F., Palaniappan, K., Sun, M., Forgacs, G.: Cell Spreading Analysis with Directed Edge Profile-Guided Level Set Active Contours. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 376–383. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Eltonsy, N.H., Tourassi, G.D., Elmaghraby, A.S.: A Concentric Morphology Model for the Detection of Masses in Mammography. IEEE Trans. Med. Imaging 26(6), 880–889 (2007)CrossRefGoogle Scholar
  11. 11.
    Xu, C.Y., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. Int. J. Comput. Vis. 1, 321–331 (1987)CrossRefGoogle Scholar
  13. 13.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamiltons-Jacobi Formulations. J. Comput. Phys. 79(1), 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation. IEEE Trans. Pattern Anal. Machine Intell. 17(2), 158–175 (1995)CrossRefGoogle Scholar
  15. 15.
    Starck, J.L., Elad, M., Donoho, D.: Redundant Multiscale Transforms and Their Application for Morphological Component Analysis. Adv. Imag. Elect. Phys. 132, 287–348 (2004)Google Scholar
  16. 16.
    Li, C.M., Xu, C.Y., Gui, C.F., Fox, M.D.: Level Set Evolution Without Re-initialization: A New Variational Formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 430–436. IEEE Press, San Diego (2005)Google Scholar
  17. 17.
    University of South Florida, Digital Database for Screening Mammography (DDSM), http://marathon.csee.usf.edu/Mammography/Database.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ying Wang
    • 1
  • Xinbo Gao
    • 1
  • Xuelong Li
    • 2
  • Dacheng Tao
    • 3
  • Bin Wang
    • 1
  1. 1.School of Electronic EngineeringXidian UniversityXi’anP.R. China
  2. 2.State Key Laboratory of Transient Optics and Technology, Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anP.R. China
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations