Interactive segmentation of 3D ultrasound using deformable solid models and active contours

  • C. R. Dance
  • M. H. Syn
  • R. W. Prager
  • J. P. M. Gosling
  • L. H. Berman
  • K. J. Dalton
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


This paper presents a prototype segmentation system for three-dimensional ultrasound data. 3D ultrasound is cheap and non-nvasive but the data has a low signal-to-noise ratio and contains artifacts. To overcome these difficulties we have developed a system which uses a prior model, initialised by a clinician, to provide the starting point for a data-driven segmentation algorithm based on active contours. Results are presented showing how the technique can facilitate the segmentation of a gall-bladder.


Active Contour Active Contour Model Initial Segmentation Ultrasound Data Interactive Segmentation 
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.


  1. [1]
    W.A. Barrett and E.N. Mortensen. Fast, accurate and reproducible live-wire boundary extraction. In K.H. Höhne and Kikinis R., editors, Proceedings of the 4th International Conference on Visualization in Biomedical Computing, volume 1131 of Lecture Notes in Computer Science, pages 183–192. Springer, September 1996.Google Scholar
  2. [2]
    C.J. Bouma, W.J. Niessen, K.J. Zuiderveld, E.J. Gussenhoven, and M.A. Viergever. Evaluation of segmentation algorithms for intravascular ultrasound images. In Proceedings of the 4th International Conference on Visualization in Biomedical Computing, pages 203–212, September 1996.Google Scholar
  3. [3]
    L.D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1131–1147, 1993.CrossRefGoogle Scholar
  4. [4]
    C.R. Dance. Computing models from 3D ultrasound. PhD thesis, Cambridge University, 1997.Google Scholar
  5. [5]
    R.M. Haralick and L.G. Shapiro. Image segmentation techniques. Computer Vision, Graphics and Image Processing, 29:100–132, 1985.Google Scholar
  6. [6]
    A. Hill, A. Thornham, and C.J. Taylor. Model based interpretation of 3d medical images. In Proceedings of the British Machine Vision Conference, pages 339–348, 1993.Google Scholar
  7. [7]
    M. Kass, A. Witkin, and D. Terzopoulos. Snakes: active contour models. In Proc. 1st International Conference on Computer Vision, pages 259–268, 1987.Google Scholar
  8. [8]
    A. Lobregt and M.A. Viergever. A discrete dynamic contour model. IEEE Transactions on Medical Imaging, 14(1):12–24, 1995.CrossRefGoogle Scholar
  9. [9]
    A. Pentland and S. Sclaroff. Closed-form solutions for physically based shape modeling and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(7):715–729, 1991.CrossRefGoogle Scholar
  10. [10]
    S. Revankar, D. Sher, C. Cheung, V.L. Shalin, M. Ramamurthy, and S. Rosenthal. Supervised interpretation of echocardiograms with a psychological model of expert supervision. Computerized Medical Imaging and Graphics, 19(1):47–59, 1995.CrossRefPubMedGoogle Scholar
  11. [11]
    M.H. Syn. Model-based three-dimensional freehand ultrasound imaging. PhD thesis, Cambridge University, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • C. R. Dance
    • 1
  • M. H. Syn
    • 1
  • R. W. Prager
    • 1
  • J. P. M. Gosling
    • 1
  • L. H. Berman
    • 2
  • K. J. Dalton
    • 3
  1. 1.Cambridge University Engineering Dept.CambridgeUK
  2. 2.Cambridge University Radiology Dept.CambridgeUK
  3. 3.Rosie Maternity HospitalCambridge University Obstetrics & Gynaecology Dept.CambridgeUK

Personalised recommendations