A new method of extracting closed contours using maximal discs

  • Gabriele Lohmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 970)


In this paper, a new scheme for extracting a closed contour of some object given one initial point in the interior of the object is proposed.

The principal domain of application is the analysis of MRI images of the human brain. The scenario envisaged here is the following. The user (a medical doctor for instance) ”clicks” on some area of interest in the MRI image, and the algorithm then automatically extracts the object containing the pixel clicked upon, so that the object's shape parameters and its size can be computed.

The assumption underlying the approach is that the contour of the object can at least be partially extracted by some standard edge extraction procedure. The purpose of our algorithm is then to connect the edge fragments in a meaningful manner, so that a closed contour of the object results. The principal idea of the approach is to pack the interior of the object with maximal discs. Special provisions are made to prevent the discs from squeezing through gaps in the boundary.


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  1. 1.
    C. Arcelli, G. Sanniti di Baja. Euclidean skeleton via centre-of-maximal-disc extraction. Image and Vision Computing, 11(3):163–173, Apr. 1993.Google Scholar
  2. 2.
    G. Borgefors. Distance transforms in arbitrary dimensions. Computer Vision, Graphics, and Image Processing, 27:321–345, 1984.Google Scholar
  3. 3.
    J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679–698, Nov. 1986.Google Scholar
  4. 4.
    T.F. Cootes, A. Hill, C.J. Taylor, J. Haslem. Use of active shape models for locating structures in medical images. Image and Vision Computing, 12(6):355–365, July/August 1994.Google Scholar
  5. 5.
    F. Klein, O. Kübler. Euclidean distance transformations and model-guided image interpretation. Pattern Recognition Letters, 5:19–29, 1987.Google Scholar
  6. 6.
    R. Mohan, R. Nevatia. Perceptual organization for scene segmentation and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(6):616–635, June 1992.Google Scholar
  7. 7.
    R. Mohr, R. Bajcsy. Packing volumes by spheres. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1):111ff., Jan. 1983.Google Scholar
  8. 8.
    R.L. Ogniewicz. Skeleton-space: a multiscale shape description combining region and boundary information. In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 746–751, Seattle, WA, USA, June 21–23 1994.Google Scholar
  9. 9.
    D. Paglieroni. Distance transforms: properties and machine vision applications. Computer Vision, Graphics, and Image Processing, 54(1):57–74, Jan. 1992.Google Scholar
  10. 10.
    M. Xie, M. Thonnat. An algorithm for finding closed curves. Pattern Recognition Letters, 13:73–81, Jan. 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Gabriele Lohmann
    • 1
  1. 1.Institut für InformatikTechnische Universität MünchenMünchenGermany

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