Medical image segmentation using topologically adaptable surfaces

  • Tim McInerney
  • Demetri Terzopoulos
Segmentation and Deformable Models

DOI: 10.1007/BFb0029221

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1205)
Cite this paper as:
McInerney T., Terzopoulos D. (1997) Medical image segmentation using topologically adaptable surfaces. In: Troccaz J., Grimson E., Mösges R. (eds) CVRMed-MRCAS'97. Lecture Notes in Computer Science, vol 1205. Springer, Berlin, Heidelberg

Abstract

Efficient and powerful topologically adaptable deformable surfaces can be created by embedding and defining discrete deformable surface models in terms of an Affine Cell Decomposition (ACD) framework. The ACD framework, combined with a novel and original reparameterization algorithm, creates a simple but elegant mechanism for multiresolution deformable curve, surface, and solid models to “flow” or “grow” into objects with complex geometries and topologies, and adapt their shape to recover the object boundaries. ACD-based models maintain the traditional parametric physics-based formulation of deformable models, allowing them to incorporate a priori knowledge in the form of energy and force-based constraints, and provide intuitive interactive capabilities. This paper describes ACD-based deformable surfaces and demonstrates their potential for extracting and reconstructing some of the most complex biological structures from medical image volumes.

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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Tim McInerney
    • 1
  • Demetri Terzopoulos
    • 1
  1. 1.Dept. of Computer ScienceUniversity of TorontoTorontoCanada

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