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A discrete dynamic contour model.

S Lobregt1, M A Viergever

  • 1Dept. of Adv. Dev., Philips Med. Syst., Best.

IEEE Transactions on Medical Imaging
|January 1, 1995
PubMed
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This study introduces a new active contour model for image segmentation. The discrete dynamic model uses energy minimization to accurately define contours, overcoming common issues like shrinking and vertex clustering.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Active contour models are widely used for image segmentation.
  • Existing models often suffer from undesirable deformation effects such as shrinking and vertex clustering.
  • A robust and reproducible contour definition method is needed.

Purpose of the Study:

  • To develop a discrete dynamic model for defining contours in 2-D images.
  • To address limitations of existing active contour models, specifically shrinking and vertex clustering.
  • To achieve reproducible approximations of desired contours through energy minimization.

Main Methods:

  • A discrete dynamic model composed of connected vertices is proposed.
  • The model is initialized with minimal user interaction.

Related Experiment Videos

  • An energy minimizing process modifies the contour, with internal energy based on curvature and external energy on image features.

    • Main Results:

    • The model successfully avoids shrinking and vertex clustering.
    • The deformation process converges to a local minimum of the energy function.
    • The method yields reproducible contour approximations for both synthetic and clinical images.

    Conclusions:

    • The developed discrete dynamic active contour model offers an effective solution for image segmentation.
    • It overcomes common limitations of existing methods, providing stable and accurate contour definition.
    • The model's reproducibility and robustness make it suitable for various imaging applications.