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DISJUNCTIVE NORMAL LEVEL SET: AN EFFICIENT PARAMETRIC IMPLICIT METHOD.

Fitsum Mesadi1, Mujdat Cetin2, Tolga Tasdizen1

  • 1Department of Electrical and Computer Eng., University of Utah, United States.

Proceedings. International Conference on Image Processing
|April 11, 2017
PubMed
Summary
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We introduce the Disjunctive Normal Level Set (DNLS), a novel parametric method for faster and more regular image segmentation. The DNLS method offers improved initialization sensitivity and stable computational costs for multi-object segmentation.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Level set methods are essential for image segmentation, particularly for handling complex topological changes.
  • Existing methods often face challenges with convergence speed, regularity, and scalability in multiphase segmentation.

Purpose of the Study:

  • To introduce a novel parametric level set method, Disjunctive Normal Level Set (DNLS).
  • To apply DNLS to both two-phase and multiphase image segmentation tasks.
  • To demonstrate the advantages of DNLS over existing level set techniques.

Main Methods:

  • The Disjunctive Normal Level Set (DNLS) is constructed as a union of polytopes, derived from intersections of half-spaces.
  • The method is applied to segment single (two-phase) and multiple (multiphase) objects in images.
Keywords:
Level set methodmultiphase level setparametric level set methodsegmentation

Related Experiment Videos

  • The evolution of the DNLS level set function is analyzed for regularity and convergence properties.

    • Main Results:

    • DNLS demonstrates significantly faster convergence rates in image segmentation.
    • The DNLS level set function maintains regularity throughout its evolution, ensuring stable segmentation.
    • The multiphase DNLS is robust to initialization variations and exhibits near-constant computational and memory demands as the number of segmented objects increases.

    Conclusions:

    • The proposed Disjunctive Normal Level Set (DNLS) method offers substantial improvements in speed, regularity, and scalability for image segmentation.
    • DNLS shows significant potential for both two-phase and multiphase image segmentation applications.
    • The method's efficiency and robustness make it a promising alternative to current level set techniques.