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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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On two multigrid algorithms for modeling variational multiphase image segmentation.

Noor Badshah1, Ke Chen

  • 1Centre for Mathematical Imaging Techniques (CMIT), Department of Mathematical Sciences, The University of Liverpool, Liverpool L69 7ZL, U.K.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 4, 2009
PubMed
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Two new multigrid algorithms for multiphase image segmentation are introduced. Algorithm II, using a hierarchical model, offers improved robustness over Algorithm I, making it more reliable for image segmentation tasks.

Area of Science:

  • Computer Vision
  • Image Processing
  • Numerical Analysis

Background:

  • Multiphase image segmentation is crucial for analyzing complex images.
  • Existing methods like additive operator splitting (AOS) and initial multigrid approaches (MG1) face robustness challenges with initial guesses.
  • The Chan-Vese model is a foundational tool for two-phase segmentation.

Purpose of the Study:

  • To develop and present two novel multigrid algorithms for multiphase image segmentation.
  • To enhance the robustness of multigrid methods in image segmentation.
  • To compare the efficiency and robustness of the proposed algorithms against existing methods.

Main Methods:

  • Algorithm I (MG1) generalizes a multigrid method to a multiphase segmentation model, incorporating a novel smoother derived from local Fourier analysis.

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  • Algorithm II (MG2) employs a hierarchical segmentation model, iteratively applying the Chan-Vese two-phase model within a multigrid framework.
  • Both algorithms are evaluated for computational efficiency and robustness against variations in initial segmentation guesses.

    • Main Results:

    • MG1 demonstrates significant speed improvements over the additive operator splitting (AOS) method.
    • Both MG1 and MG2 are found to be efficient for multiphase image segmentation.
    • MG2 exhibits superior robustness compared to MG1, particularly concerning the sensitivity to initial segmentation guesses.

    Conclusions:

    • The presented multigrid algorithms offer efficient solutions for multiphase image segmentation.
    • Algorithm II (MG2) provides a more robust alternative to Algorithm I (MG1) by leveraging a hierarchical approach, reducing dependence on initial conditions.
    • These findings contribute to the advancement of reliable and efficient image segmentation techniques.