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A Robust and Efficient Curve Skeletonization Algorithm for Tree-Like Objects Using Minimum Cost Paths.

Dakai Jin1, Krishna S Iyer2, Cheng Chen1

  • 1Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.

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Summary
This summary is machine-generated.

This study introduces a novel curve skeletonization algorithm for 3D fuzzy objects, significantly reducing spurious branches and computation complexity. The minimum cost path approach enhances accuracy and robustness in skeleton detection.

Keywords:
CT imagingairway treecenter of maximal ballcurve skeletonizationdistance transformminimum cost path

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Area of Science:

  • Computer Vision
  • Medical Imaging Analysis
  • Geometric Modeling

Background:

  • Conventional curve skeletonization methods often generate spurious branches, impacting accuracy.
  • Existing algorithms struggle with boundary irregularities and digital artifacts in 3D fuzzy objects.

Purpose of the Study:

  • To develop a robust and efficient 3D curve skeletonization algorithm for elongated fuzzy objects.
  • To overcome limitations of existing methods by eliminating spurious branches without post-processing.

Main Methods:

  • A minimum cost path approach is employed, starting from a root voxel and iteratively expanding the skeleton.
  • A novel local significance factor, based on fuzzy distance transform, guides the pathfinding to object centerlines.
  • Algorithm termination occurs when the object volume is filled or meaningful branches can no longer be generated.

Main Results:

  • The new algorithm demonstrates superior accuracy and robustness in detecting true and false skeletal branches compared to existing methods.
  • Evaluated using computer-generated phantoms and in vivo CT imaging of human airways, the method showed improved performance.
  • Significant reduction in computation complexity achieved by detecting multiple branches per iteration.

Conclusions:

  • The proposed minimum cost path algorithm offers a robust and efficient solution for 3D curve skeletonization of fuzzy objects.
  • It effectively mitigates spurious branches and reduces computational load, outperforming conventional techniques.
  • This advancement has implications for accurate analysis and modeling of complex biological structures.