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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Published on: November 30, 2022

Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

Krzysztof Chris Ciesielski1, Paulo A V Miranda, Alexandre X Falcão

  • 1Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310, United States; Department of Radiology, MIPG, University of Pennsylvania, Blockley Hall, 4th Floor, 423 Guardian Drive, Philadelphia, PA 19104-6021, United States.

Medical Image Analysis
|July 25, 2013
PubMed
Summary
This summary is machine-generated.

A new image segmentation algorithm, GC(sum)(max), combines Relative Fuzzy Connectedness (RFC) and Graph Cut (GC) for improved accuracy. It overcomes limitations of existing methods, offering robust and precise image segmentation across various applications.

Keywords:
Fuzzy connectednessGraph cutImage segmentationRobustness

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Area of Science:

  • Computer Vision and Image Processing
  • Medical Imaging Analysis
  • Algorithm Development

Background:

  • Existing image segmentation algorithms like Graph Cut (GC) and Relative Fuzzy Connectedness (RFC) have limitations.
  • GC can suffer from the "shrinking problem" and leak through poorly defined boundaries.
  • RFC is robust to seed choice but may not offer sufficient control over boundary leakage.

Purpose of the Study:

  • To introduce a novel image segmentation algorithm, GC(sum)(max), integrating the strengths of RFC and GC.
  • To address the limitations of existing segmentation methods, enhancing accuracy and robustness.
  • To provide a theoretically and experimentally validated segmentation approach.

Main Methods:

  • Developed GC(sum)(max) by combining RFC and GC principles.
  • Utilized theoretical framework of Generalized Graph Cut (GGC) for algorithm analysis.
  • Implemented GC(sum)(max) using a linear-time RFC subroutine based on Image Forest Transform.

Main Results:

  • GC(sum)(max) preserves RFC's robustness to seed selection while mitigating GC's boundary leakage issues.
  • The algorithm achieves near-linear time complexity.
  • Experimental comparisons show GC(sum)(max) outperforms GC, Power Watershed (PW), and Iterative RFC (IRFC) in accuracy.

Conclusions:

  • GC(sum)(max) represents a significant advancement in image segmentation accuracy and robustness.
  • The algorithm offers a superior alternative to existing methods for both medical and non-medical image analysis.
  • The developed method demonstrates a promising direction for future image segmentation research.