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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm.

Chuang Zhang1, Yue-Han Pei2, Xiao-Xue Wang3

  • 1School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, China.

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Summary
This summary is machine-generated.

A novel method enhances multilevel image segmentation accuracy and speed using an improved pelican optimization algorithm. This approach offers superior global optimization and segmentation performance compared to existing techniques.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional multilevel image segmentation methods suffer from low accuracy and slow convergence.
  • Global optimization is crucial for effective image segmentation.
  • Existing swarm intelligence algorithms have limitations in complex optimization tasks.

Purpose of the Study:

  • To propose a novel symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) for improved accuracy and convergence.
  • To enhance the pelican optimization algorithm with multi-strategy improvements for superior global optimization.
  • To evaluate the effectiveness of MSIPOA in both benchmark function optimization and image segmentation tasks.

Main Methods:

  • The proposed MSIPOA algorithm incorporates Sine chaotic mapping for initial population enhancement.
  • A spiral search mechanism and Levy flight strategy are integrated to improve search diversity and escape local minima.
  • Performance is evaluated using 12 benchmark test functions and 8 other swarm intelligence algorithms, followed by application to multilevel thresholding image segmentation on BSDS300 dataset.

Main Results:

  • MSIPOA demonstrated superior convergence speed and accuracy compared to other optimization algorithms based on non-parametric statistical analysis.
  • The algorithm achieved better global optimization and image segmentation results on the BSDS300 dataset.
  • Statistical tests confirmed the significant outperformance of MSIPOA over similar algorithms in segmentation tasks.

Conclusions:

  • The multi-strategy improved pelican optimization algorithm (MSIPOA) effectively addresses the limitations of traditional methods.
  • MSIPOA provides a robust and efficient solution for multilevel thresholding image segmentation.
  • The proposed method shows significant potential for practical applications in image analysis and computer vision.