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Threshold image segmentation based on improved sparrow search algorithm.

Dongmei Wu1, Chengzhi Yuan1

  • 1Jiangsu Engineering Lab IOT Intelligent Robots, School of Automation & Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China.

Multimedia Tools and Applications
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved sparrow search algorithm for enhanced image threshold segmentation. The method boosts segmentation quality for both standard and medical images.

Keywords:
2-D histogramLevy flightMaximum entropyNonlinear weightSwarm optimization algorithmThreshold image segmentation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Swarm intelligence optimization algorithms are popular for image segmentation due to effectiveness and simplicity.
  • Existing methods may struggle with local optima and exploration limitations.

Purpose of the Study:

  • To propose an improved sparrow search algorithm (ISSA) for threshold segmentation.
  • To enhance the exploration and exploitation capabilities of the sparrow search algorithm (SSA).
  • To apply the improved algorithm to 2-D maximum entropy image segmentation.

Main Methods:

  • Introduced nonlinear inertia weight to enhance local exploration in the SSA.
  • Incorporated Levy flight into the SSA to prevent premature convergence to local optima.
  • Validated the ISSA on benchmark functions and applied it to image segmentation using the 2-D maximum entropy method.

Main Results:

  • The improved sparrow search algorithm demonstrated superior performance on benchmark functions compared to other algorithms.
  • Experimental results showed improved segmentation quality for classical and medical images.
  • The method achieved higher peak signal-to-noise ratio and feature similarity.

Conclusions:

  • The proposed improved sparrow search algorithm effectively enhances image threshold segmentation.
  • The integration of nonlinear inertia weight and Levy flight improves the algorithm's robustness and performance.
  • This approach offers a promising solution for high-quality image segmentation in various applications.