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Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid

Khalid M Hosny1, Asmaa M Khalid1, Hanaa M Hamza1

  • 1Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt.

Neural Computing & Applications
|October 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Coronavirus Optimization algorithm for multilevel thresholding in image segmentation. The enhanced method improves solution diversity and accuracy, outperforming existing algorithms on benchmark and satellite images.

Keywords:
Image segmentationMetaheuristicOptimizationSatelliteThresholding

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Image segmentation is crucial for image analysis.
  • Multilevel thresholding is a common technique but faces computational challenges with high thresholds.
  • Existing algorithms may lack efficiency and solution diversity.

Purpose of the Study:

  • To develop an efficient and accurate multilevel thresholding method for image segmentation.
  • To enhance the Coronavirus Optimization algorithm for improved performance.
  • To address the computational complexity of traditional thresholding methods.

Main Methods:

  • A modified Coronavirus Optimization algorithm incorporating chaotic maps for initialization.
  • A novel fitness function combining Otsu's and Kapur's entropy for optimal threshold determination.
  • Evaluation using benchmark and satellite image datasets with various metrics (MSE, PSNR, SSIM, FSIM, NCC).

Main Results:

  • The proposed algorithm demonstrated superior performance in image segmentation compared to eleven other metaheuristics.
  • Enhanced solution diversity was achieved through the integration of chaotic maps.
  • The hybrid fitness function effectively identified optimal threshold values.

Conclusions:

  • The modified Coronavirus Optimization algorithm offers a robust and efficient solution for multilevel image segmentation.
  • The integration of chaotic maps and hybrid entropy significantly improves segmentation quality and computational efficiency.
  • This approach represents a notable advancement in image processing applications requiring accurate segmentation.