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Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation.

Laith Abualigah1,2, Nada Khalil Al-Okbi3, Mohamed Abd Elaziz4,5,6,7

  • 1Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan.

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Summary
This summary is machine-generated.

A new hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA), called MPASSA, effectively determines optimal multilevel image segmentation thresholds. This method improves accuracy and efficiency over traditional techniques for digital image processing.

Keywords:
Image segmentationMarine Predator algorithmMeta-heuristic algorithmsMultilevel thresholdingSalp Swarm algorithm

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Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Optimization Algorithms

Background:

  • Pixel intensity is crucial for digital image processing, particularly in determining optimal image segmentation thresholds.
  • Existing methods like Otsu and Kapur are effective for single or bi-level thresholds but struggle with multilevel segmentation due to computational cost and accuracy issues.

Purpose of the Study:

  • To address the limitations of conventional methods in multilevel image segmentation thresholding.
  • To propose an efficient and accurate hybrid optimization algorithm for determining optimal multilevel thresholds.

Main Methods:

  • A novel hybrid algorithm, Marine Predators Algorithm (MPA) integrated with Salp Swarm Algorithm (SSA) (MPASSA), was developed.
  • The MPASSA algorithm utilizes image histograms to represent segmentation solutions.
  • Performance was evaluated using standard metrics like fitness function, time consumption, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM).

Main Results:

  • The proposed MPASSA algorithm demonstrated superior performance in multilevel image segmentation thresholding.
  • MPASSA achieved better results compared to other established optimization algorithms on benchmark images.
  • The method effectively balances accuracy and computational efficiency for complex segmentation tasks.

Conclusions:

  • The hybrid MPASSA algorithm offers a significant advancement for optimal multilevel image segmentation.
  • This approach overcomes the drawbacks of traditional methods, providing a more robust solution for image processing applications.
  • MPASSA shows promise for enhancing the quality and efficiency of image segmentation.