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An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image

Shubham Mahajan1, Nitin Mittal2, Rohit Salgotra3

  • 1School of Electronics & Communication, Shri Mata Vaishno Devi University, Katra-182320, India.

Computational and Mathematical Methods in Medicine
|February 8, 2022
PubMed
Summary
This summary is machine-generated.

The adaptive salp swarm algorithm (ASSA) improves image segmentation by combining with type II fuzzy entropy. This novel approach enhances multilevel thresholding for better image analysis and performance.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • The Salp Swarm Algorithm (SSA) is an effective swarm intelligence algorithm but faces challenges in exploitation, convergence, and exploration stability.
  • Image segmentation, particularly multilevel thresholding, is crucial for image analysis but struggles with determining optimal threshold values.
  • Existing thresholding methods often rely on traditional objective functions, posing difficulties in selecting the appropriate number of thresholds.

Purpose of the Study:

  • To introduce an improved Salp Swarm Algorithm (ASSA) to address the limitations of the basic SSA.
  • To propose a novel method for multilevel image thresholding using ASSA combined with type II fuzzy entropy.
  • To evaluate the performance and robustness of the proposed ASSA-based multilevel image segmentation technique.

Main Methods:

  • An enhanced Salp Swarm Algorithm (ASSA) was developed to improve exploitation and convergence.
  • Type II fuzzy entropy was integrated with ASSA to serve as the objective function for image segmentation.
  • The proposed method was applied to multilevel image thresholding, utilizing image histograms for analysis.

Main Results:

  • The adaptive salp swarm algorithm (ASSA) demonstrated improved performance over the basic SSA in addressing exploitation and convergence issues.
  • The integration of ASSA with type II fuzzy entropy proved effective for multilevel image thresholding.
  • Experimental results on various images showed the proposed method's efficiency and robustness in image segmentation tasks.

Conclusions:

  • The proposed ASSA combined with type II fuzzy entropy offers a robust and efficient solution for multilevel image thresholding.
  • This approach overcomes the limitations of traditional thresholding methods and the basic SSA.
  • The study validates the effectiveness of the enhanced algorithm for diverse image segmentation applications.