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An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation.

Essam H Houssein1, Gaber M Mohamed1, Nagwan Abdel Samee2

  • 1Faculty of Computers and Information, Minia University, Minia 61519, Egypt.

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|May 16, 2023
PubMed
Summary
This summary is machine-generated.

A new meta-heuristic algorithm, mSAR, enhances image segmentation by improving multi-level thresholding. This method, combining search and rescue optimization with opposition-based learning, achieves superior results in blood-cell image segmentation.

Keywords:
fuzzy entropy and Otsu methodimage segmentationmeta-heuristicsmulti-level thresholdingopposition-based learningsearch and rescue optimization algorithm

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Traditional multi-level thresholding methods struggle with optimal image segmentation.
  • Meta-heuristic algorithms offer potential for complex optimization problems.

Purpose of the Study:

  • To develop an efficient multi-level thresholding technique for image segmentation.
  • To introduce an enhanced search and rescue optimization algorithm (mSAR) for improved segmentation accuracy.

Main Methods:

  • Proposed an opposition-based learning (OBL) enhanced search and rescue optimization (SAR) algorithm, termed mSAR.
  • Applied mSAR to multi-level thresholding for blood-cell image segmentation.
  • Evaluated mSAR against various algorithms using fuzzy entropy and Otsu's method.

Main Results:

  • mSAR demonstrated superior performance in image segmentation compared to existing algorithms.
  • The integration of OBL significantly improved SAR's convergence speed and solution quality.
  • Experiments confirmed mSAR's efficiency in preserving image features.

Conclusions:

  • The proposed mSAR algorithm is highly effective for multi-level thresholding image segmentation.
  • mSAR offers a robust solution for accurate blood-cell image analysis.
  • Combining OBL with SAR provides a powerful approach for optimization tasks.