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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images.

Essam H Houssein1, Marwa M Emam1, Abdelmgeid A Ali1

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

Neural Computing & Applications
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the MRFO-OBL algorithm for segmenting COVID-19 CT scans, improving accuracy and robustness. The enhanced method outperforms existing algorithms in identifying disease-related regions.

Keywords:
COVID-19 CT imagesManta ray foraging optimizationMeta-heuristics algorithmsMultilevel thresholding image segmentationOtsu’s method

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Coronavirus disease 2019 (COVID-19) presents a global health challenge.
  • Computed tomography (CT) imaging is crucial for COVID-19 diagnosis.
  • Image segmentation enhances the accuracy of COVID-19 classification from CT scans.

Purpose of the Study:

  • To propose an efficient Manta Ray Foraging Optimization (MRFO) algorithm enhanced with Opposition-Based Learning (OBL), termed MRFO-OBL.
  • To apply MRFO-OBL for multilevel thresholding-based image segmentation of COVID-19 CT images.
  • To evaluate and compare the performance of MRFO-OBL against other meta-heuristic algorithms.

Main Methods:

  • Implementation of the MRFO-OBL algorithm incorporating Opposition-Based Learning in the initialization phase.
  • Application of the algorithm to segment regions of interest in COVID-19 CT images using multilevel thresholding.
  • Comparative analysis using Otsu's method against six other meta-heuristic algorithms.

Main Results:

  • MRFO-OBL demonstrated superior performance in segmentation quality and consistency.
  • The algorithm achieved high scores in evaluation metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).
  • MRFO-OBL exhibited greater robustness in image segmentation compared to all other tested algorithms.

Conclusions:

  • The proposed MRFO-OBL algorithm is an effective and robust method for segmenting COVID-19 CT images.
  • MRFO-OBL significantly outperforms the original MRFO and other comparative algorithms.
  • This enhanced optimization technique improves the identification of relevant features in medical imaging for disease detection.