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A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images.

Marwa M Emam1, Essam H Houssein1, Rania M Ghoniem2

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

Computers in Biology and Medicine
|December 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a modified reptile search algorithm (mRSA) that integrates the RUNge Kutta optimizer to enhance global optimization. The mRSA algorithm improves convergence speed and balances exploration-exploitation for better results in image segmentation.

Keywords:
Brain tumor imagesGlobal optimizationImage segmentationMetaheuristicsMulti-level thresholdingRUNge kutta optimizerReptile search algorithm

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Image Processing

Background:

  • Metaheuristic algorithms like the reptile search algorithm (RSA) often suffer from inadequate diversity and local optima.
  • The RUNge Kutta optimizer (RUN) offers enhanced solution quality (ESQ) and a randomized scale factor (SF) for improved exploration-exploitation balance.
  • Existing metaheuristic algorithms require enhancements to overcome inherent limitations in solving complex optimization problems.

Purpose of the Study:

  • To propose a modified reptile search algorithm (mRSA) by integrating the RUNge Kutta optimizer with the RSA.
  • To enhance the performance of the RSA algorithm in terms of convergence speed, solution quality, and exploration-exploitation balance.
  • To evaluate the effectiveness of mRSA for global optimization and multilevel image segmentation, specifically in MRI brain image analysis.

Main Methods:

  • The modified reptile search algorithm (mRSA) was developed by combining the RSA with the RUN optimizer, incorporating its ESQ mechanism and SF.
  • mRSA was tested on CEC'2020 benchmark functions to assess its statistical, convergence, and diversity measurements against other algorithms.
  • The algorithm was applied to multilevel thresholding for magnetic resonance imaging (MRI) brain image segmentation.

Main Results:

  • mRSA demonstrated superior search capabilities compared to the original RSA and other popular algorithms on benchmark functions.
  • Experimental results confirmed mRSA's strong optimization ability and its effectiveness in improving convergence speed and bypassing local optima.
  • The mRSA method achieved more successful multilevel thresholding segmentation for MRI brain images, outperforming comparison methods.

Conclusions:

  • The proposed mRSA effectively mitigates the drawbacks of the original RSA, offering enhanced performance in global optimization.
  • mRSA shows significant improvements in convergence speed, solution quality, and the balance between exploration and exploitation.
  • The algorithm is a successful tool for multilevel image segmentation, particularly for MRI brain scans, outperforming existing methods.