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Comprehensive evaluation of optimization algorithms for medical image segmentation.

Nijad A Al-Najdawi1, Ali F Al-Shawabkeh2, Sara Tedmori3

  • 1Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117, Jordan. n.al-najdawi@bau.edu.jo.

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PubMed
Summary

This study optimizes Otsu's method for medical image segmentation using various algorithms. The goal is to reduce computational cost and convergence time for faster, accurate disease diagnosis and research.

Keywords:
Computational efficiencyMedical imagesMulti-level thresholdingOptimization algorithmsSegmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Biology

Background:

  • Medical image segmentation is vital for disease diagnosis and research.
  • Multilevel thresholding methods offer superior performance in image segmentation.
  • Classical methods like Otsu's are accurate but computationally expensive for multilevel thresholding.

Purpose of the Study:

  • To integrate optimization algorithms with Otsu's method for efficient multilevel thresholding.
  • To reduce the computational cost and convergence time of Otsu's method.
  • To maintain high segmentation quality while improving computational efficiency.

Main Methods:

  • Integration of established optimization algorithms with Otsu's thresholding method.
  • Experimental evaluation on public datasets, including the TCIA COVID-19-AR collection.
  • Comparative analysis of computational cost, convergence time, and segmentation accuracy.

Main Results:

  • Identification of optimization algorithms that significantly reduce computational demands.
  • Demonstration of substantial reductions in convergence time for multilevel thresholding.
  • Validation of maintained segmentation quality with optimized Otsu's method.

Conclusions:

  • Optimized Otsu's method offers a computationally efficient solution for medical image segmentation.
  • Selected algorithms effectively address the computational challenges of multilevel thresholding.
  • This approach enhances the practical application of image segmentation in healthcare.