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Battle royale optimizer for multilevel image thresholding.

Taymaz Akan1,2, Diego Oliva3, Ali-Reza Feizi-Derakhshi4

  • 1Department of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, USA.

The Journal of Supercomputing
|August 18, 2025
PubMed
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This summary is machine-generated.

The Battle Royal Optimizer (BRO) effectively optimizes multilevel image thresholding for superior image segmentation. This new method outperforms existing techniques in key performance metrics, offering a promising solution for image processing tasks.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Image segmentation partitions images into meaningful regions, vital for computer vision and medical imaging.
  • Histogram-based thresholding, including Otsu's and Kapur's methods, is a common technique for image segmentation.
  • Extending these methods to multilevel thresholding requires significant iterations, often necessitating optimization algorithms.

Purpose of the Study:

  • To apply the Battle Royal Optimizer (BRO) for optimizing multilevel image thresholding.
  • To evaluate BRO's effectiveness in image segmentation using the Berkeley segmentation dataset.
  • To compare BRO's performance against other state-of-the-art optimization methods.

Main Methods:

  • Multilevel image thresholding using the Battle Royal Optimizer (BRO).
Keywords:
Battle royal optimization algorithmImage segmentationMetaheuristicsMultilevel thresholding

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  • Segmentation of various images from the Berkeley segmentation dataset.
  • Comparative analysis with existing optimization-based methods for image segmentation.

    • Main Results:

    • BRO achieved optimal threshold values for multilevel image thresholding.
    • The method demonstrated superior performance in terms of fitness value, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation.
    • BRO outperformed other state-of-the-art optimization techniques.

    Conclusions:

    • The Battle Royal Optimizer (BRO) is a highly effective tool for multilevel image thresholding.
    • BRO presents a promising and efficient solution for image segmentation tasks.
    • This study highlights BRO's potential to advance image processing applications.