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Battle royale optimizer for multilevel image thresholding

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Abstract

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu’s and Kapur’s methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding.

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Authors and Affiliations

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

    Taymaz Akan & Mohammad Alfrad Nobel Bhuiyan

  2. Software Engineering Department, Ayvansaray University, Istanbul, Turkey

    Taymaz Akan

  3. Depto de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Mexico

    Diego Oliva & Marco Pérez-Cisneros

  4. Computerized Intelligence Systems Laboratory, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Ali-Reza Feizi-Derakhshi

  5. Computerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran

    Amir-Reza Feizi-Derakhshi

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  1. Taymaz Akan
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Akan, T., Oliva, D., Feizi-Derakhshi, AR. et al. Battle royale optimizer for multilevel image thresholding. J Supercomput 80, 5298–5340 (2024). https://doi.org/10.1007/s11227-023-05664-8

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