Akan Taymaz, Oliva Diego, Feizi-Derakhshi Ali-Reza, Feizi-Derakhshi Amir-Reza, Pérez-Cisneros Marco, Bhuiyan Mohammad Alfrad Nobel
Department of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, USA.
Software Engineering Department, Ayvansaray University, Istanbul, Turkey.
J Supercomput. 2024 Mar;80(4):5298-5340. doi: 10.1007/s11227-023-05664-8. Epub 2023 Sep 26.
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.
图像分割是将图像划分为有意义区域的过程，是图像处理中的一个基本步骤，对于计算机视觉、医学成像和目标识别等应用至关重要。图像分割是图像处理的一个关键步骤，直接影响其成败。在用于图像分割的方法中，基于直方图的阈值处理很普遍。基于直方图的阈值处理的两种著名方法是灰度图像中的大津法和卡普尔法，它们分别最大化类间方差和熵测度。这两种技术都是为二值阈值处理而引入的。然而，这些技术可以扩展到多级图像阈值处理。为此，需要大量迭代来确定精确的阈值。为此，人们使用了各种优化技术来克服这一缺点。最近，一种名为皇家战斗优化器（BRO）的新优化算法已发表，该算法被证明能有效解决各种优化任务。在本研究中，BRO已被应用于在多级图像阈值处理中产生最佳阈值。本文还展示了BRO在对来自标准公开可用的伯克利分割数据集的各种图像进行图像分割方面的有效性。我们将BRO的性能与其他基于优化的先进方法进行比较，结果表明，在适应度值、峰值信噪比、结构相似性指数方法、特征相似性指数方法（FSIM）、彩色FSIM（FSIMc）和标准差方面，BRO优于这些方法。这些结果强调了BRO作为图像分割任务的一种有前景的解决方案的潜力，特别是通过其在多级阈值处理方面的有效实现。
