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Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-05-29 , DOI: 10.1007/s12652-020-02143-3
Pankaj Upadhyay , Jitender Kumar Chhabra

Thresholding is one of the highly accepted methods for image segmentation because of its simplicity in nature. The selection of optimal threshold values in threshold-based image segmentation is a tricky job. In this work, Kapur’s entropy is used to solve the optimal threshold selection problem and a multistage hybrid nature-inspired optimization algorithm is used to get the best possible parameters for this objective function. The proposed method has three stages namely: primary stage, booster stage and final stage. Particle swarm optimization (PSO), artificial bee colony optimization (ABC) and ant colony optimization (ACO) used at these stages. In this proposed work various benchmarked images have been used for experimentation purpose. The proposed method has been assessed and performance is compared with well-known metaheuristic optimization like PSO, ABC, ACO, classical Otsu thresholding method and modified bacterial foraging optimization qualitatively and quantitatively. Peak signal to noise ratio and Structure Similarity Index are used for qualitative assessment. Wilcoxon p value test, ANOVA test and box plots are used for statistical analysis. The experimental results showed that the proposed method performed better in terms of quality and consistency.



中文翻译:

新的多阶段混合优化算法的基于多阈值的图像分割

阈值处理由于其本质上的简单性而成为一种被广泛接受的图像分割方法。在基于阈值的图像分割中选择最佳阈值是一项棘手的工作。在这项工作中,Kapur的熵被用来解决最佳阈值选择问题,而多级混合自然启发式优化算法被用来为该目标函数获得最佳的参数。所提出的方法包括三个阶段:初级阶段,增强阶段和最终阶段。在这些阶段使用的粒子群优化(PSO),人工蜂群优化(ABC)和蚁群优化(ACO)。在这项拟议的工作中,各种基准图像已用于实验目的。已对该方法进行了评估,并将其与PSO,ABC,ACO,经典的Otsu阈值方法和改良的细菌觅食优化进行了定性和定量的比较,比较了性能。峰信噪比和结构相似性指数用于定性评估。Wilcoxon p值检验,ANOVA检验和箱形图用于统计分析。实验结果表明,该方法在质量和一致性方面表现更好。

更新日期:2020-05-29
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