College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Comput Biol Med. 2022 Mar;142:105181. doi: 10.1016/j.compbiomed.2021.105181. Epub 2022 Jan 3.
The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.
人工蜂群算法（ABC）已经成功应用于各种优化问题，但该算法在优化过程中仍然存在收敛速度慢和最优解质量差的问题。因此，本文提出了一种基于水平搜索机制和垂直搜索机制的改进人工蜂群算法（CCABC），以提高算法的性能。此外，本文还提出了一种基于 CCABC 的多层次阈值图像分割（MTIS）方法，以增强多层次阈值图像分割方法的有效性。为了验证所提出的 CCABC 算法的性能和改进的图像分割方法的性能。首先，本文通过使用 30 个基准函数将 CCABC 与 15 种同类型的算法进行比较，证明了 CCABC 算法本身的性能。然后，本文使用改进的多阈值分割方法对 COVID-19 X 射线图像进行分割，并与其他类似方案进行了详细比较。最后，通过分析适当的评价标准，确认了 CCABC 在 MTIS 中的融合是非常有效的，并肯定了新的 MTIS 方法具有很强的分割性能。
