College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, Jilin, China.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sci Rep. 2024 Jun 9;14(1):13239. doi: 10.1038/s41598-024-63739-9.
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.
图像分割技术在辅助 COVID-19 诊断中起着至关重要的作用。多阈值图像分割方法因其计算简单和操作效率高而受到青睐。多阈值图像分割中的现有阈值选择技术，如基于穷举的 Kapur 方法，往往会影响效率和准确性。鲸鱼优化算法 (WOA) 在解决这一挑战方面表现出了潜力，但仍存在一些问题，包括在 COVID-19 阈值图像分割中的稳定性差、效率低和准确性低。为了解决这些问题，我们引入了一种基于拉丁超立方采样初始化的多策略增强鲸鱼优化算法 (CAGWOA)。它结合了 COS 采样初始化策略 (COSI)、自适应全局搜索方法 (GS) 和全维邻域机制 (ADN)。COSI 利用拉丁超立方采样创建的概率密度函数，确保解决方案空间的均匀覆盖，从而提高分割模型的稳定性。GS 拓宽了探索范围，以克服迭代过程中的停滞现象，并提高分割效率。ADN 则在最优个体周围细化收敛精度，以提高分割精度。通过在各种基准函数测试集上的实验，验证了 CAGWOA 的性能。此外，我们将 CAGWOA 与类似方法一起应用于多阈值图像分割模型中，对感染患者的 X 射线肺部图像进行了对比实验。结果表明，CAGWOA 具有优越性，包括更好地保留图像细节、清晰的分割边界以及在不同阈值水平下的适应性。
