Big Data Mining and Multimedia Research Group, Centre for Data Analytics and Cybersecurity (CDAC), Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates; Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt.
Computer Science Department, University of Sharjah, Sharjah 27272, United Arab Emirates.
Comput Biol Med. 2024 Sep;180:109011. doi: 10.1016/j.compbiomed.2024.109011. Epub 2024 Aug 14.
Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur's entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC'22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO's superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method's effectiveness in solving image segmentation problems.
图像分割在医学图像分析中起着关键作用，特别是对于准确分离肿瘤和病变。有效的分割可以提高诊断精度并促进定量分析，这对医学专业人员至关重要。然而，传统的分割方法由于涉及到计算复杂性，通常在多级阈值处理方面存在困难。因此，确定最优的阈值集是一个 NP 难问题，这突出表明需要有效的优化策略来克服这些挑战。本文提出了一种基于多阈值图像分割（MTIS）的方法，该方法结合了差分进化（DE）和小龙虾优化算法（COA）的混合方法，称为 HADECO。该方法利用二维（2D）Kapur 熵和 2D 直方图，旨在提高后续图像分析和诊断的效率和准确性。HADECO 是一种混合算法，通过基于预定义规则交换信息来结合 DE 和 COA，利用两者的优势获得更好的优化结果。它使用拉丁超立方抽样（LHS）生成高质量的初始种群。HADECO 引入了一种改进的 DE 算法（IDE），该算法对 DE 参数进行自适应和动态调整，并采用新的突变策略来增强其搜索能力。此外，它还包含一种自适应 COA（ACOA），该算法对切换概率参数进行动态调整，有效地平衡了探索和开发。为了评估 HADECO 的有效性，首先使用 CEC'22 基准函数评估其性能。然后使用 Wilcoxon 符号秩检验（WSRT）和 Friedman 检验（FT）对 HADECO 与几种现代算法进行了评估，并对结果进行了整合。研究结果表明，HADECO 具有优越的优化能力，其平均 Friedman 排名最低为 1.08。此外，还使用 MRI 图像对膝关节和 CT 扫描对脑颅内出血（ICH）进行了 HADECO 基于的 MTIS 方法评估。脑出血图像分割的定量结果表明，所提出的方法在 6 级阈值下达到了 1.5 的平均峰值信噪比（PSNR）和 1.7 的特征相似性指数（FSIM）。在膝关节图像分割中，它在 5 级阈值下达到了 1.3 的平均 PSNR 和 1.2 的 FSIM，证明了该方法在解决图像分割问题方面的有效性。
