摘要
针对显微镜下乳腺癌病理组织图像结构复杂,细胞边界模糊等情况,基于传统的阈值分割在乳腺癌图像的分割应用中不能很好地实现把病灶区准确分离开来的问题,提出一种基于增强蒲公英优化算法(IDO)的乳腺癌图像多阈值分割方法.该方法引入IDO计算类间方差的最大值(Otsu)作为目标函数寻找最佳阈值,IDO建立回守策略解决传统蒲公英算法(DO)无限制搜索,超出像素范围的问题;引入对立式学习(OBL)避免算法陷入局部最优.实验结果表明,与哈里斯鹰算法(HHO)、人工猩猩部队优化算法(GTO)、传统蒲公英优化算法(DO)、海洋捕食者算法(MPA)相比,在相同阈值个数情况下IDO算法适应度值最大、收敛最快,并且在峰值信噪比(PSNR)、结构相似度(FSIM)、特征相似度(SSIM)这3个性能指标上也比其他对比算法更具有优势.
In the context of complex structures and blurred cell boundaries in microscopic breast cancer histopathological images,traditional threshold-based segmentation faces challenges in accurately separating lesion areas of breast cancer images.To address this issue,this study proposes a multi-threshold segmentation method for breast cancer images based on the improved dandelion optimization algorithm(IDO).This method introduces the IDO to calculate the maximum inter-class variance(Otsu)as the objective function for finding the optimal thresholds.The IDO incorporates a defensive strategy to address the issue of unbounded search in the traditional dandelion optimization algorithm(DO)that extends beyond pixel ranges.Additionally,opposition-based learning(OBL)is introduced to prevent the algorithm from getting trapped in local optima.The experimental results indicate that compared with the Harris Hawks optimization(HHO),gorilla troop optimization(GTO),traditional DO,and marine predators algorithm(MPA),the IDO algorithm achieves the highest fitness value and fastest convergence under the same number of threshold levels.Moreover,it outperforms other comparative algorithms in terms of peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and feature similarity index(FSIM).
作者
王正红
王丹
胡容俊
WANG Zheng-Hong;WANG Dan;HU Rong-Jun(College of Computer Science and Technology,Beihua University,Jilin 132013,China)
出处
《计算机系统应用》
2024年第1期148-156,共9页
Computer Systems & Applications
关键词
增强蒲公英优化算法
多阈值分割
乳腺癌图像
对立式学习
回守策略
improved dandelion algorithm(IDO)
multi-threshold segmentation
breast cancer image
opposition-based learning(OBL)
fall-back strategy
