井冈山大学学报（自然科学版）2025，Vol.46Issue(6)：71-78,8.DOI:10.3969/j.issn.1674-8085.2025.06.008
基于KL粗糙散度的医学图像多阈值分割方法
Multi-threshold segmentation method of medical image based on KL rough divergence
摘要
Abstract
Existing multi-threshold optimization approaches generally exhibit the contradictory relationship between computational complexity and segmentation accuracy,its crux of the problem lies in that traditional divergence measures are difficult to effectively describe the inherent fuzziness and regional heterogeneity of medical images.The research innovatively constructs a multi-threshold segmentation model based on kullback-leibler rough divergence(KL rough divergence).By integrating rough set approximation space theory,the model establishes an uncertainty quantification mechanism:utilizing upper and lower approximation operations of rough sets to analyze fuzzy characteristics at regional boundaries.Regarding optimization strategies,an improved grey wolf optimizer(GWO)is introduced,achieving a balance between global search and local optimization in threshold space through designed dynamic convergence factors and contribution ratio strategies.The experimental results demonstrate that the proposed algorithm outperforms other comparative algorithms across evaluation metrics including coefficient,sensitivity,and specificity,indicating its significant advantages in segmentation precision and accuracy.关键词
KL散度/粗糙集理论/KL粗糙散度/医学图像多阈值分割Key words
KL divergence/rough set theory/KL rough divergence/multi-threshold medical image segmentation分类
信息技术与安全科学引用本文复制引用
吴源文,柳雪飞,蒋越,张宇涵..基于KL粗糙散度的医学图像多阈值分割方法[J].井冈山大学学报（自然科学版）,2025,46(6):71-78,8.基金项目
国家自然科学基金项目(62461004) （62461004）
广西壮族自治区级大学生创新创业项目(S202410604066) （S202410604066）
