摘要
由于飞蛾扑火优化(MFO)算法在解决实际优化问题时仍会表现出易陷局部最优、收敛停滞等不足,针对MRI图像较难分割问题,本文提出了一种基于混沌飞蛾扑火(CMFO)的膝盖MRI分割算法.为辅助医生阅片,提高诊断效率和准确率,实验先将膝盖MRI图像选作研究对象,然后将CMFO算法与最大阈值熵相结合应用到医学MRI图像多阈值分割领域.为突出基于CMFO的膝盖MRI分割的优势,引入了SOA,BFOA和M FO算法作对比实验,结果表明:CM FO算法能有效改善M FO的优化性能,而且对膝盖M RI图像分割具有更好的适用性和优越性.
The moth-flame optimization(MFO)algorithm may show shortcomings such as the local optimum and convergence stagnation when solving the practical optimization problem.Therefore,aiming at the problem that MRI(magnetic resonance imaging)images are difficult to segment,this paper proposes a chaotic moth-flame optimization(CMFO)algorithm.In order to help doctors read the MRI films and improve the efficiency and accuracy of diagnosis,the knee MRI images are selected as research objects during the experiments.Then,CMFO algorithm and maximum threshold entropy are combined and applied into multi-threshold segmentation.In order to present the advantages of the CMFO algorithm proposed,SOA,BFOA and MFO algorithms are introduced under the same condition for comparative experiments.The experimental results show that CMFO can effectively improve the optimal performance of MFO,and has better applicability and advantages for knee MRI image segmentation.
作者
王海芳
祁超飞
张瑶
朱亚锟
WANG Hai-fang;QI Chao-fei;ZHANG Yao;ZHU Ya-kun(School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第3期326-331,共6页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61703079)
秦皇岛市大学生科技创新创业专项基金资助项目(2018-79121).
关键词
混沌策略
膝盖MRI图像
最大阈值熵
多阈值分割
飞蛾扑火优化
chaotic strategy
knee MRI image
maximum threshold entropy
multilevel-threshold segmentation
moth-flame optimization(MFO)
