基于混沌飞蛾扑火优化的膝盖MRI分割算法

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MFO算法作对比实验，结果表明:CMFO算法能有效改善MFO的优化性能，而且对膝盖MRI图像分割具有更好的适用性和优越性.

Knee MRI Segmentation Algorithm Based on Chaotic Moth-Flame Optimization

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.