基于混沌飞蛾扑火优化的膝盖MRI分割算法

doi:10.12068/j.issn.1005-3026.2020.03.005

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (3): 326-331.DOI: 10.12068/j.issn.1005-3026.2020.03.005

基于混沌飞蛾扑火优化的膝盖MRI分割算法

王海芳，祁超飞，张瑶，朱亚锟

(东北大学秦皇岛分校控制工程学院，河北秦皇岛066004)

收稿日期:2019-08-04 修回日期:2019-08-04 出版日期:2020-03-15 发布日期:2020-04-10
通讯作者: 王海芳
作者简介:王海芳(1976-)，男，山西高平人，东北大学秦皇岛分校副教授.
基金资助:
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(2018-79，121).

Knee MRI Segmentation Algorithm Based on Chaotic Moth-Flame Optimization

WANG Hai-fang， QI Chao-fei， ZHANG Yao， ZHU Ya-kun

School of Control Engineering， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China.

Received:2019-08-04 Revised:2019-08-04 Online:2020-03-15 Published:2020-04-10
Contact: WANG Hai-fang
About author:-
Supported by:
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摘要/Abstract

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.

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

Abstract: 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.

Key words: chaotic strategy, knee MRI image, maximum threshold entropy, multilevel-threshold segmentation, moth-flame optimization (MFO)

中图分类号:

TG391.41

王海芳，祁超飞，张瑶，朱亚锟. 基于混沌飞蛾扑火优化的膝盖MRI分割算法[J]. 东北大学学报:自然科学版, 2020, 41(3): 326-331.

WANG Hai-fang， QI Chao-fei， ZHANG Yao， ZHU Ya-kun. Knee MRI Segmentation Algorithm Based on Chaotic Moth-Flame Optimization[J]. Journal of Northeastern University Natural Science, 2020, 41(3): 326-331.

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基于混沌飞蛾扑火优化的膝盖MRI分割算法

Knee MRI Segmentation Algorithm Based on Chaotic Moth-Flame Optimization

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