Knee MRI Segmentation Algorithm Based on Chaotic Moth-Flame Optimization

被引：0
|
作者：
Wang H.-F. [1 ]
Qi C.-F. [1 ]
Zhang Y. [1 ]
Zhu Y.-K. [1 ]
机构：
[1] School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2020年 / 41卷 / 03期
关键词：
Chaotic strategy; Knee MRI image; Maximum threshold entropy; Moth-flame optimization (MFO); Multilevel-threshold segmentation;
D O I：
10.12068/j.issn.1005-3026.2020.03.005
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：326 / 331
页数：5
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