Search

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)