首页 | 本学科首页   官方微博 | 高级检索  
检索        

模糊神经网络在颅脑磁共振图像分割中的应用研究
引用本文:黄永锋,岑康,司京玉,陈瑛,顾顺德,章鲁.模糊神经网络在颅脑磁共振图像分割中的应用研究[J].中国生物医学工程学报,2003,22(6):508-512.
作者姓名:黄永锋  岑康  司京玉  陈瑛  顾顺德  章鲁
作者单位:上海第二医科大学,上海,200025
摘    要:由于颅脑磁共振图像分割的效果受制于解剖结构和成像过程引起的边缘模糊和噪声,本研究结合神经网络和模糊逻辑技术,提出一种基于模糊神经网络的颅脑磁共振图像的半自动分割算法。分割实验的结果表明,在同等条件下,模糊神经网络分割算法的收敛速度比BP神经网络分割算法快三倍以上;与最大似然法、模糊c均值聚类和BP神经网络分割算法相比,FNN分割算法的抗噪声和抗模糊能力更强。

关 键 词:颅脑  磁共振图像  图像分割  模糊神经网络
文章编号:0258-8021(2003)-06-508-05
修稿时间:2002年1月18日

THE APPLICATION OF FUZZY NEURAL NETWORKS IN THE SEGMENTATION OF HEAD MRI
HUANG Yong-feng,CEN Kang,SI Jiang-yu,CHEN Ying,GU Shun-de,ZHANG Lu.THE APPLICATION OF FUZZY NEURAL NETWORKS IN THE SEGMENTATION OF HEAD MRI[J].Chinese Journal of Biomedical Engineering,2003,22(6):508-512.
Authors:HUANG Yong-feng  CEN Kang  SI Jiang-yu  CHEN Ying  GU Shun-de  ZHANG Lu
Abstract:The result of head MRIs segmentation is vulnerable to the edge blur and noise caused by the anatomical structure of head and the imaging process. By combining neural networks with fuzzy logic techinique, we presented a semi-automated method of segmentation of multispectral head MRIs based fuzzy neural networks(FNN). Experimental results showed that FNN was three times faster than the backprogagation neural networks(BP) under the same condition, and was more robust against edge blur and noise than maximum likelihood method(MLM), fuzzy c-means cluster(FCM) and BP.
Keywords:Head MRIs  Segmentation  Fuzzy neural networks
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号