基于MFc算法的fMRI数据集不平衡问题的处理  

Treatment of ill-balanced datasets of fMRI with Modified Fuzzy c-means method

作　　者：顾杰斌[1] 曹志彤[1] 郑希[1] 

机构地区：[1]浙江大学应用物理研究所,浙江杭州310027

出　　处：《中国医学影像技术》2005年第8期1285-1288,共4页Chinese Journal of Medical Imaging Technology

摘　　要：在fMRI数据中,因为激活体素(voxel)的数目远小于总的体素数目,由此产生了数据的不平衡性问题。以往解决此问题的方法除了应用大脑本身的生理结构来限制体素的数目外,统计方法常被应用于去掉那些肯定不激活的体素。本文章提出了一种新的解决数据不平衡问题的方法,用改进模糊c均值(MFc)方法将总的数据分成两个子集,而激活的体素总是聚在一个子集中,由此可以将需要进行分析运算的体素数目减至一半,这样不但可以有效的解决fMRI数据存在的不平衡问题,而且也提高了聚类分析的效率。MFc方法与统计方法的最大不同是,它是完全数据驱动的。In fMRI dataset, the population of actived voxels is always much less than the total population of the voxels, and that produced an ill-balanced dataset. Some methods, such as limiting the analysis to the gray matter voxels where the BOLD signal is expected and removing the voxels that is absolutely non-actived based on statistical criteria, have been used to treat the ill balanced dataset. In this article, a new method, Modified Fuzzy c-means （MFc） method, is proposed to treat the ill-balanced dataset of fMRI. The MFc method is used to classify the voxels into two clusters with nearly the same population and all actived voxels are contained in one cluster. Thus we get nearly half voxels to analysis and the ill-balanced dataset can be treated. The efficiency of clustering analysis is also boosted. The main difference from other statistical methods is that it is data-driven.

关 键 词：改进模糊c均值 功能磁共振成像 

分 类 号：O411[理学—理论物理]