摘要
将小波变换和聚类方法结合,提出了一种基于小波域的f MRI激活区聚类检测方法.该方法首先采用多步决策的思想,对f MRI图像进行模糊C均值聚类,去除f MRI数据的不平衡问题;之后利用平稳小波变换,对数据进行小波分解,提取出感兴趣的频率范围的信息,并在小波域对体素用改进的K均值聚类算法进行分析,从而找出大脑中因任务刺激而激活的区域.对多名被试进行了视觉刺激实验,并与目前主流的SPM方法进行了比较,结果表明本文方法较SPM方法具有更高的合理性,对大脑功能连通性检测具有指导意义和实用价值.
In this paper, wavelet transform and clustering method are combined in order to detect the task stimulation-caused activation areas of a human brain in f MRI. According to the idea of multi-step decision, firstly, we perform fuzzy-c-means clustering in the f MRI image to solve the ill-balanced problem of the data. Secondly, we use the stationary wavelet transform to decompose the data, and extract those data of interested frequency, and then the improved k-means clustering algorithm is proposed to analyze these data in the wavelet domain. The experimental results with several subjects show that the visual stimulation-caused activation areas of human brain can be detected. Compared with the popular SPM method, the proposed method in this paper is more reasonable, and has directive significance and practical value on the functional connectivity detection of human brains.
出处
《计算机系统应用》
2016年第1期214-218,共5页
Computer Systems & Applications
基金
国家自然科学基金(31170952
31470954)
上海科委项目(14590501700)
关键词
小波变换
聚类
不平衡问题
功能连通性检测
功能磁共振成像
wavelet transform
clustering
Ill-balanced problem
functional connectivity detection
functional magnetic resonance imaging
