海军航空工程学院学报2017，Vol.32Issue(3)：261-264,283,5.DOI:10.7682/j.issn.1673-1522.2017.03.001
多个空间高斯源信号情况下成组三维图像特征提取方法
Group Three-Dimensional Image Feature Extraction Method of Multiple Spatial Gaussian Source Signals
摘要
Abstract
A new method was introduced for the problem of blind source separation with multiple Gaussian signal sources in functional magnetic resonance imaging data. The group inference framework of this method refered to the group ICA al-gorithm in the GIFT toolbox. The specific canonical correlation analysis method was the BSS-CCA algorithm proposed by Friman et al. The simulation results showed that the method could well identify two spatial Gaussian signals mixed with hu-man brain magnetic resonance imaging data. The results showed that the method could be used to verify the effectiveness of the method. Group BSS-CCA had a high practical value in the study of functional magnetic resonance imaging data of human brain.关键词
功能磁共振成像/盲源分离/典型相关分析/成组分析Key words
functional magnetic resonance imaging/blind source separation/canonical correlation analysis/group analysis分类
信息技术与安全科学引用本文复制引用
武兴杰,曾令李,李明,沈辉,王晓红,胡德文..多个空间高斯源信号情况下成组三维图像特征提取方法[J].海军航空工程学院学报,2017,32(3):261-264,283,5.基金项目
国家自然科学基金资助项目(61503397 （）
61420106001 （）
61375111) （）
