| 多个空间高斯源信号情况下成组三维图像特征提取方法 |
| |
| 引用本文： | 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. |
| |
| 作者姓名： | 武兴杰 曾令李 李明 沈辉 王晓红 胡德文 |
| |
| 作者单位： | 海军航空工程学院控制工程系,山东烟台264001,国防科学技术大学机电一体与自动化学院自控系,长沙410073,国防科学技术大学机电一体与自动化学院自控系,长沙410073,国防科学技术大学机电一体与自动化学院自控系,长沙410073,解放军第107医院信息科,山东烟台264000,国防科学技术大学机电一体与自动化学院自控系,长沙410073 |
| |
| 摘 要： | 针对功能磁共振成像数据中含有多个高斯信号源的盲源分离问题,介绍了一种成组典型相关分析方法(Group BSS-CCA)。这个方法的组分析框架参照了GIFT工具箱中的Group ICA算法,具体的典型相关分析方法应用的是Friman等人提出的BSS-CCA算法。以验证该方法的有效性为目的,设计了仿真实验;结果表明,该方法能较好识别出混合在人脑功能磁共振成像数据的2个空间高斯信号。Group BSS-CCA算法对研究人脑的功能磁共振成像数据具有较高的实用价值。
|
| 关 键 词： | 功能磁共振成像 盲源分离 典型相关分析 成组分析 |
Group Three-Dimensional Image Feature Extraction Methodof Multiple Spatial Gaussian Source Signals |
| |
| WU Xingjie,ZENG Lingli,LI Ming,SHEN Hui,WANG Xiaohong and HU Dewen.Group Three-Dimensional Image Feature Extraction Methodof Multiple Spatial Gaussian Source Signals[J].Journal of Naval Aeronautical Engineering Institute,2017,32(3):261-264, 283. |
| |
| Authors: | WU Xingjie ZENG Lingli LI Ming SHEN Hui WANG Xiaohong and HU Dewen |
| |
| Institution: | Department of Control Engineering, NAAU, Yantai Shandong 264001, China,College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China,College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China,College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China,Information Department, 107th Hospital of PLA, Yantai Shandong 264000, China and College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China |
| |
| 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. |
| |
| Keywords: | functional magnetic resonance imaging blind source separation canonical correlation analysis group analysis |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《海军航空工程学院学报》浏览原始摘要信息 |
| 点击此处可从《海军航空工程学院学报》下载免费的PDF全文 |
|
