Clustering analysis for fMRI dataset based on ISODATA algorithm

被引：0
|
作者：
Zheng, X [1 ]
Cao, ZT [1 ]
Shao, B [1 ]
Fang, JZ [1 ]
He, GG [1 ]
机构：
[1] Zhejiang Univ, Inst Appl Phys, Hangzhou 310027, Peoples R China
来源：
PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the paper, the Modified Fuzzy c-means (MFc) is firstly used to treat the ill-balanced fMRI dataset to improve the efficiency, remove the redundance and reduce the population of analyzed voxels. Then the Iteration Self-Organization Data Analysis Techniques Algorithm (ISODATA) method, as the development of data-driving methods, is utilized to rind out the activated region in the brain. Therefore a multi-step strategy, including We and ISODATA, has been proposed to analyze a hybrid dataset and a real experimental UARI dataset. On the whole, clustering analysis is calculated by multi-step strategy for local activity of WRI dataset under auditory stimulation. Results show the multi-step strategy has its special characteristics in flexibility and efficiency compared with other data-driving dynamic method and SPM.
引用
收藏
页码：1373 / 1377
页数：5
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