Title :
Clustering Analysis for fMRI Dataset based on ISODATA Algorithm
Author :
Zheng, Xi ; Cao, Zhitong ; Shao, Bo ; Fang, Jiazhong ; He, Guoguang
Author_Institution :
Inst. of Appl. Phys., Zhejiang Univ., Hangzhou
Abstract :
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 find out the activated region in the brain. Therefore a multi-step strategy, including MFc and ISODATA, has been proposed to analyze a hybrid dataset and a real experimental fMRI dataset. On the whole, clustering analysis is calculated by multi-step strategy for local activity of fMRI 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
Keywords :
biomedical MRI; data analysis; medical computing; pattern clustering; ISODATA algorithm; clustering analysis; data-driving methods; fMRI dataset; iteration self-organization data analysis techniques algorithm; modified fuzzy c-means; Algorithm design and analysis; Clustering algorithms; Data analysis; Helium; Local activities; Magnetic analysis; Magnetic fields; Physics; Scanning probe microscopy; Statistical analysis;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614886
