Open AccessA split–merge‐based region‐growing method for fMRI activation detection
Author(s) -
Lu Yingli,
Jiang Tianzi,
Zang Yufeng
Publication year - 2004
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20034
Subject(s) - merge (version control) , cluster analysis , computer science , fuzzy logic , pattern recognition (psychology) , functional magnetic resonance imaging , artificial intelligence , computation , prior probability , prior information , data mining , algorithm , neuroscience , biology , bayesian probability , information retrieval
Abstract We introduce a hybrid method for functional magnetic resonance imaging (fMRI) activation detection based on the well‐developed split–merge and region‐growing techniques. The proposed method includes conjoining both of the spatio‐temporal priors inherent in split–merge and the prior information afforded by the hypothesis‐led component of region selection. Compared to the fuzzy c‐means clustering analysis, this method avoids making assumptions about the number of clusters and the computation complexity is reduced markedly. We evaluated the effectiveness of the proposed method in comparison with the general linear model and the fuzzy c‐means clustering method conducted on simulated and in vivo datasets. Experimental results show that our method successfully detected expected activated regions and has advantages over the other two methods. Hum. Brain Mapping 22:271–279, 2004. © 2004 Wiley‐Liss, Inc.