A split-merge-based region-growing method for fMRI activation detection

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.