Journal
NEUROIMAGE
Volume 20, Issue 1, Pages 455-465Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/S1053-8119(03)00352-5
Keywords
-
Ask authors/readers for more resources
Existing analytical techniques for functional magnetic resonance imaging (fMRI) data always need some specific assumptions on the time series. In this article, we present a new approach for fMRI activation detection, which can be implemented without any assumptions on the time series. Our method is based on a region growing method, which is very popular for image segmentation. A comparison of performance on fMRI activation detection is made between the proposed method and the deconvolution method and the fuzzy clustering method with receiver operating characteristic (ROC) methodology. In addition, we examine the effectiveness and usefulness of our method on real experimental data. Experimental results show that our method outperforms over the deconvolution method and the fuzzy clustering method on a number of aspects. These results suggest that our region growing method can serve as a reliable analysis of fMRI data. (C) 2003 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available