DocumentCode
441783
Title
Functional MRI activation detection using genetic K-means clustering
Author
Shi, Lin ; Heng, Pheng Ann ; Wong, Tien-Tsin
Author_Institution
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China
Volume
3
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
1680
Abstract
We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.
Keywords
biomedical MRI; genetic algorithms; pattern clustering; K-means algorithm; cluster centroids; fMRI activation detection problem; functional magnetic resonance imaging; genetic K-means clustering; Clustering algorithms; Clustering methods; Data preprocessing; Genetics; Independent component analysis; Iterative algorithms; Magnetic resonance imaging; Partitioning algorithms; Principal component analysis; Signal to noise ratio; activation detection; fMRI; genetic K-means clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527215
Filename
1527215
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