Title :
Hierarchical Fast Clustering Method for fMRI Feature Reconstruction
Author :
Li, Xiaomin ; Lin, Wei ; Huang, Shuanghua
Author_Institution :
Coll. of Electron. Eng., Naval Univ. of Eng., Wuhan, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
In order to solve the feature reconstruction problem of fMRI time series, hierarchical fast clustering method (HFCM) is proposed. The reconstruction of features can be thought as finding the task-related region of interest (ROI) in the human brain fMRI in order to eliminate information redundary. HFCM takes advantage of optimizing the hierarchical structure and tuning weights of different kind of distances. Comparing with the existing reconstruction methods, e.g. K-means and t-test, HFCM saves more than 62% running time, on condition of ensuring the precision of task-related estimating.
Keywords :
biomedical MRI; brain; image reconstruction; medical image processing; optimisation; time series; K-means; brain fMRI; feature reconstruction; hierarchical fast clustering method; optimization; t-test; task-related region of interest; time series; Clustering algorithms; Clustering methods; Computer science; Cost function; Educational institutions; Humans; Knowledge acquisition; Knowledge engineering; Signal to noise ratio; Testing; Feature Reconstruction; Hierarchical Clustering; K-means; ROI; fMRI;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.148
