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

Volume

1

fYear

2009

fDate

Nov. 30 2009-Dec. 1 2009

Firstpage

63

Lastpage

67

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3888-4

Type

conf

DOI

10.1109/KAM.2009.148

Filename

5362211