Paper Info
Title
Clustering-Based Framework For Comparing Fmri Analysis Methods
Abstract
In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering, method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.
Year
DOI
Venue
2004
10.1109/ISBI.2004.1398711
2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 AND 2
Keywords
Field
DocType
algorithm design and analysis,clustering,image analysis,receiver operating characteristics,comparative analysis,clustering algorithms,magnetic resonance,accuracy,radiology,receiver operator characteristic,principal component analysis,testing,feature space
Data mining,Feature vector,Receiver operating characteristic,Algorithm design,Pattern recognition,Computer science,Magnetic analysis,Functional magnetic resonance images,Artificial intelligence,Cluster analysis,Principal component analysis
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Gholam-Ali Hossein-Zadeh1143.09
A. M. Golestani200.34
Hamid Soltanian-Zadeh324422.92