Clustering-based framework for comparing fMRI analysis methods

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 metho...

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Bibliographic Details
Published in2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821) pp. 1008 - 1011 Vol. 1
Main Authors Hossein-Zadeh, G.-A., Golestani, A.-M., Soltanian-Zadeh, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Accuracy
Algorithm design and analysis
Clustering algorithms
Clustering methods
Image analysis
Magnetic analysis
Magnetic resonance
Radiology
Reproducibility of results
Testing
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Summary: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.
ISBN:0780383885
9780780383883
DOI:10.1109/ISBI.2004.1398711