Paper
30 April 2004 Novel approach to control false positive rate in fuzzy cluster analysis of fMRI
Hesamoddin Jahanian, Hamid Soltanian-Zadeh, Gholam-Ali Hossein-Zadeh
Author Affiliations +
Abstract
Fuzzy c-means (FCM) suffers from some limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results, when it is applied to the raw fMRI time series. Based on randomization, we developed a method to control the false positive detection rate in FCM and estimate the statistical significance of the results. Using this novel approach, we proposed an fMRI activation detection method which uses FCM with controlled false positive rate. The ability of the method in controlling the false positive rate is shown by an analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate allows comparison of different feature spaces and fuzzy clustering methods. A new feature space, in multi and scalar wavelet domain, is proposed for activation detection in fMRI to address the stability problem. Finally, using the proposed method for controlling the false positive rate, the proposed feature space is compared to the cross-correlation feature space.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hesamoddin Jahanian, Hamid Soltanian-Zadeh, and Gholam-Ali Hossein-Zadeh "Novel approach to control false positive rate in fuzzy cluster analysis of fMRI", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); https://doi.org/10.1117/12.535324
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Functional magnetic resonance imaging

Wavelets

Statistical analysis

Feature extraction

Fuzzy logic

Brain

Computer simulations

RELATED CONTENT

Leveraging sparsity to detect HRF variability in fMRI
Proceedings of SPIE (January 26 2017)
Multivariate indexing of multichannel images
Proceedings of SPIE (October 02 2007)
fMRI activation detection in wavelet signal subspace
Proceedings of SPIE (April 24 2002)
Estimation of a semiparametric model of fMRI data
Proceedings of SPIE (December 05 2001)

Back to Top