ROC-based determination of the number of clusters for fMRI activation detection

Hesamoddin Jahanian; Hamid Soltanian-Zadeh; Gholam Ali Hossein-Zadeh; Mohammad-Reza Siadat

doi:10.1117/12.535691

12 May 2004 ROC-based determination of the number of clusters for fMRI activation detection

Hesamoddin Jahanian, Hamid Soltanian-Zadeh, Gholam Ali Hossein-Zadeh, Mohammad-Reza Siadat

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535691
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Fuzzy C-means (FCM), in spite of its potent advantages in exploratory analyze of functional magnetic resonance imaging (fMRI), suffers from limitations such as a priori determination of number of clusters, unknown statistical significance for the results, and instability of the results when it is applied on raw fMRI time series. Choosing different number of clusters, or thresholding the membership degree at different levels, lead to considerably different activation maps. However, research work for finding a standard index to determine the number of clusters has not yet succeeded. Using randomization, we developed a method to control false positive rate in FCM, which gives a meaningful statistical significance to the results. Making use of this novel method and an ROC-based cluster validity measure, we determined the optimal number of clusters. In this study, we applied the FCM on a feature space that takes the variability of hemodynamic response function into account (HRF-based feature space). The proposed method found the accurate number of clusters in simulated fMRI data. In addition, the proposed method generated excellent results for experimental fMRI data and showed a good reproducibility for determining the number of clusters.

Citation Download Citation

Hesamoddin Jahanian, Hamid Soltanian-Zadeh, Gholam Ali Hossein-Zadeh, and Mohammad-Reza Siadat "ROC-based determination of the number of clusters for fMRI activation detection", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535691

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