1. Support vector clustering for brain activation detection.
- Author
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Wang D, Shi L, Yeung DS, Heng PA, Wong TT, and Tsang EC
- Subjects
- Brain anatomy & histology, Humans, Image Enhancement methods, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Artificial Intelligence, Brain physiology, Brain Mapping methods, Evoked Potentials physiology, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging methods, Pattern Recognition, Automated methods
- Abstract
In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of fMRI time series and cluster these features by SVC. In SVC, these features are mapped from their original feature space to a very high dimensional kernel space. By finding a compact sphere that encloses the mapped features in the kernel space, one achieves a set of cluster boundaries in the feature space. The SVC is an effective and robust fMRI activation detection method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality detection results without explicitly specifying the number of clusters, (3) the stronger robustness due to the mechanism in outlier elimination. Experimental results on simulated and real fMRI data demonstrate the effectiveness of SVC.
- Published
- 2005
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