Ellipsoidal support vector clustering for functional MRI analysis |
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Authors: | Defeng Wang [Author Vitae] Lin Shi Eric C.C. Tsang |
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Affiliation: | a Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong c Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong |
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Abstract: | As an exploratory approach, the clustering of fMRI time series has proved its effectiveness in analyzing the functional MRI, especially in the detection of activated regions. Due to the arbitrary distribution of fMRI time series in the temporal domain, imposing simple assumption on the data structure usually could be misleading and limit the detector's performance. Therefore, a true data-driven clustering algorithm that adapts to the data structure is preferred, and only high-level control over the clustering procedure is desired. Support vector clustering (SVC) is a suitable one in some extent because of its advantages, such as no cluster shape restriction, no need to explicitly specify the number of clusters, and the mechanism in outlier elimination. In this work, we propose an extension of the SVC to step further toward a data-sensitive detector. This approach is named as ellipsoidal support vector clustering (ESVC). To be robust to noise, the clustering is performed on features extracted from the fMRI time series via Fourier transform. Experimental results on simulated and real data sets demonstrate the effectiveness of incorporating data structure in clustering fMRI time series. |
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Keywords: | fMRI Activation detection Support vector clustering Ellipsoidal support vector clustering |
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