Support vector clustering for brain activation detection

  • Authors:

  • Defeng Wang;Lin Shi;Daniel S. Yeung;Pheng-Ann Heng;Tien-Tsin Wong;Eric C. C. Tsang

  • Affiliations:

  • Department of computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;Department of computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;Department of computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

  • Venue:
  • MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
  • Year:
  • 2005

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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.