Unifying Genetic Algorithm and Clustering Method for Recognizing Activated fMRI Time Series

  • Lin Shi
  • Pheng Ann Heng
  • Tien-Tsin Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


In order to get more reliable activation detection result in functional MRI data, we attempt to bring together the advantages of the genetic algorithm, which is deterministic and able to escape from the local optimal solution, and the K-means clustering, which is fast. Thus a novel clustering approach, namely the genetic K-means algorithm, is proposed to detect fMRI activation. It is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experimental results show that the proposed method recognizes fMRI activation regions with higher accuracy than ordinary K-means clustering.


fMRI Data Independent Component Analysis Cluster Centroid fMRI Time Series Empty Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lin Shi
    • 1
  • Pheng Ann Heng
    • 1
  • Tien-Tsin Wong
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, NT, Hong Kong

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