Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Shi, L., Heng, P.A., Wong, TT. (2006). Unifying Genetic Algorithm and Clustering Method for Recognizing Activated fMRI Time Series. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_25
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DOI: https://doi.org/10.1007/11739685_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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