Abstract
The paper presents a novel artificial bee colony clustering (ABCC) algorithm with a self-adaptive multidimensional search mechanism based on difference bias for insula functional parcellation, called as DABCC. In the new algorithm, the preprocessed functional magnetic resonance imaging (fMRI) data was mapped into a low-dimension space by spectral mapping to reduce its dimension in the initialization. Then, clustering centers in the space were searched by the search procedure composed of employed bee search, onlooker bee search and scout bee search, where a self-adaptive multidimensional search mechanism based on difference bias for employed bee search was developed to improve search capability of ABCC. Finally, the experiments on fMRI data demonstrate that DABCC not only has stronger search ability, but can produce better parcellation structures in terms of functional consistency and regional continuity.
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Acknowledgments
The work is partly supported by the NSFC Research Program (61375059, 61672065), the National “973” Key Basic Research Program of China (2014CB744601), Nanyang Normal University - level Young Teacher Project (QN2017040), the scientific and technological project in Henan Province of China (142102210588, 172102310702), and the Science and Technology Foundation of Henan Educational Committee of China (17A520049, 17A630046).
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Zhao, X., Ji, J., Yao, Y. (2017). Insula Functional Parcellation from FMRI Data via Improved Artificial Bee-Colony Clustering. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_7
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DOI: https://doi.org/10.1007/978-3-319-70772-3_7
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