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作者:

Zhao, Xuewu (Zhao, Xuewu.) | Ji, Junzhong (Ji, Junzhong.) (学者:冀俊忠) | Yao, Yao (Yao, Yao.)

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摘要:

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. © 2017, Springer International Publishing AG.

关键词:

Clustering algorithms Magnetic resonance imaging Optimization Photomapping

作者机构:

  • [ 1 ] [Zhao, Xuewu]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhao, Xuewu]College of Software, Nanyang Normal University, Nanyang, China
  • [ 3 ] [Ji, Junzhong]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Yao, Yao]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, Faculty of Information Technology, Beijing University of Technology, Beijing, China

通讯作者信息:

  • 冀俊忠

    [ji, junzhong]beijing municipal key laboratory of multimedia and intelligent software, faculty of information technology, beijing university of technology, beijing, china

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来源 :

ISSN: 0302-9743

年份: 2017

卷: 10654 LNAI

页码: 72-82

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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