[1]HUANG Jiashuang,MEI Xue,YUAN Xiaolong,et al.FMRI feature extraction and identification of brain diseases based on the brain functional network[J].CAAI Transactions on Intelligent Systems,2015,10(2):248-254.[doi:10.3969/j.issn.1673-4785.201312043]
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FMRI feature extraction and identification of brain diseases based on the brain functional network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 248-254 Column: 学术论文—机器感知与模式识别 Public date: 2015-04-25
Title:
FMRI feature extraction and identification of brain diseases based on the brain functional network
Author(s):
HUANG Jiashuang;  MEI Xue;  YUAN Xiaolong;  LI Zhenhua
College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 211816, China
Keywords:
fMRI; schizophrenia; complex network theory; feature extraction; brain disease; machine recognition
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201312043
Abstract:
The machine recognition of brain diseases is a hotspot issue in the field of medical images. However, traditional fMRI image analysis only treats part of the brain region. Considering the overall characteristics of the brain network, the maximal overlap discrete wavelet transform is used to construct weighted and binary networks based on the rest-fMRI data. The complex networks theory is applied to the network structure analysis. Finally, the clustering coefficient of the network is extracted as the characteristic component of classification identification, which allowed the separation of schizophrenia patients from normal control subjects. This method is applied to the recognition of schizophrenia in this paper. The experimental results of recognition rate, sensitivity and specificity show that this method is able to improve the effect of recognition and has the universal adaptability, which can be extended to the recognition of other brain diseases.
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