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Fusion of FMRI-sMRI-EEG by Ensemble Feature Selection Improves Classification of Schizophrenia
Sui Jing(隋婧); Hao He; Yuhui Du; Qingbao Yu; Jiayu Chen; Eduardo Castro; David A Bridwell; Godfrey D Pearlson; Vince D Calhoun
2014
Conference Name2014 The 20th annual confernece of Organization for Human Brain Mapping(OHBM 2014)
Conference Date2014/6/8-12
Conference PlaceHamburg, Germany
AbstractNonuniformity correction (NUC) is a critical task for achieving higher performances in modern infrared imaging systems. For cases where radiometry is not required, we proposed an extension to a recently reported scene-based NUC technique RASBA, the scene-based algorithm using perimeter diaphragm strips (SBA-PDS)*. This method initially guarantees all detectors along FPA perimeter have an uniform, but unknown bias through one-point calibration, which is dependent on the reciprocating movement of diaphragm strips. Then the SBA-PDS proceeds bias estimation recursively based on a special algebraic algorithm and can effectively "transport" the calibration of the perimeter detectors to those interior uncorrected ones. This approach provides the advantages of operating NUC with an almost unobstructive field of view, no need for cost of blackbody sources, and achieving acceptable results during the time of hundreds of frames, which is usually as long as thousands of frames by statistical algorithm. The technique was applied to real infrared data obtained from two kinds of uncooled infrared cameras and the experimental results appeared promising.
KeywordFmri-smri-eeg Feature Selection Improves Classification Of Schizophrenia
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20786
Collection脑网络组研究中心
AffiliationInstitute of Automation Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Sui Jing,Hao He,Yuhui Du,et al. Fusion of FMRI-sMRI-EEG by Ensemble Feature Selection Improves Classification of Schizophrenia[C],2014.
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