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dc.contributor.authorDuann, Jeng-Renen_US
dc.contributor.authorJan, Chia-Ingen_US
dc.contributor.authorOu-Yang, Mangen_US
dc.contributor.authorLin, Chia-Yien_US
dc.contributor.authorMo, Jen-Fengen_US
dc.contributor.authorLin, Yung-Jiunen_US
dc.contributor.authorTsai, Ming-Hsuien_US
dc.contributor.authorChiou, Jin-Chernen_US
dc.date.accessioned2014-12-08T15:35:15Z-
dc.date.available2014-12-08T15:35:15Z-
dc.date.issued2013-12-01en_US
dc.identifier.issn1083-3668en_US
dc.identifier.urihttp://dx.doi.org/10.1117/1.JBO.18.12.126005en_US
dc.identifier.urihttp://hdl.handle.net/11536/23886-
dc.description.abstractRecently, hyperspectral imaging (HSI) systems, which can provide 100 or more wavelengths of emission autofluorescence measures, have been used to delineate more complete spectral patterns associated with certain molecules relevant to cancerization. Such a spectral fingerprint may reliably correspond to a certain type of molecule and thus can be treated as a biomarker for the presence of that molecule. However, the outcomes of HSI systems can be a complex mixture of characteristic spectra of a variety of molecules as well as optical interferences due to reflection, scattering, and refraction. As a result, the mixed nature of raw HSI data might obscure the extraction of consistent spectral fingerprints. Here we present the extraction of the characteristic spectra associated with keratinized tissues from the HSI data of tissue sections from 30 oral cancer patients (31 tissue samples in total), excited at two different wavelength ranges (330 to 385 and 470 to 490 nm), using independent and principal component analysis (ICA and PCA) methods. The results showed that for both excitation wavelength ranges, ICA was able to resolve much more reliable spectral fingerprints associated with the keratinized tissues for all the oral cancer tissue sections with significantly higher mean correlation coefficients as compared to PCA (p < 0.001). (C) The Authors.en_US
dc.language.isoen_USen_US
dc.subjectoral canceren_US
dc.subjecthyperspectrumen_US
dc.subjectautofluorescenceen_US
dc.subjectindependent component analysisen_US
dc.subjectprincipal component analysisen_US
dc.subjectkeratinizationen_US
dc.titleSeparating spectral mixtures in hyperspectral image data using independent component analysis: validation with oral cancer tissue sectionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1117/1.JBO.18.12.126005en_US
dc.identifier.journalJOURNAL OF BIOMEDICAL OPTICSen_US
dc.citation.volume18en_US
dc.citation.issue12en_US
dc.citation.epageen_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000331706500026-
dc.citation.woscount2-
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