File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Light induced autofluorescence for detection of nasopharyngeal carcinoma in vivo

TitleLight induced autofluorescence for detection of nasopharyngeal carcinoma in vivo
Authors
KeywordsEndoscopy
In Vivo Diagnosis
Light Induced Fluorescence (Lif)
Nasopharyngeal Carcinoma (Npc)
Principal Component Analysis (Pca)
Issue Date2001
PublisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Diagnostic Optical Spectroscopy in Biomedicine Conference, Munich, Germany, 19-21 June 2001. In Proceedings of SPIE - The International Society For Optical Engineering, 2001, v. 4432, p. 186-195 How to Cite?
AbstractTo improve the accuracy of conventional white light endoscopy in detecting the small lesion and identifying the margin of observable tumors, in vivo, the potential of light-induced fluorescence (LIF) spectroscopic imaging, using a general multivariate spectral classification algorithm, was evaluated. A conventional endoscopic system with a multiple channel spectrometer was used to measure the autofluorescence of nasopharyngeal tissue in vivo. Classification was based on the spectral difference between the carcinoma and normal tissue. A sophisticated algorithm based on Principal Component Analysis (PCA) was developed to differentiate between the nasopharyngeal carcinoma (NPC) from the normal tissue. Firstly, preprocessing was done to reduce noise and to calibrate the different measurement distances and geometry. Secondly, processing by PCA was done to effectively reduce the variable dimensions while maintaining useful information for analysis. Thirdly, various post-processing techniques were investigated and the classification performance was compared. Algorithms based on ratio of autofluorescence at two-wavelength and three-wavelength bands were used for comparison. The PCA based method shows a significant improvement over the two-wavelength and three-wavelength algorithm. Based on the entire spectra, the sensitivity of 92% and specificity of 96% were achieved using the PCA based algorithm for the detection of nasopharyngeal carcinomas. In conclusion, the PCA based statistical algorithm is efficient to achieve high spectral classification performance of NPC.
Persistent Identifierhttp://hdl.handle.net/10722/172797
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Hen_US
dc.contributor.authorWen, Yen_US
dc.contributor.authorLee, SLen_US
dc.contributor.authorYuen, Pen_US
dc.contributor.authorWei, WIen_US
dc.contributor.authorSham, Jen_US
dc.contributor.authorQu, JYen_US
dc.date.accessioned2012-10-30T06:24:59Z-
dc.date.available2012-10-30T06:24:59Z-
dc.date.issued2001en_US
dc.identifier.citationDiagnostic Optical Spectroscopy in Biomedicine Conference, Munich, Germany, 19-21 June 2001. In Proceedings of SPIE - The International Society For Optical Engineering, 2001, v. 4432, p. 186-195en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/172797-
dc.description.abstractTo improve the accuracy of conventional white light endoscopy in detecting the small lesion and identifying the margin of observable tumors, in vivo, the potential of light-induced fluorescence (LIF) spectroscopic imaging, using a general multivariate spectral classification algorithm, was evaluated. A conventional endoscopic system with a multiple channel spectrometer was used to measure the autofluorescence of nasopharyngeal tissue in vivo. Classification was based on the spectral difference between the carcinoma and normal tissue. A sophisticated algorithm based on Principal Component Analysis (PCA) was developed to differentiate between the nasopharyngeal carcinoma (NPC) from the normal tissue. Firstly, preprocessing was done to reduce noise and to calibrate the different measurement distances and geometry. Secondly, processing by PCA was done to effectively reduce the variable dimensions while maintaining useful information for analysis. Thirdly, various post-processing techniques were investigated and the classification performance was compared. Algorithms based on ratio of autofluorescence at two-wavelength and three-wavelength bands were used for comparison. The PCA based method shows a significant improvement over the two-wavelength and three-wavelength algorithm. Based on the entire spectra, the sensitivity of 92% and specificity of 96% were achieved using the PCA based algorithm for the detection of nasopharyngeal carcinomas. In conclusion, the PCA based statistical algorithm is efficient to achieve high spectral classification performance of NPC.en_US
dc.languageengen_US
dc.publisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_US
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.subjectEndoscopyen_US
dc.subjectIn Vivo Diagnosisen_US
dc.subjectLight Induced Fluorescence (Lif)en_US
dc.subjectNasopharyngeal Carcinoma (Npc)en_US
dc.subjectPrincipal Component Analysis (Pca)en_US
dc.titleLight induced autofluorescence for detection of nasopharyngeal carcinoma in vivoen_US
dc.typeConference_Paperen_US
dc.identifier.emailWei, WI: hrmswwi@hku.hken_US
dc.identifier.authorityWei, WI=rp00323en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1117/12.447134en_US
dc.identifier.scopuseid_2-s2.0-0035759377en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035759377&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume4432en_US
dc.identifier.spage186en_US
dc.identifier.epage195en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChang, H=7407523707en_US
dc.identifier.scopusauthoridWen, Y=55239414700en_US
dc.identifier.scopusauthoridLee, SL=8369148500en_US
dc.identifier.scopusauthoridYuen, P=7103124007en_US
dc.identifier.scopusauthoridWei, WI=7403321552en_US
dc.identifier.scopusauthoridSham, J=24472255400en_US
dc.identifier.scopusauthoridQu, JY=7201534954en_US
dc.customcontrol.immutablesml 160322 - amend-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats