Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis

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dc.contributor.advisorDrezek, Rebekah A.
dc.creatorAppiah, Benjamin
dc.date.accessioned2009-06-03T21:11:42Z
dc.date.available2009-06-03T21:11:42Z
dc.date.issued2007
dc.description.abstractWe present an inverse model to decompose bulk fluorescence spectra and extract tissue biological parameters. By deconvolving the effects of absorption and scattering from measured spectra, we are able to extract the intrinsic contributions from cellular and stromal fluorophores. Ovarian fluorescence, acquired ex-vivo immediately upon removal from patients, are analyzed using this model. We test the validity of the inverse model, and show that it has the ability to improve our understanding of the biological changes that cause the observed differences in the fluorescence spectra. The outputs of the model are used to develop classifiers for tissue diagnosis. Classifiers based on PLS selected features are also developed. An accuracy of more than 93% for discrimination between normal and cancerous ovarian tissues is achieved.
dc.format.extent63 p.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.callnoTHESIS E.E. 2007 APPIAH
dc.identifier.citationAppiah, Benjamin. "Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis." (2007) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/20485">https://hdl.handle.net/1911/20485</a>.
dc.identifier.urihttps://hdl.handle.net/1911/20485
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectBiomedical engineering
dc.subjectBiophysics
dc.titleInverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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