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Inverse model for the extraction of ...

Appiah, Benjamin.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis./
作者:	Appiah, Benjamin.
面頁冊數:	63 p.
附註:	Source: Masters Abstracts International, Volume: 45-04, page: 2021.
Contained By:	Masters Abstracts International45-04.
標題:	Engineering, Biomedical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441796

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

Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis. - 63 p.

Source: Masters Abstracts International, Volume: 45-04, page: 2021.

Thesis (M.S.)--Rice University, 2007.

We 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.Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis.
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245 1 0 $a Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis.
300 $a 63 p.
500 $a Source: Masters Abstracts International, Volume: 45-04, page: 2021.
500 $a Adviser: Rebekah Drezek.
502 $a Thesis (M.S.)--Rice University, 2007.
520 $a We 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.
590 $a School code: 0187.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Biophysics, Medical. $3 1017681
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773 0 $t Masters Abstracts International $g 45-04.
790 1 0 $a Drezek, Rebekah, $e advisor
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