東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Inverse model for the extraction of ...

Appiah, Benjamin.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inverse model for the extraction of biological parameters from ovarian tissue fluorescence spectra and multivariate classifiers for tissue diagnosis./
Author:	Appiah, Benjamin.
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 45-04, page: 2021.
Contained By:	Masters Abstracts International45-04.
Subject:	Engineering, Biomedical. -
Online resource:	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.
LDR:01730nmm 2200265 4500 001 1834284
005 20071119110255.5
008 130610s2007 eng d
035 $a (UMI)AAI1441796
035 $a AAI1441796
040 $a UMI $c UMI
100 1 $a Appiah, Benjamin. $3 1922945
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
690 $a 0541
690 $a 0760
710 2 0 $a Rice University. $3 960124
773 0 $t Masters Abstracts International $g 45-04.
790 1 0 $a Drezek, Rebekah, $e advisor
790 $a 0187
791 $a M.S.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441796