Adaptive spectral window sizes for extraction of diagnostic features from optical spectra.
Adaptive spectral window sizes for extraction of diagnostic features from optical spectra.
复制标题
用于从光谱中提取诊断特征的自适应光谱窗口大小。
DOI:
10.1117/1.3481143
复制
发表时间:
2010
影响因子:
3.5
通讯作者:
Markey,MiaK
中科院分区:
文献类型:
--
作者:
Kan,Chih-wen;Lee,AndyY;Nieman,LindaT;Sokolov,Konstantin;Markey,MiaK
We present an approach to adaptively adjust the spectral window sizes for optical spectra feature extraction. Previous studies extracted features from spectral windows of a fixed width. In our algorithm, piecewise linear regression is used to adaptively adjust the window sizes to find the maximum window size with reasonable linear fit with the spectrum. This adaptive windowing technique ensures the signal linearity in defined windows; hence, the adaptive windowing technique retains more diagnostic information while using fewer windows. This method was tested on a data set of diffuse reflectance spectra of oral mucosa lesions. Eight features were extracted from each window. We performed classifications using linear discriminant analysis with cross-validation. Using windowing techniques results in better classification performance than not using windowing. The area under the receiver-operating-characteristics curve for windowing techniques was greater than a nonwindowing technique for both normal versus mild dysplasia (MD) plus severe high-grade dysplasia or carcinama (SD)and benign versus. Although adaptive and fixed-size windowing perform similarly, adaptive windowing utilizes significantly fewer windows than fixed-size windows (number of windows per spectrum: 8 versus 16). Because adaptive windows retain most diagnostic information while reducing the number of windows needed for feature extraction, our results suggest that it isolates unique diagnostic features in optical spectra.
登录
查看更多内容
DOI:
10.1002/hed.10381
发表时间:
2004-03-01
影响因子:
2.9
作者:
Svistun, E;Alizadeh-Naderi, R;Richards-Kortum, R
通讯作者:
Richards-Kortum, R
影响因子:
3.5
作者:
Xueding Wang;Lihong V. Wang
通讯作者:
Xueding Wang;Lihong V. Wang
影响因子:
254.7
作者:
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者:
Thun, Michael J.
影响因子:
3.5
作者:
Inseok Seo;J. You;C. Hayakawa;V. Venugopalan
通讯作者:
Inseok Seo;J. You;C. Hayakawa;V. Venugopalan
影响因子:
29.4
作者:
Georgakoudi, I;Jacobson, BC;Feld, MS
通讯作者:
Feld, MS