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Proceedings Paper

Combined classifier for discriminating cancerous tissue from normal tissue using light-induced autofluorescence
Author(s): Wumei Lin; Xin Yuan; Po Wing Yuen; Jonathan Sham; William I. Wei; Yue Wen; Peng-Cheng Shi; Jianan Y. Qu
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Paper Abstract

We investigated a novel method combining principal component analysis (PCA) and supervised learning technique, support vector machine (SVM), for classifying carcinoma lesion from normal tissue with light-induced autofluorescence. The autofluorescence spectral signals were collected in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. With the combined PCA and SVM classifying algorithm, the achieved overall accuracy is over 97%, companied with 95% sensitivity and 99% specificity for discriminating carcinoma from normal tissue. In comparison with the previously developed algorithms based on PCA method, this new method outperforms threshold- and probability-based PCA algorithms in all instances. The experimental results indicate great promise for autofluorescence spectroscopy based detection of small carcinoma lesion in the nasopharynx and other tissues.

Paper Details

Date Published: 8 October 2003
PDF: 10 pages
Proc. SPIE 5141, Diagnostic Optical Spectroscopy in Biomedicine II, (8 October 2003); doi: 10.1117/12.500406
Show Author Affiliations
Wumei Lin, Hong Kong Univ. of Science and Technology (China)
Xin Yuan, Hong Kong Univ. of Science and Technology (China)
Po Wing Yuen, Queen Mary Hospital, Univ. of Hong Kong (China)
Jonathan Sham, Queen Mary Hospital, Univ. of Hong Kong (China)
William I. Wei, Queen Mary Hospital, Univ. of Hong Kong (China)
Yue Wen, Northern Jiaotong Univ. (China)
Peng-Cheng Shi, Hong Kong Univ. of Science and Technology (China)
Jianan Y. Qu, Hong Kong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 5141:
Diagnostic Optical Spectroscopy in Biomedicine II
Georges A. Wagnieres, Editor(s)

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