NDLI: Real-time raman spectroscopy for non-invasive skin cancer detection

Content Provider	IEEE Xplore Digital Library
Author	Zhao, Jianhua Lui, Harvey McLean, David I. Zeng, Haishan
Copyright Year	2008
Description	Author affiliation: Laboratory for Advanced Medical Photonics (LAMP), Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia & Vancouver Coastal Health Research Institute, Canada (Zhao, Jianhua; Lui, Harvey; McLean, David I.; Zeng, Haishan)
Abstract	Raman spectroscopy is a non-invasive optical technique, which can assess molecular structures and conformations within biological tissue. The probability of Raman scattering is inherently low such that previous clinical applications of Raman spectroscopy have been limited by long data acquisition times. We have developed a rapid real-time Raman spectrometer system with measurement times of less than 1 second, paving the way for clinical application. In this presentation, we report preliminary clinical results for this real-time Raman system. To date 289 skin cancers and benign skin lesions have been measured. Using partial least squares regression and linear discriminant analysis to analyze the Raman spectra we found that skin cancers could be well differentiated from benign skin lesions (sensitivity 91% and specificity 75%) and malignant melanoma from benign pigmented lesions (sensitivity 97%, specificity 78%).
Starting Page	3107
Ending Page	3109
File Size	246064
Page Count	3
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	partial least square Raman spectroscopy skin cancers skin diseases skin cancer detection
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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