Share Email Print

Proceedings Paper

Hyperspectral fluorescence image analysis for use in medical diagnostics
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents hyperspectral fluorescence imaging and a support vector machine for detecting skin tumors. Skin cancers may not be visually obvious since the visual signature appears as shape distortion rather than discoloration. As a definitive test for cancer diagnosis, skin biopsy requires both trained professionals and significant waiting time. Hyperspectral fluorescence imaging offers an instant, non-invasive diagnostic procedure based on the analysis of the spectral signatures of skin tissue. A hyperspectral image contains spatial information measured at a sequence of individual wavelength across a sufficiently broad spectral band at high-resolution spectrum. Fluorescence is a phenomenon where light is absorbed at a given wavelength and then is normally followed by the emission of light at a longer wavelength. Fluorescence generated by the skin tissue is collected and analyzed to determine whether cancer exists. Oak Ridge National Laboratory developed an endoscopic hyperspectral imaging system capable of fluorescence imaging for skin cancer detection. This hyperspectral imaging system captures hyperspectral images of 21 spectral bands of wavelength ranging from 440 nm to 640 nm. Each band image is spatially co-registered to eliminate the spectral offset caused during the image capture procedure. Image smoothing by means of a local spatial filter with Gaussian kernel increases the classification accuracy and reduces false positives. Experiments show that the SVM classification with spatial filtering achieves high skin tumor detection accuracies.

Paper Details

Date Published: 1 April 2005
PDF: 8 pages
Proc. SPIE 5692, Advanced Biomedical and Clinical Diagnostic Systems III, (1 April 2005); doi: 10.1117/12.596463
Show Author Affiliations
Seong Gon Kong, Univ. of Tennessee/Knoxville (United States)
Zheng Du, Univ. of Tennessee/Knoxville (United States)
Matthew Martin, Oak Ridge National Lab. (United States)
Tuan Vo-Dinh, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 5692:
Advanced Biomedical and Clinical Diagnostic Systems III
Tuan Vo-Dinh; Warren S. Grundfest; David A. Benaron; Gerald E. Cohn, Editor(s)

© SPIE. Terms of Use
Back to Top