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Hyperspectral Imaging and Classification for Grading Skin Erythema
Author(s) -
Ramy Abdlaty,
Lilian Doerwald-Munoz,
Ali Madooei,
Samir Sahli,
ShuChi A. Yeh,
Josiane Zerubia,
Raimond Wong,
Joseph E. Hayward,
Thomas J. Farrell,
Qiyin Fang
Publication year - 2018
Publication title -
frontiers in physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.754
H-Index - 31
ISSN - 2296-424X
DOI - 10.3389/fphy.2018.00072
Subject(s) - erythema , grading (engineering) , hyperspectral imaging , dermatology , linear discriminant analysis , medicine , rgb color model , artificial intelligence , computer science , civil engineering , engineering
Erythema is an inflammatory condition of the skin that is commonly used as a feature to monitor the progression of cutaneous diseases or treatment induced side effects. In radiation therapy, skin erythema is routinely assessed visually by an expert using standardized grading criteria. However, visual assessment (VA) is subjective and commonly used grading tools are too coarse to score the onset of erythema. Therefore, an objective method capable of quantitatively grading early erythema changes may help identify patients at higher risk for developing severe radiation induced skin toxicities. The purpose of this study is to investigate the feasibility of using hyperspectral imaging (HSI) for quantitative assessment of early erythema and to characterize its performance against VA documented on conventional digital photographic red-green-blue (RGB) images. Erythema was induced artificially on 3 volunteers in a controlled pilot study; and was subsequently measured using HSI, color imaging, and reflectance spectroscopy. HSI and color imaging data was analyzed using linear discriminant analysis (LDA) to perform classification. The classification results, including accuracy and precision, demonstrated that HSI is superior to color imaging in skin erythema assessment.

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