Accurate diagnosis of burns, mainly in terms of depth and healing potential, has still remained an unsolved clinical problem. Hyperspectral imaging, with its unique capabilities to simultaneously provide both spatial and spectral information, can be considered as a particularly useful tool in early diagnosis of burns by providing accurate and valuable information about injured biological tissues. In this study, the potential of hyperspectral imaging to generate burn characteristics maps was evaluated. Two supervised classification methods (spectral angle mapper and support vector machine) of the hyperspectral data were investigated and their classification accuracy was compared. The study was performed on a 24 hours old burn of the hand (superficial-partial and deep-partial thickness burn wound). A pushbroom hyperspectral imaging system was used to acquire the hyperspectral image of the burn wound within the wavelength range from 400 nm to 800 nm. The hyperspectral image was calibrated with respect to the white and dark reference images in order to minimize the influences of light intensity variations across the spatial scanning lines and the dark current in the hyperspectral system. Minimum noise fraction transform was used to determine the inherent dimensionality of hyperspectral data and to separate the information from noise before the calibrated hyperspectral image being analyzed using the spectral angle mapper and support vector machine classifiers. The accuracy of these two classification methods in mapping the skin burn characteristics was evaluated based on the classification accuracy assessment of the resulted skin burns characteristic maps. The results revealed that the overall classification accuracy of support vector machine classifier exceeded (overall accuracy = 91.94 % and Kappa coefficient = 0.902) that of the spectral angle mapper classifier (overall accuracy = 84.13 % and Kappa coefficient = 0.808). In conclusion, these preliminary data suggest that hyperspectral imaging combined with support vector machine classifier could play an important role in burn characterization and mapping.

Date Published: 13 August 2018
PDF: 9 pages
Proc. SPIE 10677, Unconventional Optical Imaging, 106773P (13 August 2018); doi: 10.1117/12.2319267

Published in SPIE Proceedings Vol. 10677:
Unconventional Optical Imaging
Corinne Fournier; Marc P. Georges; Gabriel Popescu, Editor(s)