Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral
resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected
data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information.
In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images
obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes
advantage of spatial and spectral information simultaneously, and provides information about the average optical
properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn
injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics
informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries
in short post-injury time.
Lukasz A. Paluchowski, Asgeir Bjorgan, Håvard B. Nordgaard M.D., and Lise L. Randeberg, "Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images," Proc. SPIE 9689, Photonic Therapeutics and Diagnostics XII, 96890F (Presented at SPIE BiOS: February 14, 2016; Published: 29 February 2016); https://doi.org/10.1117/12.2212163.
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