Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images

Lukasz A. Paluchowski; Asgeir Bjorgan; Håvard B. Nordgaard M.D.; Lise L. Randeberg

doi:10.1117/12.2212163

29 February 2016 Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images

Lukasz A. Paluchowski, Asgeir Bjorgan, Håvard B. Nordgaard M.D., Lise L. Randeberg

Author Affiliations +

Proceedings Volume 9689, Photonic Therapeutics and Diagnostics XII; 96890F (2016) https://doi.org/10.1117/12.2212163
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (29 February 2016); https://doi.org/10.1117/12.2212163

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