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Physics-informed and learning-based approaches to biomedical hyperspectral data analysis

Bjørgan, Asgeir
Doctoral thesis
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URI
https://hdl.handle.net/11250/2754051
Date
2021
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  • Institutt for elektroniske systemer [1831]
Abstract
The high spectral and spatial resolution of hyperspectral imaging makes it a promising imaging technique for a wide range of biomedical applications. A recurring challenge is the handling and processing of the large amounts of data generated by the technique. The work of this thesis has focused on the analysis of an in vitro wound model dataset and a burn wound model dataset, utilizing supervised and unsupervised learning techniques and photon and heat transport modeling to extract information from the data. New insights on the characterizable optical property changes of these applications has been obtained, along with their relation to the tissue composition and underlying mechanisms. This enables development of targeted automated processing algorithms and better understanding of the technique.
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Paper 1: Bjorgan, Asgeir; Pukstad, Brita Solveig; Randeberg, Lise Lyngsnes. Hyperspectral characterization of re‐epithelialization in an in vitro wound model. Journal of Biophotonics 2020 ;Volum 13.(10) https://doi.org/10.1002/jbio.202000108 This is an open access article under the terms of the Creative Commons Attribution License (CC BY 4.0)

Paper 2: Bjorgan, Asgeir; Randeberg, Lise Lyngsnes. Exploiting scale-invariance: a top layer targeted inverse model for hyperspectral images of wounds. Biomedical Optics Express 2020 ;Volum 11.(9) s. 5070-5091 https://doi.org/10.1364/BOE.399636 © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Paper 3: Bjorgan, A.; Pukstad, B.S.; Randeberg, L.L. Identification of wound healing in an in vitro wound model

Paper 4: Bjorgan, Asgeir; Randeberg, Lise Lyngsnes. Application of smoothing splines for spectroscopic analysis in hyperspectral images. Proceedings of SPIE, the International Society for Optical Engineering 2019 ;Volum 10873. https://doi.org/10.1117/12.2506618

Paper 5: Bjorgan, Asgeir; Randeberg, Lise Lyngsnes. A random forest-based method for selection of regions of interest in hyperspectral images of ex vivo human skin. Proceedings of SPIE, the International Society for Optical Engineering 2019 ;Volum 10889. https://doi.org/10.1117/12.2506620

Paper 6: Bjorgan, Asgeir; Randeberg, Lise Lyngsnes. Combining hyperspectral classification and heat transport modeling: An investigation of experimental burn wound heterogeneity
Publisher
NTNU
Series
Doctoral theses at NTNU;2021:157

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