Comparison of pre-processing techniques to reduce non-tissue related variations in hyperspectral reflectance imaging

Mark Witteveen; Ton A.G.J. M. Leeuwen; Maurice Aalders; Henricus Sterenborg; Theo Ruers; Anouk L. Post

doi:10.1117/12.2647777

17 March 2023 Comparison of pre-processing techniques to reduce non-tissue related variations in hyperspectral reflectance imaging

Mark Witteveen, Ton A.G.J. M. Leeuwen, Maurice Aalders, Henricus Sterenborg, Theo Ruers, Anouk L. Post

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Proceedings Volume PC12363, Multiscale Imaging and Spectroscopy IV; PC123630C (2023) https://doi.org/10.1117/12.2647777
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Hyperspectral reflectance imaging can be used to develop tissue classification algorithms, which is often based on machine learning. To improve the performance of classification algorithms, pre-processing is often used to remove variations in data not related to the tissue itself. In hyperspectral imaging, these variations are the result of reflections from the tissue surface (glare) and height variations within and between tissue samples. We investigated and quantified the performance of 8 commonly used pre-processing algorithms to reduce differences in spectra due to glare and height differences, while retaining contrast between tissues with different optical properties on simulated and clinical datasets.

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Mark Witteveen, Ton A.G.J. M. Leeuwen, Maurice Aalders, Henricus Sterenborg, Theo Ruers, and Anouk L. Post "Comparison of pre-processing techniques to reduce non-tissue related variations in hyperspectral reflectance imaging", Proc. SPIE PC12363, Multiscale Imaging and Spectroscopy IV, PC123630C (17 March 2023); https://doi.org/10.1117/12.2647777

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KEYWORDS

Tissues

Algorithm development

Hyperspectral imaging

Reflectivity

Machine learning

Medicine

Natural surfaces

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