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Mid-IR imaging combined with machine learning is a powerful combination for non-destructive, label free chemical imaging. Key applications include computational staining and tissue classification. These applications are enabled by information rich mid-IR hyperspectral images and reliable ground truth data. As novel, nano-scale spatial resolution mid-IR spectroscopy techniques are finding broader use we realize that ground truth datasets will be needed at the nano-scale as well. Here, we propose image fusion and registration of nano-scale images as a generic approach for establishing such datasets. We demonstrate the viability of this approach for imaging the sub-cellular distribution of proteins and specific enzymes. Furthermore, we demonstrate that image registration of AFM-IR spectral data is a key step in processing AFM-IR chemical imaging data in general.
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Georg Ramer, A. Catarina V. D. dos Santos, Yide Zhang, Ufuk Yilmaz, Bernhard Lendl, "Image processing as basis for chemometrics in photothermal atomic force microscopy infrared imaging," Proc. SPIE 12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, 1239209 (17 March 2023); https://doi.org/10.1117/12.2651424