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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