Image processing as basis for chemometrics in photothermal atomic force microscopy infrared imaging

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.

• Publication year

  2023

• Venue

  BiOS

• Publication date

  2023-03-15

• Fields of study

  Materials Science, Chemistry, Engineering, Computer Science

• Identifiers
  DOI 10.1117/12.2651424
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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