Leveraging image processing techniques to visualize sub-cellular domains in optical photothermal infrared imaging

Area of Science:

• Chemical Imaging
• Spectroscopy
• Biomedical Optics

Background:

• Vibrational spectroscopic imaging traditionally captures full spectra per pixel (hyperspectral imaging).
• High-resolution methods like optical photothermal infrared (O-PTIR) spectroscopy often acquire data at specific wavenumbers for speed.
• Both methods require extensive pre-processing to mitigate noise, scattering, and artifacts, especially for sparse data.

Purpose of the Study:

• To develop a straightforward, step-by-step protocol for spatial processing and segmentation of sparse chemical imaging data.
• To address limitations of vendor-specific and non-standard processing pipelines in chemical imaging.
• To enable reliable feature extraction and classification from single-wavenumber images.

Main Methods:

• Development of a protocol for spatial pre-processing (masking, spatial filtering) and segmentation.
• Utilized standard open-source libraries for broad accessibility and compatibility.
• Demonstrated the protocol on single-wavenumber O-PTIR images of human cells.

Main Results:

• Successfully implemented a protocol for spatial processing and segmentation of sparse chemical imaging data.
• The open-source approach enhances data comparability and reduces maintenance issues.
• The protocol effectively prepares data for downstream analysis like feature extraction.

Conclusions:

• A standardized, open-source protocol for spatial processing of sparse chemical imaging data is presented.
• This approach facilitates reliable analysis of high-resolution vibrational spectroscopy data.
• The protocol is applicable to various chemical imaging modalities, including O-PTIR.