# Quasi-spectral characterization of intracellular regions in bright-field light microscopy images

## TL;DR

This paper introduces a model-free, physically accurate method for extracting spectral features from bright-field microscopy images, enabling semantic segmentation and potential intracellular chemical analysis without prior structural information.

## Contribution

It presents a novel data processing technique that extracts transparency-like spectra from standard bright-field images, facilitating label-free cell segmentation and analysis.

## Key findings

- Unsupervised clustering yields meaningful cell segmentation.
- Method is compatible with existing microscopes.
- Potential for intracellular chemical analysis.

## Abstract

Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis.