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Investigation of cell structure is hardly imaginable without bright-fieldmicroscopy. Numerous modifications such as depth-wise scanning orvideoenhancement make this method being state-of-the-art. This raises aquestion what maximal information can be extracted from ordinary (but wellacquired) bright-field images in a model-free way. Here we introduce a methodof a physically correct extraction of features for each pixel when thesefeatures resemble a transparency spectrum. The method is compatible withexistent ordinary bright-field microscopes and requires mathematicallysophisticated data processing. Unsupervised clustering of the spectra yieldsreasonable semantic segmentation of unstained living cells without any a prioriinformation about their structures. Despite the lack of reference data (toprove 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.

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