Two-point Stokes vector diagnostic approach for characterization of optically anisotropic biological tissues

Motahareh Peyvasteh, Alexander Dubolazov, Alexey Popov*, Alexander Ushenko, Yuriy A Ushenko, Igor Meglinski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The purpose of the study is to demonstrate a new method of Stokes-correlometric evaluation of polarization-inhomogeneous images of optically thin (optical thickness smaller than 0.01) histological sections from optically anisotropic biological tissues of different morphological structure. This method is based on a correlation ('two-point') generalization of traditional optical methods for analyzing 'one-point' distributions of polarization states of microscopic images of biological tissues. Analytical algorithms are obtained for describing the 'two-point' complex parameters of the Stokes vector image of a birefringent biological tissue. An experimental technique has been developed for measuring polarization-correlation maps, i.e. the coordinate distributions of the magnitude and phase of the 'two-point' Stokes vector parameters. Within the framework of the statistical and correlation analysis of the obtained data, new quantitative criteria for the differentiation of the optical properties of biological tissues of various morphological structures are found. A comparative analysis of the distribution of the 'single-point' and 'two-point' parameters of the Stokes vector of polarizationally inhomogeneous images was performed. It revealed a higher sensitivity (2-5 times) of the Stokes-correlometry method to variations in orientation-phase structure of biological tissues compared to the single-point approach.

Original languageEnglish
Article number395401
JournalJournal of Physics D: Applied Physics
Issue number39
Early online date21 May 2020
Publication statusPublished - 6 Jul 2020

Bibliographical note

Original content from this work may be used under the terms
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Funding: This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie
Sklodowska-Curie (grant agreement No 713606, MP); Academy of Finland (grants 326204 and 325097, IM and 314369,
AP), INFOTECH grant project (IM), MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005, IM), and National
Research Tomsk State University Academic D.I. Mendeleev Fund Program (IM).


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