Probing layered structures by multi-color backscattering polarimetry and machine learning

Abstract

Polarization imaging can quantitatively probe the characteristic microstructural features of biological tissues non-invasively. In biomedical tissues, layered structures are common. Superposition of two simple layers can result in a complex Mueller matrix, and multi-color backscattering polarimetry can help to probe layered structures. In this work, multi-color backscattering Mueller matrix images are measured for living nude mice skins. Preliminary analysis of anisotropy parameter A and linear polarizance parameter b show signs of a layered structure in the skin. For more detailed examinations on polarization features of layered samples, we generate Mueller matrices by experimenting with two-layered thick tissues and concentrically aligned silk submerged in milk. Then we use supervised machine learning to identify polarization parameters that are sensitive to layered structure and guide the synthesis of more parameters. Monte Carlo simulation is also adopted to explore the relationship between parameters and microstructures of media. We conclude that multi-color backscattering polarimetry combined with supervised machine learning can be applied to probe the characteristic microstructure in layered living tissue samples.