LAUSR

A Mueller matrix is a comprehensive representation of the polarization transformation properties of a sample, encoding very rich information on the microstructure of the scattering objects. However, it is often inconvenient to use individual Mueller matrix elements to characterize the microstructure due to a lack of explicit connections between the matrix elements and the physics properties of the scattering samples. In this review, we summarize the methods to derive groups of polarization parameters, which have clear physical meanings and associations with certain structural properties of turbid media, including various Mueller matrix decomposition (MMD) methods and the Mueller matrix transformation (MMT) technique. Previously, experimentalists have chosen the most suitable method for the specific measurement scheme. In this review, we introduce an emerging novel research paradigm called ‘polaromics’. In this paradigm, both MMD and MMT parameters are considered as polarimetry basis parameters (PBP), which are used to construct polarimetry feature parameters (PFPs) for the quantitative characterization of complex biomedical samples. Machine learning techniques are involved to find PFPs that are sensitive to specific micro- or macrostructural features. The goal of this review is to provide an overview of the emerging ‘polaromics’ paradigm, which may pave the way for biomedical and clinical applications of polarimetry.