Four Channel based Stokes-Mueller Polarimetry for Tissue Characterization Integrated with Machine Learning

Abstract

We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as machine learning (ML) output images were analysed effectively. With the statistical features obtained from polarization images, a support vector machine (SVM) algorithm was trained to facilitate the tissue classification associated with its pathological condition.