Machine-learning-based classification of Stokes-Mueller polarization images for tissue characterization

The microstructural analysis of tissues plays a crucial role in the early detection of abnormal tissue morphology. Polarization microscopy, an optical tool for studying the anisotropic properties of biomolecules, can distinguish normal and malignant tissue features even in the absence of exogenous labelling. To facilitate the quantitative analysis, we developed a polarization-sensitive label-free imaging system based on the Stokes-Mueller calculus. Polarization images of ductal carcinoma tissue samples were obtained using various input polarization states and Stokes-Mueller images were reconstructed using Matlab software. Further, polarization properties, such as degree of linear and circular polarization and anisotropy, were reconstructed from the Stokes images. The Mueller matrix obtained was decomposed using the Lu-Chipman decomposition method to acquire the individual polarization properties of the sample, such as depolarization, diattenuation and retardance. By using the statistical parameters obtained from the polarization images, a support vector machine (SVM) algorithm was trained to facilitate the tissue classification associated with its pathological condition.