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

Abstract

Interaction of polarized light with healthy and abnormal regions of tissue, reveal structural information associated to its pathological condition. Even a slight variation in structural alignment, can induce change in polarization property, which can play a crucial role in early detection of abnormal tissue morphology. We propose, transmission-based Stokes-Mueller microscope for quantitative analysis of 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 in an effective manner. The algorithm trained with standard sample images, was able to classify the new sample into respective disease category. Further, by using the statistical parameters obtained from polarization images, a support vector machine (SVM) algorithm was trained to facilitate the tissue classification associated with its pathological condition. This robust technique enables automatic analysis of images with minimum human intervention and hence increases the classification accuracy.

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Speaker

Nirmal Mazumder
Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India-576104
India

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