Polarimetric learning: a Siamese approach to learning distance metrics of algal Mueller matrix images

Polarisation optics for biomedical and clinical applications: a review

Many polarisation techniques have been harnessed for decades in biological and clinical research, each based upon measurement of the vectorial properties of light or the vectorial transformations imposed on light by objects. Various advanced vector measurement/sensing techniques, physical interpretation methods, and approaches to analyse biomedically relevant information have been developed and harnessed. In this review, we focus mainly on summarising methodologies and applications related to tissue polarimetry, with an emphasis on the adoption of the Stokes-Mueller formalism. Several recent breakthroughs, development trends, and potential multimodal uses in conjunction with other techniques are also presented. The primary goal of the review is to give the reader a general overview in the use of vectorial information that can be obtained by polarisation optics for applications in biomedical and clinical research.

MuellerNet: a hybrid 3D-2D CNN for cell classification with Mueller matrix images

Different from conventional microimaging techniques, polarization imaging can generate multiple polarization images in a single perspective by changing the polarization angle. However, how to efficiently fuse the information in these multiple polarization images by a convolutional neural network (CNN) is still a challenging problem. In this paper, we propose a hybrid 3D-2D convolutional neural network called MuellerNet, to classify biological cells with Mueller matrix images (MMIs). The MuellerNet includes a normal stream and a polarimetric stream, in which the first Mueller matrix image is taken as the input of normal stream, and the rest MMIs are stacked to form the input of a polarimetric stream. The normal stream is mainly constructed with a backbone network and, in the polarimetric stream, the attention mechanism is used to adaptively assign weights to different convolutional maps. To improve the network's discrimination, a loss function is introduced to simultaneously optimize parameters of the two streams. Two Mueller matrix image datasets are built, which include four types of breast cancer cells and three types of algal cells, respectively. Experiments are conducted on these two datasets with many well-known and recent networks. Results show that the proposed network efficiently improves the classification accuracy and helps to find discriminative features in MMIs.

Polarization Measurements and Evaluation Based on Multidimensional Polarization Indices Applied in Analyzing Atmospheric Particulates

Online identification and characterization of suspended aerosols can provide a scientific basis for understanding aerosol transformations, quantitatively evaluating the impacts on air quality, public health, and the source apportionment of different atmospheric particulate matters. In this study, we confirm the validity of our developed high-throughput multi-angle polarized scattering vector detection of aerosols and multidimensional polarization scattering index systems. By observation of the mean values, variance, and Wilk’s Lambda of multidimensional polarization indices for different aerosol types, the polarization index shows unique characterization abilities for aerosol properties, and the optimal combination of polarization indices can always be found for a specific aerosol category with a high resolution and discrimination. Clearly, the multidimensional polarization indices of individual aerosols are more suitable for online and real-time aerosol identification and even help to explain the in situ microphysical characteristics of aerosols or evaluate the dynamic evolution of aerosols.