Analyzing the Influence of Imaging Resolution on Polarization Properties of Scattering Media Obtained From Mueller Matrix.

The Mueller matrix contains abundant micro- and even nanostructural information of media. Especially, it can be used as a powerful tool to characterize anisotropic structures quantitatively, such as the particle size, density, and orientation information of fibers in the sample. Compared with unpolarized microscopic imaging techniques, Mueller matrix microscopy can also obtain some essential structural information about the sample from the derived parameters images at low resolution. Here, to analyze the comprehensive effects of imaging resolution on polarization properties obtained from the Mueller matrix, we, first, measure the microscopic Mueller matrices of unstained rat dorsal skin tissue slices rich in collagen fibers using a series of magnifications or numerical aperture (NA) values of objectives. Then, the first-order moments and image texture parameters are quantified and analyzed in conjunction with the polarization parameter images. The results show that the Mueller matrix polar decomposition parameters diattenuation D, linear retardance δ, and depolarization Δ images obtained using low NA objective retain most of the structural information of the sample and can provide fast imaging speed. In addition, the scattering phase function analysis and Monte Carlo simulation based on the cylindrical scatterers reveal that the diattenuation parameter D images with different imaging resolutions are expected to be used to distinguish among the fibrous scatterers in the medium with different particle sizes. This study provides a criterion to decide which structural information can be accurately and rapidly obtained using a transmission Mueller matrix microscope with low NA objectives to assist pathological diagnosis and other applications.

Comparative study of the influence of imaging resolution on linear retardance parameters derived from the Mueller matrix.

Polarization imaging techniques are emerging tools to provide quantitative information of anisotropic structures, such as the density and orientation distribution of fibers in tissue samples. Recently, it is found that when using Mueller matrix polarimetry to obtain the structural features of tissue samples, some information can be revealed by relatively low-resolution polarization parameter images. Thus, to analyze what kinds of anisotropic optical and structural information contained in high-resolution polarization images are preserved in low-resolution ones, here we carry out a comparative study of the influence of imaging resolution on the Mueller matrix derived linear retardance parameters. We measure the microscopic Mueller matrix of human healthy breast duct tissues and ductal carcinoma in situ (DCIS) tissues, which have distinct typical fibrous structures, using objectives with different numerical aperture. Then we quantitatively compare a group of image texture feature parameters of the linear retardance parameters images under high and low imaging resolutions. The results demonstrate that the fibers density information contained in the texture features of linear retardance δ parameter image are preserved well with the decline of imaging resolution. While for the azimuthal orientation parameter θ which closely related to the spatial location, we still need high imaging resolution to obtain quantitative structural information. The study provides an important criterion to decide which information of fibrous structures can be extracted accurately using transmission Mueller matrix microscope with low numerical aperture objectives.

Probing variations of fibrous structures during the development of breast ductal carcinoma tissues via Mueller matrix imaging.

Recently, we developed a label-free method to probe the microstructural information and optical properties of unstained thin tissue slices based on microscopic Mueller matrix imaging technique. In this paper, we take the microscopic Mueller matrix images of human breast ductal carcinoma tissue samples at different pathological stages, and then calculate and analyze their retardance-related Mueller matrix-derived parameters. To reveal the microstructural features more quantitatively and precisely, we propose a new method based on first-order statistical properties of image to transform the 2D images of Mueller matrix parameters into several statistical feature vectors. We evaluate each statistical feature vector by corresponding classification characteristic value extracted from the statistical features of Mueller matrix parameters images of healthy breast duct tissue samples. The experimental results indicate that these statistical feature vectors of Mueller matrix derived parameters may become powerful tools to quantitatively characterize breast ductal carcinoma tissue samples at different pathological stages. It has the potential to facilitate automating the staging process of breast ductal carcinoma tissue, resulting in the improvement of diagnostic efficiency.