Objective: Mueller matrix polarimetry technique has been regarded as a powerful tool for probing the microstructural information of tissues. The multiplying of cells and remodeling of collagen fibers in breast carcinoma tissues have been reported to be related to patient survival and prognosis, and they give rise to observable patterns in hematoxylin and eosin (H&E) sections of typical breast tissues (TBTs) that the pathologist can label as three distinctive pathological features (DPFs)-cell nuclei, aligned collagen, and disorganized collagen. The aim of this paper is to propose a pixel based extraction approach of polarimetry feature parameters (PFPs) using a linear discriminant analysis (LDA) classifier. These parameters provide quantitative characterization of the three DPFs in four types of TBTs. Methods: The LDA-based training method learns to find the most simplified linear combination from polarimetry basis parameters (PBPs) constrained under the accuracy remains constant to characterize the specific microstructural feature quantitatively in TBTs. Results: We present results from a cohort of 32 clinical patients with analysis of 224 regions-of-interest. The characterization accuracy for PFPs ranges from 0.82 to 0.91. Conclusion: This work demonstrates the ability of PFPs to quantitatively characterize the DPFs in the H&E pathological sections of TBTs. Significance: This technique paves the way for automatic and quantitative evaluation of specific microstructural features in histopathological digitalization and computer-aided diagnosis.

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.

Mueller matrix polarimetry is a potentially powerful technique for obtaining microstructural information of biomedical specimens. Thus, it has found increasing application in both backscattering imaging of bulk tissue samples and transmission microscopic imaging of thin tissue slices. Recently, we proposed a technique to transform the 4 × 4 Mueller matrix elements into a group of parameters, which have explicit associations with specific microstructural features of samples. In this paper, we thoroughly analyze the relationships between the Mueller matrix transformation parameters and the characteristic microstructures of tissues by using experimental phantoms and Monte Carlo simulations based on different tissue mimicking models. We also adopt quantitative evaluation indicators to compare the Mueller matrix transformation parameters with the Mueller matrix polar decomposition parameters. The preliminary imaging results of bulk porcine colon tissues and thin human pathological tissue slices demonstrate the potential of Mueller matrix transformation parameters as biomedical diagnostic indicators. Also, this study provides quantitative criteria for parameter selection in biomedical Mueller matrix imaging.

Polarimetry is attracting increasing attention nowadays, due to its unique ability to obtain microstructural and optical information of tissue samples label-free.^{1 – 4} Among the available polarimetric techniques, Mueller matrix (MM) imaging can comprehensively characterize the polarization-related properties of media, including diattenuation, retardance, and depolarization, which are of great importance in biomedical studies and clinical trials.^{5 – 7} Recently, transmission MM microscopy has shown potential in detection of various abnormal and pathological tissues, such as breast cancer,⁸ cervical cancer,⁹ colon cancer,¹⁰ skin cancer,^11,12 liver cirrhosis,^13,14 gastrointestinal cancer,^15,16 thyroid cancer,¹⁷ and lung cancer.¹⁸ Meanwhile, the backscattering MM polarimetry provides a promising non-contact and quantitative tool for ex-vivo measurement of bulk tissue samples,^{6,7,19 – 22} in-vivo imaging of skin tissue,^{23 – 26} and endoscopic detection of internal organs.^{27 – 31}

Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts in imaging results, hindering following accurate microstructural features characterization. In this paper, we quantitatively analyze the beam drift phenomenon in dual-rotating retarder Mueller matrix microscopy and its impact on linear retardance measurement, which is frequently used to reflect tissue fiber arrangement. It is demonstrated that polarizer rotation induces larger beam drift than waveplate rotation due to surface non-uniformity and stress deformation. Furthermore, for waveplates rotated constantly in dual-rotating retarder scheme, their tilt within polarization state analyzer can result in more drift and throughput loss than those within polarization state generator. Finally, phantom and tissue experiments confirm that beam drift, rather than inherent optical path changes, dominates the systematic overestimation of linear retardance in boundary image regions. The findings highlight beam drift as a dominant error source for quantifying linear retardance, necessitating careful optical design alignment and a reliable registration algorithm to obtain highly accurate polarization data for training machine learning models of pathological diagnosis using Mueller matrix microscopy.