Mueller Matrix Imaging Research Articles

Ptychographic Mueller matrix imaging (PMMI): principle and proof-of-concept demonstration.

Quantitative characterization of optical anisotropies is extremely important for wide fields and applications. The Mueller matrix, providing all the polarization-related properties of a medium, is a powerful tool for the comprehensive evaluation of optical anisotropies. Here, we propose a ptychographic Mueller matrix imaging (PMMI) technique, which features the Mueller matrix polarization modulation being introduced into the ptychography. The ptychographic reconstruction is performed for each polarization state, and the Mueller matrix can be determined from the reconstructed polarization-modulated amplitude images. A proof-of-concept of the proposed PMMI is implemented, and both simulations and experiments are conducted to demonstrate the validity of the method. Results indicate that the imaging resolution of the home-built PMMI apparatus achieves 1.550 µm at the wavelength of 633 nm, which is of the highest level for the Mueller matrix imaging to the best of our knowledge. A customized birefringent specimen is characterized, and both retardance and axis azimuth are quantitatively evaluated.

PoLambRimetry: a multispectral polarimetric atlas of lamb brain.

Mueller matrix imaging (MMI) is a comprehensive form of polarization imaging useful for assessing structural changes. However, there is limited literature on the polarimetric properties of brain specimens, especially with multispectral analysis. We aim to employ multispectral MMI for an exhaustive polarimetric analysis of brain structures, providing a reference dataset for future studies and enhancing the understanding of brain anatomy for clinicians and researchers. A multispectral wide-field MMI system was used to measure six fresh lamb brain specimens. Multiple decomposition methods (forward polar, symmetric, and differential) and polarization invariants (indices of polarimetric purity and anisotropy coefficients) have been calculated to obtain a complete polarimetric description of the samples. A total of 16 labels based on major brain structures, including grey matter (GM) and white matter (WM), were identified. -nearest neighbors classification was used to distinguish between GM and WM and validate the feasibility of MMI for WM identification. As the wavelength increases, both depolarization and retardance increase, suggesting enhanced tissue penetration into deeper layers. Moreover, utilizing multiple wavelengths allowed us to track dynamic shifts in the optical axis of retardance within the brain tissue, providing insights into morphological changes in WM beneath the cortical surface. The use of multispectral data for classification outperformed all results obtained with single-wavelength data and provided over 95% accuracy for the test dataset. The consistency of these observations highlights the potential of multispectral wide-field MMI as a non-invasive and effective technique for investigating the brain's architecture.

Clear imaging method for underwater targets based on the second Lorentz depolarization index

Underwater target information is submerged in background scattered light due to the scattering effect of suspended particles in turbid water. In order to improve the quality of target imaging in turbid water, this paper proposes a method for achieving clear underwater target imaging using the second Lorentz depolarization index based on a dual division of focal plane Mueller matrix imaging system. This method can directly quantify the attenuation of light in water, and then solve a clear image of the target. Experimental results on underwater targets of different materials (plastic, paper, metal) show that the method proposed can effectively remove background scattering light while enhancing image contrast, ultimately improving the imaging quality of targets in turbid water.

Identification of microalgal particles using pixel feature analysis of Mueller matrix images

Fine sorting of aquatic particles is of great significance for water environment monitoring. Natural water contains aquatic particles that exhibit a high degree of diversity and complexity, and the identification of aquatic particles remains a persistent challenge in the field. In this article, we propose a new technique for identifying the target species of microalgal particles by using the pixel feature analysis of Mueller matrix images. This technique is independent of any prior knowledge or data about the existing particles in the environment, which is advantageous when applied to real-world situations. The pixel-level polarimetric features are fully leveraged to construct polarization feature templates, which can be used to characterize and filter specific microalgal particles in complex environments. This method could enable the accurate detection of harmful algal blooms species in natural water, which can facilitate early warning of algal blooms. The preliminary results show that the recall rate reached 97.2%, and the average accuracy is 98.9%, which demonstrate the effectiveness of this approach for identifying the target species of aquatic particles in natural water.

Combination of Muller matrix imaging polarimetry and artificial intelligence for classification of mice skin cancer tissue in-vitro and in-vivo

Mueller matrix imaging polarimetry is a fast and non-invasive technique for discriminating between different types of biological samples based on the characteristics of polarized light interacting with them. Combining Mueller matrix imaging polarimetry with artificial intelligence provides further advantages in detecting different kinds of medical conditions in an automated manner. Accordingly, the present study proposes a method based on Mueller matrix polarimetry and machine learning algorithms for discriminating between (1) four different types of mice skin tissues (normal, acanthosis, papilloma, and squamous cell carcinoma); (2) two types of mice skin tissues which show histological similarities to human equivalents (normal and squamous cell carcinoma); and (3) 7,12-dimethylbenz[a]anthracene/estrogen-induced mice skin tissues. Five machine learning classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting, and Gaussian Naïve Bayes, are considered in the first and second applications, while three models (Support Vector Machine, K-Nearest Neighbor, Gradient Boosting) are considered in the third application. For each application, the features which dominate the machine learning prediction performance are determined through multivariate correlation matrix analysis, kernel density estimation, and Analysis of Variance tests. The experimental results show that the Random Forest model achieves the highest classification accuracy (93.55 %) for the first application, while the Support Vector Machine model yields the highest accuracy for both the second and third applications (97.66 % and 100 %, respectively). Overall, the proposed framework consisting of Mueller matrix imaging polarimetry and machine learning provides a strong foundation for the on-going development of screening and diagnosis methods for human skin cancer.

Time-efficient filtering of imaging polarimetric data by checking physical realizability of experimental mueller matrices.

Imaging Mueller polarimetry has already proved its potential for biomedicine, remote sensing and metrology. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, ie to test the positive semi-definiteness of the 4 × 4 Hermitian coherency matrix calculated from the elements of corresponding Mueller matrix pixel-wise. For this purpose, we compared the execution time for the calculations of i) eigenvalues, ii) Cholesky decomposition, iii) Sylvester's criterion, and iv) coefficients of the characteristic polynomial (two different approaches) of the Hermitian coherency matrix, all calculated for the experimental Mueller matrix images (600 pixels × 700 pixels) of mouse uterine cervix. The calculations were performed using C ++ and Julia programming languages. Our results showed the superiority of the algorithm iv) based on the simplification via Pauli matrices over other algorithms for our dataset. The sequential implementation of latter algorithm on a single core already satisfies the requirements of real-time polarimetric imaging. This can be further amplified by the proposed parallelization (e.g., we achieve a 5-fold speed up on 6 cores). The source codes of the algorithms and experimental data are available at https://github.com/pogudingleb/mueller_matrices.

Metasurface-enabled single-shot and complete Mueller matrix imaging

Metasurface-enabled single-shot and complete Mueller matrix imaging

Complex Spatial Illumination Scheme Optimization of Backscattering Mueller Matrix Polarimetry for Tissue Imaging and Biosensing.

Polarization imaging and sensing techniques have shown great potential for biomedical and clinical applications. As a novel optical biosensing technology, Mueller matrix polarimetry can provide abundant microstructural information of tissue samples. However, polarimetric aberrations, which lead to inaccurate characterization of polarization properties, can be induced by uneven biomedical sample surfaces while measuring Mueller matrices with complex spatial illuminations. In this study, we analyze the detailed features of complex spatial illumination-induced aberrations by measuring the backscattering Mueller matrices of experimental phantom and tissue samples. We obtain the aberrations under different spatial illumination schemes in Mueller matrix imaging. Furthermore, we give the corresponding suggestions for selecting appropriate illumination schemes to extract specific polarization properties, and then provide strategies to alleviate polarimetric aberrations by adjusting the incident and detection angles in Mueller matrix imaging. The optimized scheme gives critical criteria for the spatial illumination scheme selection of non-collinear backscattering Mueller matrix measurements, which can be helpful for the further development of quantitative tissue polarimetric imaging and biosensing.

Fast and high-accuracy collinear reflection Mueller imaging polarimeter implemented with the compound calibration method

The collinear reflection Mueller matrix imaging polarimeter is suitable for characterizing thick samples with high-scattering depolarization such as biological tissues or in-situ living organs. Achieving fast detection and high measurement accuracy is vital to prevent artifacts and accurately assess polarization characteristics in these applications. This paper demonstrates a fast collinear reflection imaging polarimeter based on liquid crystal variable retarders (LCVRs-CRMMIP). We propose a novel compound calibration method (CCM), to the best of our knowledge, which enhances measurement accuracy through light intensity correction and an improved equivalent calibration sample model. This method surpasses the double-pass eigenvalue calibration method (dp-ECM), enhancing accuracy by over 23 times. Performance evaluations with standard samples, including mirrors, linear polarizers, and wave plates, reveal that the LCVRs-CRMMIP achieves rapid measurements (about 3 s) and high accuracy with an error of less than 0.0017.

Multifractal Scaling of Singularity Spectra ofDigital Mueller-matrix Images of BiologicalTissues: Fundamental and Applied Aspects

Multifractal Scaling of Singularity Spectra ofDigital Mueller-matrix Images of BiologicalTissues: Fundamental and Applied Aspects

Mechanical stability of polarization signatures in biological tissue characterization.

Mueller matrix imaging polarimetry (MMIP) is a promising technique for investigating structural abnormalities in pathological diagnosis. The characterization stability of polarization signatures, described by Mueller matrix parameters (MMPs), correlates with the mechanical state of the biological medium. In this study, we developed an MMIP system capable of applying quantitative forces to samples and measuring the resulting polarization signatures. Mechanical stretching experiments were conducted on a mimicking phantom and a tissue sample at different force scales. We analyzed the textural features and data distribution of MMP images and evaluated the force effect on the characterization of MMPs using the structural similarity index. The results demonstrate that changes in the mechanical microenvironment (CMM) can cause textural fluctuations in MMP images, interfering with the stability of polarization signatures. Specifically, parameters of anisotropic orientation, retardance, and optical rotation are the most sensitive to CMM, inducing a dramatic change in the overall image texture, while other parameters (e.g., polarization, diattenuation, and depolarization) exhibit locality in their response to CMM. For some MMPs, CMM can enhance regional textural contrasts. This study elucidates the mechanical stability of polarization signatures in biological tissue characterization and provides a valuable reference for further research toward minimizing CMM influence.

Probing Layered Tissues by Backscattering Mueller Matrix Imaging and Tissue Optical Clearing

Polarization imaging is a label-free and non-invasive technique that is sensitive to microstructure and suitable for probing the microstructure of living tissues. However, obtaining deep-layer information from tissues has been a challenge for optical techniques. In this work, we used tissue optical clearing (TOC) to increase optical penetration depth and characterize the layered structures of tissue samples. Different tissue phantoms were constructed to examine changes in the polarization features of the layered structure during the TOC process. We found that depolarization and anisotropy parameters were able to distinguish between single-layer and double-layer phantoms, reflecting microstructural information from each layer. We observed changes in polarization parameter images during the TOC process and, by analyzing different regions of the images, explained the sensitivity of these parameters to double-layer structures and analyzed the influence of oblique incident illumination. Finally, we conducted TOC experiments on living skin samples, leveraging the experience gained from phantom experiments to identify the double-layer structure of the skin and extract features related to layered structures. The results show that the combination of backscattering polarization imaging and tissue optical clearing provides a powerful tool for the characterization of layered samples.

MULTI-PARAMETER MUELLER-MATRIX TOMOGRAPHY OF HISTOLOGICAL SAMPLES OF BIOLOGICAL TISSUES AS AN ACCURATE AND EFFECTIVE METHOD FOR DETERMINING THE DEGREE OF BLOOD LOSS

Establishing the volume of blood loss is extremely important in the context of forensic practice, as it can indicate various circumstances of death and is important in solving criminal cases. Consideration of modern methods of determining this parameter in the article is relevant and aims to reveal new opportunities and perspectives in this field of forensic medical examination.
 Aim of the work. To develop a set of new forensic criteria for accurate determination of the volume of blood loss using the method of multichannel polarization Muller-matrix tomography of histological sections of parenchymal organs and human blood samples.
 Materials and methods. Samples of parenchymal organs and human blood were collected from 76 corpses of both sexes with varying degrees of blood loss from 0 mm3 to 2500 mm3. The research was carried out using the method of multichannel polarization Muller-matrix linear dichroism tomography of biological tissue samples.
 Results. For all studied biological samples, a decrease in the level of circular birefringence (CB) of formed blood elements against the background of a gradual necrotic decrease in distributions of linear birefringence (LB) of the optical anisotropy of parenchymal tissues and blood films was established for the process of blood loss. The range of sensitivity of the method of differential Mueller-matrix tomography with algorithmic reproduction of linear dichroism maps to changes in blood loss volume of the deceased, which is (0±2000) mm3, is determined.
 Conclusions. The accuracy of the method of differential Mueller matrix mapping with algorithmic reproduction of linear dichroism maps of biological preparations was determined, which is 86-92 % in the range of the level of blood loss ΔV = (0±2000) mm3.

Polarization-Based Digital Histology of Human Skin Biopsies Assisted by Deep Learning

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.

Algorithms for Polarization-singular processing of Mueller-matrix images of Soft Tissues for Biomedical Applications

Algorithms for Polarization-singular processing of Mueller-matrix images of Soft Tissues for Biomedical Applications

Speculum-free portable preterm imaging system.

Preterm birth is defined as a birth before 37 weeks of gestation and is one of the leading contributors to infant mortality rates globally. Premature birth can lead to life-long developmental impairment for the child. Unfortunately, there is a significant lack of tools to diagnose preterm birth risk, which limits patient care and the development of new therapies. To develop a speculum-free, portable preterm imaging system (PPRIM) for cervical imaging; testing of the PPRIM system to resolve polarization properties of birefringent samples; and testing of the PPRIM under an IRB on healthy, non-pregnant volunteers for visualization and polarization analysis of cervical images. The PPRIM can perform Mueller-matrix imaging to characterize the remodeling of the uterine cervix during pregnancy. The PPRIM is built with a polarized imaging probe and a flexible insertable sheath made with a compatible flexible rubber-like material to maximize comfort and ease of use. The PPRIM device is developed to meet specific design specifications as a speculum-free, portable, and comfortable imaging system with polarized imaging capabilities. This system comprises a main imaging component and a flexible silicone inserter. The inserter is designed to maximize comfort and usability for the patient. The PPRIM shows high-resolution imaging capabilities at the 20mm working distance and 25mm circular field of view. The PPRIM demonstrates the ability to resolve birefringent sample orientation and full field capture of a healthy, non-pregnant cervix. The development of the PPRIM aims to improve access to the standard of care for women's reproductive health using polarized Mueller-matrix imaging of the cervix and reduce infant and maternal mortality rates and better quality of life.

Discrepancy of coordinate system selection in backscattering Mueller matrix polarimetry: exploring photon coordinate system transformation invariants.

In biomedical studies, Mueller matrix polarimetry is gaining increasing attention because it can comprehensively characterize polarization-related vectorial properties of the sample, which are crucial for microstructural identification and evaluation. For backscattering Mueller matrix polarimetry, there are two photon coordinate selection conventions, which can affect the following Mueller matrix parameters calculation and information acquisition quantitatively. In this study, we systematically analyze the influence of photon coordinate system selection on the backscattering Mueller matrix polarimetry. We compare the Mueller matrix elements in the right-handed-nonunitary and non-right-handed-unitary coordinate systems, and specifically deduce the changes of Mueller matrix polar decomposition, Mueller matrix Cloude decomposition and Mueller matrix transformation parameters widely used in backscattering Mueller matrix imaging as the photon coordinate system varied. Based on the theoretical analysis and phantom experiments, we provide a group of photon coordinate system transformation invariants for backscattering Mueller matrix polarimetry. The findings presented in this study give a crucial criterion of parameters selection for backscattering Mueller matrix imaging under different photon coordinate systems.

Polarization properties and Umov effect of human hair

This study delves into the polarization properties of various hair colors using several techniques, including polarization ray tracing, full Stokes, and Mueller matrix imaging. Our analysis involved studying hair in both indoor and outdoor settings under varying lighting conditions. Our results demonstrate a strong correlation between hair color and the degree of linear polarization. Specifically, light-colored hair, such as white and blond, exhibits high albedo and low DoLP. In contrast, dark hair, like black and brown hair, has low albedo and high DoLP. Our research also revealed that a single hair strand displays high diattenuation near specular reflections but high depolarization in areas with diffuse reflections. Additionally, we investigated the wavelength dependency of the polarization properties by comparing the Mueller matrix under illumination at 450 nm and 589 nm. Our investigation demonstrates the impact of hair shade and color on polarization properties and the Umov effect.

Imaging Mueller matrix ellipsometry measurements on measuring fields in the micrometre range

An imaging Mueller matrix ellipsometer is used to measure structures in measuring fields in the micrometre range, which are too small for conventional ellipsometry. Line and grid structures are measured and evaluated with the help of numerical simulations using the finite element method to characterize the structure parameters.

Mueller matrix imaging polarimeter with polarization camera self-calibration applied to structured light components

This work presents a complete Mueller matrix imaging polarimeter that uses three liquid-crystal retarders and a pixelated polarization camera. The polarimeter is characterized and optimized with a standard correction procedure here adapted to be performed fully in-situ, without any additional element, based on considering the polarization camera as the reference. The accuracy limit caused by the extinction ratio in the camera micro-polarizers is analyzed. Finally, the imaging polarimeter is tested experimentally by analyzing well-known samples for structured light applications such as patterned retarders, a patterned polarizer, and a liquid-crystal depolarizer. The work is presented in a tutorial style useful to reproduce the procedure by non-experts in polarimetry.