Mueller polarimetric imaging of biological tissues: classification in a decision-theoretic framework

Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films

Our work uses a polarization matrix formalism to analyze and algorithmically represent optical anisotropy by open dehydration of blood plasma films. Analytical relations for Jones matrix reconstruction of optical birefringence maps of protein crystal networks of dehydrated biofluid films are found. A technique for 3D step-by-step measurement of the distributions of the elements of the Jones matrix or Jones matrix images (JMI) of the optically birefringent structure of blood plasma films (BPF) has been created. Correlation between JMI maps and corresponding birefringence images of dehydrated BPF and saliva films (SF) obtained from donors and prostate cancer patients was determined. Within the framework of statistical analysis of layer-by-layer optical birefringence maps, the parameters most sensitive to pathological changes in the structure of dehydrated films were found to be the central statistical moments of the 1st to 4th orders. We physically substantiated and experimentally determined the sensitivity of the method of 3D polarization scanning technique of BPF and SF preparations in the diagnosis of endometriosis of uterine tissue.

Mueller polarimetric imaging as a tool for detecting the effect of non-thermal plasma treatment on the skin

Non-thermal plasma (NTP) is a promising technique studied for several medical applications such as wound healing or tumor reduction. The detection of microstructural variations in the skin is currently performed by histological methods, which are time-consuming and invasive. This study aims to show that full-field Mueller polarimetric imaging is suitable for fast and without-contact detection of skin microstructure modifications induced by plasma treatment. Defrosted pig skin is treated by NTP and analyzed by MPI within 30 minutes. NTP is shown to modify the linear phase retardance and the total depolarization. The tissue modifications are inhomogeneous and present distinct features at the center and the fringes of the plasma-treated area. According to control groups, tissue alterations are primarily caused by the local heating concomitant to plasma-skin interaction.

Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.

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.

Mueller matrix imaging for collagen scoring in mice model of pregnancy

Preterm birth risk is associated with early softening of the uterine cervix in pregnancy due to the accelerated remodeling of collagen extracellular matrix. Studies of mice model of pregnancy were performed with an imaging Mueller polarimeter at different time points of pregnancy to find polarimetric parameters for collagen scoring. Mueller matrix images of the unstained sections of mice uterine cervices were taken at day 6 and day 18 of 19-days gestation period and at different spatial locations through the cervices. The logarithmic decomposition of the recorded Mueller matrices mapped the depolarization, linear retardance, and azimuth of the optical axis of cervical tissue. These images highlighted both the inner structure of cervix and the arrangement of cervical collagen fibers confirmed by the second harmonic generation microscopy. The statistical analysis and two-Gaussians fit of the distributions of linear retardance and linear depolarization in the entire images of cervical tissue (without manual selection of the specific regions of interest) quantified the randomization of collagen fibers alignment with gestation time. At day 18 the remodeling of cervical extracellular matrix of collagen was measurable at the external cervical os that is available for the direct optical observations in vivo. It supports the assumption that imaging Mueller polarimetry holds promise for the fast and accurate collagen scoring in pregnancy and the assessment of the preterm birth risk.

Probing layered structures by multi-color backscattering polarimetry and machine learning

Polarization imaging can quantitatively probe the characteristic microstructural features of biological tissues non-invasively. In biomedical tissues, layered structures are common. Superposition of two simple layers can result in a complex Mueller matrix, and multi-color backscattering polarimetry can help to probe layered structures. In this work, multi-color backscattering Mueller matrix images are measured for living nude mice skins. Preliminary analysis of anisotropy parameter A and linear polarizance parameter b show signs of a layered structure in the skin. For more detailed examinations on polarization features of layered samples, we generate Mueller matrices by experimenting with two-layered thick tissues and concentrically aligned silk submerged in milk. Then we use supervised machine learning to identify polarization parameters that are sensitive to layered structure and guide the synthesis of more parameters. Monte Carlo simulation is also adopted to explore the relationship between parameters and microstructures of media. We conclude that multi-color backscattering polarimetry combined with supervised machine learning can be applied to probe the characteristic microstructure in layered living tissue samples.

Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K-NN and semantic segmentation classification

We propose an approach for discriminating fibrillar collagen fibers from elastic fibers in the mouse cervix in Mueller matrix microscopy using convolutional neural networks (CNN) and K-nearest neighbor (K-NN) for classification. Second harmonic generation (SHG), two-photon excitation fluorescence (TPEF), and Mueller matrix polarimetry images of the mice cervix were collected with a self-validating Mueller matrix micro-mesoscope (SAMMM) system. The components and decompositions of each Mueller matrix were arranged as individual channels of information, forming one 3-D voxel per cervical slice. The classification algorithms analyzed each voxel and determined the amount of collagen and elastin, pixel by pixel, on each slice. SHG and TPEF were used as ground truths. To assess the accuracy of the results, mean-square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used. Although the training and testing is limited to 11 and 5 cervical slices, respectively, MSE accuracy was above 85%, SNR was greater than 40 dB, and SSIM was larger than 90%.

Revisiting the generalized polar decomposition of Mueller matrices

Mueller polarimetry is a powerful imaging modality that has been successfully applied to various application fields. Decomposition of Mueller matrices in elementary components is classically considered in order to unfold complex physical phenomena taking place in probed samples or scenes. In this context, the generalized polar decomposition, also known as Lu and Chipman decomposition, plays a prominent role. In this paper, we show that the set of candidate generalized polar decompositions is richer than the set used so far. Negative-determinant Mueller matrices are naturally addressed in the proposed framework. We show that taking into account those supplementary polar decompositions addresses issues raised in the literature. Application is carried out on synthetic and on measured Mueller matrices.

Feature extraction on Mueller matrix data for detecting nonporous electrospun fibers based on mutual information

The surface morphology of electrospun fibers largely determines their application scenarios. Conventional scanning electron microscopy is usually used to observe the microstructure of polymer electrospun fibers, which is time consuming and will cause damage to the samples. In this paper, we use backscattering Mueller polarimetry to classify the microstructural features of materials by statistical learning methods. Before feeding the Mueller matrix (MM) data into the classifier, we use a two-stage feature extraction method to find out representative polarization parameters. First, we filter out the irrelevant MM elements according to their characteristic powers measured by mutual information. Then we use Correlation Explanation (CorEx) method to group interdependent elements and extract parameters that represent their relationships in each group. The extracted parameters are evaluated by the random forest classifier in a wrapper forward feature selection way and the results show the effectiveness in classification performance, which also shows the possibility to detect nonporous electrospun fibers automatically in real time.

Innovative integrated numerical-experimental method for high-performance multispectral Mueller polarimeters based on ferroelectric liquid crystals

In this work, an original and effective integrated numerical-experimental approach is proposed for building a high-performance multispectral Mueller polarimeter based on ferroelectric liquid crystals (FLCs). This method relies on accurate experimental characterization of the optical components specifically selected to construct such a system, combined with a numerical procedure used to optimize it, in the spectral range of interest, by means of a global optimization function. The proposed strategy enabled the construction of an FLC-based Mueller polarimeter in transmission configuration operating between 450 and 700 nm. The robustness of this system to various optical component misalignments, as well as the conditions to keep the measurement error less than 1% over the whole spectral range of interest, have been determined experimentally. The proposed strategy is very well suited to build optimized multispectral Mueller polarimetric systems for biomedical applications for which variations of the order of a few percent in the elements of the measured Mueller matrices need to be appreciated.