This PDF file contains the front matter associated with SPIE Proceedings Volume 12382, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Scars usually do not show strong contrast under standard skin examination by using dermoscopes. We show that Mueller matrix polarimetry can provide strong contrast for in vivo scar imaging. Scars usually develop after skin injury when the body repairs the damaged tissue. They are causing multiple distresses such as movement restrictions, pain, itchiness, and the psychological impact of the associated cosmetic disfigurement. Scar treatment has significant economic impact as well. Mueller matrix polarimetry with integrated autofocus and automatic data registration can potentially improve the scar assessment by the dermatologists and help to objectify the evaluation of the treatment outcome.

Knowledge of fiber microstructure and orientation in the brain is critical for understanding the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease. Diffusion magnetic resonance imaging (dMRI) is a noninvasive imaging modality that can generate mappings of nerve fiber orientation. Due to rigorous levels of mathematical modeling involved in reconstructing dMRI data; and limited spatial resolution, there arises a need to validate the biological accuracy of collected dMRI data. Polarized light imaging (PLI) has been shown to have potential for microstructural validation due to the anisotropy in many biological tissues, particularly in myelin sheaths surrounding nerve fibers in the brain. Using PLI for this purpose is appealing because it is directly sensitive to tissue structure and can be done at high resolution. While several studies have had success using PLI for fiber mapping, continuing to advance this modality, particularly reflectance based PLI systems, could provide a valuable avenue for in vivo neural imaging. In order to reach the full potential of reflectance PLI systems, some key questions remain such as the ability of PLI to resolve crossing fibers, and the sensitivity of reflectance PLI to fiber inclination. Tissue phantoms are one potential method to isolate these issues in order to investigate them. In this proceeding, a five-wavelength reflectance Mueller matrix polarimeter is used for imaging of promising PLI tissue phantoms as well as regions of interest in fixed ferret brain samples. The retardance, diattenuation and depolarization mappings are derived from the Mueller matrix and studied in order to assess the sensitivity of this polarimeter configuration to different fiber orientations.

We developed a polarized hyperspectral microscope to collect four types of Stokes vector data cubes (S0, S1, S2, and S3) of the pathologic slides with head and neck squamous cell carcinoma (HNSCC). Our system consists of an optical light microscope with a movable stage, two polarizers, two liquid crystal variable retarders (LCVRs), and a SnapScan hyperspectral camera. The polarizers and LCVRs work in tandem with the hyperspectral camera to acquire polarized hyperspectral images. Synthetic pseudo-RGB images are generated from the four Stokes vector data cubes based on a transformation function similar to the spectral response of human eye for the visualization of hyperspectral images. Collagen is the most abundant extracellular matrix (ECM) protein in the human body. A major focus of studying the ECM in tumor microenvironment is the role of collagen in both normal and abnormal function. Collagen tends to accumulate in and around tumors during cancer development and growth. In this study, we acquired images from normal regions containing normal cells and collagen fibers and from tumor regions containing cancerous squamous cells and collagen fibers on HNSCC pathologic slides. The preliminary results demonstrated that our customized polarized hyperspectral microscope is able to improve the visualization of collagen on HNSCC pathologic slides under different situations, including thick fibers of normal stroma, thin fibers of normal stroma, fibers of normal muscle cells, fibers accumulated in tumors, fibers accumulated around tumors. Our preliminary results also demonstrated that the customized polarized hyperspectral microscope is capable of extracting the spectral signatures of collagen based on Stokes vector parameters and can have various applications in pathology and oncology.

Polarimetrical imaging is a noninvasive optical technique of great interest in biophotonics since it has the capability of obtaining relevant information of biological samples, being useful, for instance, for the early detection of diseases or the classification of biological structures, both on animal and vegetal tissues. Different structures produce different outcomes when interacting with light due to their polarimetric properties such as depolarization, dichroism or retardance. An exhaustive polarimetric analysis of these characteristics can unveil the relation between the tissue inherent characteristics and its polarimetric response, enabling us to find the most appropriate polarimetric parameters to describe or study a sample. These polarimetric characteristics can be obtained through the experimental measurement of the Mueller matrix (M) of a sample, from which a range of different polarimetric observables, giving physical interpretation, can be deduced. By taking advantage of these parameters, we propose a study of the suitability of different groups of metrics for the contrast enhancement in biological tissues imaging, taking special attention on some depolarization metrics and some physical parameters such as the wavelength or the angle of incidence of the illumination light. The results obtained suggest the convenience of certain parameters which may be of interest in multiple biomedical scenarios such as pathology early detection or enhanced visualization of different structures for clinical applications.

Polarization-resolved extension of Second Harmonic Generation microscopy (PSHG) exhibits proven efficiency in cancer diagnosis. Contrary to the case of white light microscopy, PSHG can reveal small structural collagen changes, during tumorigenesis, for a broad range of organs such as breast, thyroid, lung, pancreas, and ovary. However, despite its effectiveness for cancer diagnosis, PSHG is not yet fully exploited. One way of improvement consists in taking better advantage of polarization-resolved measurements which are performed by acquiring multiple images (usually between three to 20) of the same sample under different input beam polarization conditions. Each image of the resulting stacked raw images set can contain relevant information not found in the other images of the set. In the literature, information extraction from stacked raw images is performed using methods such as averaging of all images, collagen structural parameters modeling or PSHG polarimetric parameters extraction. If the two latter methods provide a richer information than the first one, they may, however, suffer from a loss of information from the stacked raw images. To examine this potential loss of information, AI methods can be used for extracting information from the stacked raw images. Using recently available images of the public SHG-TIFF database, dealing with breast and thyroid PSHG measurements of both normal and tumor tissues, we test available AI methods for information extraction and benchmark these methods to the state-of-the-art, in terms of automatic cancer diagnosis efficiency.

White blood cells, also called leukocytes, are hematopoietic cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. The abnormal development and uncontrolled proliferation of these cells can lead to devastating cancers. Their timely recognition in the peripheral blood is critical to diagnosis and treatment. In this study, we developed a microscopic imaging system for improving the visualization of white blood cells on Wright’s stained blood smear slides, with two different setups: polarized light imaging and polarized hyperspectral imaging. Based on the polarized light imaging setup, we collected the RGB images of Stokes vector parameters (S0, S1, S2, and S3) of five types of white blood cells (neutrophil, eosinophil, basophil, lymphocyte, and monocyte), and calculated the Stokes vector derived parameters: the degree of polarization (DOP), the degree of linear polarization (DOLP), and the degree of circular polarization (DOCP). Based on the polarized hyperspectral imaging setup, we also calculated Stokes vector data. The preliminary results demonstrate that Stokes vector derived parameters (DOP, DOLP, and DOCP) could improve the visualization of granules in granulocytes (neutrophils, eosinophils, and basophils). Furthermore, Stokes vector derived parameters (DOP, DOLP, and DOCP) could improve the visualization of surface structures (protein patterns) of lymphocytes enabling subclassification of lymphocyte subpopulations. Finally, S2, S3, and DOCP could improve the visualization of morphology on nucleus of monocytes. We also demonstrated that the polarized hyperspectral imaging setup could provide complementary spectral information to the spatial information on different Stokes vector parameters of white blood cells. This work demonstrates that polarized light imaging and polarized hyperspectral imaging has the potential to become a strong imaging tool in the diagnosis of disorders arising from white blood cells.

Polarimetric images are used for the characterization of biological tissues as well as for the early detection of some diseases. Recently, it has been demonstrated that accurate classification models can be constructed based on polarimetric data, such as the Mueller matrix (MM) or different polarimetric metrics resulting from combinations of different MM elements. The choice of polarimetric observables to be used for classifying is usually arbitrary, but mathematical transformations from MM elements to other metrics may benefit or impair the accuracy of the final models. This work presents a thorough comparison of different classification models based on typical machine learning algorithms trained according to different polarimetric metrics, in the search of the most efficient polarimetric basis. The classification models are tested on different biological tissues obtained from a collection of ex-vivo chickens.

In this paper, we have built a Stokes imaging microscope system with a large field of view, and then validated the system. Because of the thin pathology sections and the low signal-to-noise ratio of Stokes imaging, accurate measurements of the weaker polarization signals are required. So at the light source side, we added a beam shaping module to make the distribution of light intensity more uniform. In addition, we used air as a standard sample to calibrate the measurement results, which makes the results more accurate. The specific slide scanning process is also presented in this paper, and we have used the solution to test it on actual pathology sections, and the experimental results show that the complete system works well and has the potential to assist in pathology analysis.

For pathological diagnosis using optical microscopes, imaging speed and resolution are two important considerations. Usually, the improvement of imaging resolution will reduce the field of view and greatly extend the imaging time. Recently, studies have shown that some polarization information obtained from Mueller matrix connecting with structural features can be preserved under relatively low imaging resolution. Here, to systematically investigate the influence of imaging resolution on polarization properties derived from Mueller matrix, we first perform transmission Mueller matrix microscopic imaging on the unstained rat dorsal skin tissue samples, which have rich fibrous structures. Then, we carry out quantitative analysis using the statistics and the image texture feature parameters to compare the Mueller matrix polarimetric parameters images at different resolutions. The results show that, compared with the traditional non-polarized microscopic images, the Mueller matrix polarimetric parameters, which can characterize the fiber density information of the sample, are less sensitive to the imaging resolution, while other polarimetric parameters derived from the Mueller matrix related to the particle sizes still need high resolution to provide accurate structural information. This study demonstrates that it is possible to consider both imaging resolution and speed when using Mueller matrix polarimetry for tissue detection, and proposes relevant criteria to meet the above requirements, which is of great significance for application scenarios that need accurate and highspeed optical measurement.

Recently, Mueller matrix polarimetry has received increasing attention in the field of biophotonics, because of its great potential for non-invasive, label-free detection of microstructural and optical properties of biomedical samples. In this study, we propose a method for automatic identification and quantitative evaluation of skin hair follicle structure based on Mueller matrix polarimetry combined with the K-means clustering machine learning algorithm. First, we use the transmission Mueller matrix microscope to measure the rat skin tissue sections with hair follicles. Then we derive the Mueller matrix transformation (MMT) parameters images to reveal the characteristics of the birefringent skin hair follicle structure. By taking the MMT parameters images as the identification objects, we adopt the K-means clustering algorithm to segment them and carry out image denoising processing to achieve the automatic detection of the hair follicle structure. Finally, to identify the hair follicle structure quantitatively and accurately, we conduct a comprehensive evaluation of five indexes including quantity, area, position, long axis, and short axis of the recognized regions. The results show that the method presented in this study can realize the automatic identification and quantitative evaluation of skin hair follicle structures, having great potential for the detection and clinical diagnosis of skin structures.

The outbreak of red tide seriously affects marine ecology and exploitation of fishery resources, so it is necessary to monitor for suspended particles in seawater. Due to the characteristics of various types and great changes of suspended particles in seawater, a detailed classification method based on big data is needed, and polarized light technology has great potential in this respect. In this study, we have designed a prototype for measuring absorption and scattering properties based on polarized light illumination by adding polarization state generation module and polarization state analysis module on the base of a commercial instrument named AC-S. The prototype can measure the intrinsic optical properties of water for different incident polarized light, including water extinction coefficient c(λ) and absorption coefficient a(λ). In addition, the prototype can also measure the polarization scattering information of suspended particles, which is closely related to the complex refractive index, morphology and microstructure of particles. The polarization properties of water bodies are represented by Stokes vectors. The instrument is illuminated by LED with a central wavelength of 532 nm. During the measurement, a pump drives the sample through the flow tube for detection. The polarization generation module produces a specific incident polarization beam that is directed through an optical window into the flow tube. The light signal, which is absorbed and scattered by the suspended particles in the flow tube, is then received by the polarization analysis module, which completes the measurement of light intensity and polarization. The experimental results show that for the same sample, the inherent optical properties are different under different incident polarization states, which is closely related to the properties of particles in water. We have classified the polarization data of water bodies containing different particles with the help of support vector machine (SVM) algorithm, and all of them have obtained more than 90% classification accuracy.