Connecting the microscopic depolarizing origin of samples with macroscopic measures of the Indices of Polarimetric Purity

In this work we show how a specific set of three depolarizing observables, the Indices of Polarimetric Purity (IPP), P1, P2 and P3, are ideal metrics to study the depolarization characteristic of media. We simulate different depolarizing scenarios, based on different depolarizing origins, and we study the corresponding IPP values. The simulations are based on the incoherent addition of multiple elemental polarizing elements, as ideal polarizers and/or retarders with different specific characteristics (orientation, retardance, transmittance, etc.). Further depolarizing scenarios are also studied by including the effect of ideal depolarizers. We show for the first time how by analyzing depolarizing systems through IPP we unravel two different depolarizing origins: isotropic and anisotropic depolarization, with meaningful physical interpretation. The former, isotropic depolarization is related to pure scattering processes, and mainly connected with P3 observable. The later, anisotropic depolarization is originated by microscopic constituent elements showing polarimetric anisotropy (dichroic and/or birefringent elements with different characteristics) and anisotropic scattering produced by these elements, and mainly described by P1 and P2 observables. Both effects can be simultaneously observed in real samples and give us information of the processes that give rise to depolarization in light-matter interactions. The simulated results are experimentally validated by analyzing the depolarizing behavior, in terms of IPP, of diverse real samples with easy physical interpretation, and direct connection with simulations. The present study could be of interest in multiple scenarios, to further understand the depolarizing response of samples, and it can be of special interest for the study of biological tissues and pathologies, as they present important depolarizing behavior.Monica Canabal-Carbia reports financial support was provided by Spain Ministry of Science and Innovation (PID2021-560 126509OB-C21 and PDC2022-133332-C21). Juan Campos reports financial support was provided by Spain Ministry of Science and Innovation (PID2021-560 126509OB-C21 and PDC2022-133332-C21). Angel Lizana reports financial support was provided by Spain Ministry of Science and Innovation (PID2021-560 126509OB-C21 and PDC2022-133332-C21). Irene Estevez reports financial support was provided by Government of Catalonia (Beatriu de Pinos, 2021-BP-00206). Ignacio Moreno reports financial support was provided by Spain Ministry of Science and Innovation (PID2021-126509OB-C22). Andres Marquez reports financial support was provided by Government of Valencia. Andres Marquez reports financial support was provided by Spain Ministry of Science and Innovation ( PID2021-123124OB-I00). Esther Nabadda reports financial support was provided by Government of Valencia. Mónica Canabal-Carbia, Angel Lizana and Juan Campos reports financial support was provided by the Generalitat de Catalunya (2021SGR00138)

Inspection of plant pathologies through pseudocolored images based on polarimetric basis

The study of the interaction of biological tissue with polarized light leads to relevant information of physical properties (dichroism, retardance and depolarization) of samples. Polarimetric analysis of different characteristics in tissues is useful for applications such us tissue classification, contrast enhancement or pathology detection. By means of polarimetric imaging techniques we can characterize the polarimetric signature of biological samples in a noninvasive and nondestructive way. We have found that depolarization information is of special interest in turbid media such as plant tissue. In this manuscript we use polarimetric observables for plant inspection. In particular, we provide enhanced visualization of certain plant pathologies by constructing depolarization based pseudocolored images of pathological leaves where the pathological areas are revealed

Polarimetric Images of Biological Tissues Based on the Arrow Decomposition of Mueller Matrices

Polarimetric techniques are widely used in a vast number of applications such as remote sensing, material characterization, astronomy and biological tissue inspection. In this last scenario, different polarimetric observables have proved their potential for enhancing imaging visualization. In this work we use a set of polarimetric observables derived from the arrow decomposition of the Mueller matrix for the first time: enpolarizing, retarding and depolarizing descriptors. In particular, the mean intensity coefficient and the three indices of polarimetric purity, the absolute values and Poincaré orientations of diattenuation, polarizance, entrance retardance and exit retardance vectors are considered. Results show images with enhanced visualization or even revealing invisible structures when compared to standard intensity images. In particular, thanks to these metrics, we improve the visualization of the necrotic areas of a Vitis rupestris leaf. In the case of animal samples, boundaries between different fascicles inside a tendon of an ex vivo chicken sample are revealed, as is the directionality of fiber tracts of the subcortical white matter in an ex vivo cow brain. The experimental results show the potential for biophotonics imaging and how polarimetric techniques could be useful for biomedical and botanical applications

Enhancing biological tissue structures visualization through polarimetric parameters

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

Polarimetric Images of Biological Tissues Based on the Arrow Decomposition of Mueller Matrices

Polarimetric techniques are widely used in a vast number of applications such as remote sensing, material characterization, astronomy and biological tissue inspection. In this last scenario, different polarimetric observables have proved their potential for enhancing imaging visualization. In this work we use a set of polarimetric observables derived from the arrow decomposition of the Mueller matrix for the first time: enpolarizing, retarding and depolarizing descriptors. In particular, the mean intensity coefficient and the three indices of polarimetric purity, the absolute values and Poincaré orientations of diattenuation, polarizance, entrance retardance and exit retardance vectors are considered. Results show images with enhanced visualization or even revealing invisible structures when compared to standard intensity images. In particular, thanks to these metrics, we improve the visualization of the necrotic areas of a Vitis rupestris leaf. In the case of animal samples, boundaries between different fascicles inside a tendon of an ex vivo chicken sample are revealed, as is the directionality of fiber tracts of the subcortical white matter in an ex vivo cow brain. The experimental results show the potential for biophotonics imaging and how polarimetric techniques could be useful for biomedical and botanical applications

Depolarizing metrics in the biomedical field: Vision enhancement and classification of biological tissues

Polarimetry encompasses a collection of optical techniques broadly used in a variety of fields. Nowadays, such techniques have provided their suitability in the biomedical field through the study of the polarimetric response of biological samples (retardance, dichroism and depolarization) by measuring certain polarimetric observables. One of these features, depolarization, is mainly produced by scattering on samples, which is a predominant effect in turbid media as biological tissues. In turn, retardance and dichroic effects are produced by tissue anisotropies and can lead to depolarization too. Since depolarization is a predominant effect in tissue samples, we focus on studying different depolarization metrics for biomedical applications. We report the suitability of a set of depolarizing observables, the indices of polarimetric purity (IPPs), for biological tissue inspection. We review some results where we demonstrate that IPPs lead to better performance than the depolarization index, which is a well-established and commonly used depolarization observable in the literature. We also provide how IPPs are able to significantly enhance contrast between different tissue structures and even to reveal structures hidden by using standard intensity images. Finally, we also explore the classificatory potential of IPPs and other depolarizing observables for the discrimination of different tissues obtained from ex vivo chicken samples (muscle, tendon, myotendinous junction and bone), reaching accurate models for tissue classification