Depolarizing behavior is commonly observed in most natural samples. For this reason, optical tools measuring the differences in depolarization response among spatially separated structures are highly useful in a wide range of imaging applications for enhanced visualization of structures, target identification, etc. One commonly used tool for depolarizing discrimination is the so-called depolarizing spaces. In this article, we exploit the combined use of two depolarizing spaces, the indices of polarization purity (IPP) and polarizance–reflection–transformation (PRT) spaces, to improve the capability of optical systems to identify polarization–anisotropy depolarizers. The potential of these spaces to discriminate among different depolarizers is first studied from a series of simulations by incoherently adding diattenuations or retarders, with some control parameters emulating samples in nature. The simulated results demonstrate that the proposed methods are capable of increasing differences among depolarizers beyond other well-known techniques. Experimentally, validation is provided by conducting diverse phantom experiments of easy interpretation and mimicking the stated simulations. As a useful application of our approach, we developed a model able to retrieve intrinsic microscopic information of samples from macroscopic polarimetric measurements. The proposed methods enable non-invasive, straightforward, macroscopic characterization of depolarizing samples, and may be of interest for enhanced visualization of samples in multiple imaging scenarios.
Polarimetry comprises a set of noninvasive and nondesctructive optical techniques that demonstrated their great interest in biophotonics due to its capability to obtain relevant information from biological samples in a noninvasive and nondestructive way. Various polarimetric observables, derived from the Mueller matrix of a sample, are used to probe the efficacy of these techniques in pathology detection or different biological structures classification. The physical properties of a sample related to polarization can be categorized in three groups: retardance, dichroism and depolarization. In this work, we propose the study of the polarimetric observables linked to these physical properties for the identification of different structures within an ex-vivo cow brain sample by means of different pseudo-coloration methods. In particular, we study pseudo-coloration functions based on the Gaussian and Cauchy probabilistic functions. These probabilistic functions allow us to compute the probability of a given part of a sample to belong to a particular class (i.e. healthy or pathological or different structures inside the same sample) where, this probability depends on the polarimetric observables obtained from the studied sample. Our investigation encompasses a study of different observables and methodologies to find the optimal approach for brain tissue identification (identification of gray and white matter in ex-vivo cow brain) and, which may be of interest in multiple biomedical scenarios such as early pathology detection and diagnosis or enhanced visualization of different structures for clinical applications.
In this study we analyze the effect of experimental errors on the optimization and calibration method of a Mueller matrix imaging polarimeter based on liquid crystal variable retarders. The study is carried out through numerical simulations, where the optimized Mueller matrix polarimeter is simulated considering misalignments of the polarimetric components, and variations in the induced retardance of the LCVRs. However, the final measurement error does not depend only on non-ideal elements, but also depends on the noise, in the irradiance measurements, and the accuracy of the calibration method. Thus, the eigenvalue calibration method is used in the simulations, including random variations in the irradiance matrix. The tolerances of the optimization and calibration method are analyzed, and the results are presented.
We present the optimization and calibration of a Mueller matrix imaging polarimeter. The polarimeter is intended to be used as a compact tool for biomedical diagnosis, in particular for skin examination. The device uses a pixelated-polarization camera along with a fixed variable retarder for the polarization state analyzer, minimizing the number of elements used in the device for Full-Mueller matrix measurements. For the polarization state generator the device uses an LED as light source, and a polarizer followed by a pair of LCVRs to generate any polarization state over the Poincare sphere. With this system the polarization properties of a sample can be obtained with a total of 8 measurements to extract the 16 elements of the Mueller matrix. We study the optimization strategies by minimizing the condition number of the instrument matrix, to maximize the signal to noise ratio, and reducing the effect of experimental errors on the optimization. As a compact and transportable tool, the polarimeter will be used in different environments, so an auto-calibration method is also required. In this work we explore the necessary conditions to successfully use the Eigenvalue calibration method in a polarized-camera and liquid crystals based polarimeter. The study presented will help to measure the Mueller matrix of a sample with high accuracy and precision levels, needed for the study of biological samples.
Polarimetric techniques have widely demonstrated their potential in biophotonics due to its capability to obtain relevant information from biological samples in a noninvasive and nondestructive way. Different polarimetric observables, obtained from the Mueller matrix of a sample, are used to explore the potential of these techniques in pathology detection or different biological structures classification. The physical properties of a sample related to polarization can be divided in three main groups: retardance, dichroism and depolarization. In this work, we propose the study of the polarimetric observables related to these physical properties for the identification of different structures in a biological sample by means of different pseudo-coloration methods. In particular, we study pseudo-coloration functions based on the Gaussian and Cauchy probabilistic functions. These probabilistic functions allow us to compute the probability of a given part of a sample to belong to a particular class (i.e. healthy or pathological or different structures in the same sample) where, this probability depends on the polarimetric observables obtained from the studied sample. We present a study of the different observables and methods to find the best approach for brain tissue identification (identification of gray and white matter in ex-vivo cow brain) and, which may be of interest in multiple biomedical scenarios such as early pathology detection and diagnosis or enhanced visualization of different structures for clinical applications.
Polarimetric data is nowadays used in the biomedical field to inspect organic tissues or for the early detection of some pathologies. In this work, we present a thorough comparison between different classification models based on several sets of polarimetric data, this allowing us to choose the polarimetric framework to construct tissue classification models. Four different well-known machine learning models are compared by analyzing three polarimetric datasets: (i) a selection of ten representative polarimetric observables; (ii) the Mueller matrix elements; and (iii) the combination of (i) and (ii) datasets. The study is conducted on the experimental Mueller matrices images measured on different organic tissues: muscle, tendon, myotendinous junction and bone; all of them measured from a collection of 165 ex-vivo chicken thighs. Provided results show the potential of polarimetric datasets for classification of biological tissues and paves the way for future applications in biomedicine and clinical trials.
Polarimetry comprises a set of optical techniques of great interest in biophotonics due to its capability to obtain relevant information from biological samples by means of noninvasive and nondestructive methods. For instance, they are useful in pathology detection or different biological structures classification, among others. By studying the polarimetric response of biological samples we can obtain the information of how different structures produce different changes in the polarimetric characteristics of light. These changes depend on the polarimetric properties of the samples: depolarization, dichroism or retardance. From the experimental measurement of the Mueller matrix (M) of a sample, these polarimetric observables related to the mentioned tissue properties can be obtained. In this work, we propose the study of a particular set of observables derived from the Arrow decomposition of M. These parameters are used to inspect different biological tissues properties with the purpose of obtaining images with enhanced contrast of different biological structures in a tissue. In particular, we applied these observables to the study of a sample of animal origin: an ex-vivo cow brain; in order to differentiate between white and gray matter. Obtained results provide the interest of Arrow-derived polarimetric observables which may be of interest in multiple biomedical scenarios such as early pathology detection and diagnosis or enhanced visualization of different structures for clinical applications.
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.
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.
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