Polarimetric imaging microscopy for advanced inspection of vegetal tissues

Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry

Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs. A common challenge when capturing images in the field relates to the spectral reflection of sunlight (glare) from crop leaves that, at certain solar incidences and sensor viewing angles, presents unwanted signals. The research presented here involves the convergence of 2 parallel projects to develop a facile algorithm that can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf's tissue. The first project is a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a maize field across multiple diurnal cycles throughout a growing season. The second project is a multistatic fiber-based Mueller matrix bidirectional reflectance distribution function (mmBRDF) instrument which measures the polarized light-scattering behavior of individual maize leaves. The mmBRDF data was fitted to an existing model, which outputs parameters that were used to run simulations. The simulated data were then used to train a shallow neural network which works by comparing unpolarized 2-band vegetation index (VI) with linearly polarized data from the low-reflectivity bands of the VI. Using GNDVI and red-edge reflection ratio we saw an improvement of an order of magnitude or more in the mean error (ϵ) and a reduction spanning 1.5 to 2.7 in their standard deviation (ϵσ) after applying the correction network on the HIP sensor data.

Optimizing the classification of biological tissues using machine learning models based on polarized data

Polarimetric data is nowadays used to build recognition models for the characterization of organic tissues or the early detection of some diseases. Different Mueller matrix-derived polarimetric observables, which allow a physical interpretation of a specific characteristic of samples, are proposed in literature to feed the required recognition algorithms. However, they are obtained through mathematical transformations of the Mueller matrix and this process may loss relevant sample information in search of physical interpretation. In this work, we present a thorough comparative between 12 classification models based on different polarimetric datasets to find the ideal polarimetric framework to construct tissues classification models. The study is conducted on the experimental Mueller matrices images measured on different tissues: muscle, tendon, myotendinous junction and bone; from a collection of 165 ex-vivo chicken thighs. Three polarimetric datasets are analyzed: (A) a selection of most representative metrics presented in literature; (B) Mueller matrix elements; and (C) the combination of (A) and (B) sets. Results highlight the importance of using raw Mueller matrix elements for the design of classification models.

Optical monitoring of the plant growth status using polarimetry

Polarimetry is a powerful characterization technique that uses a wealth of information from electromagnetic waves, including polarization. Using the rich information provided by polarimetry, it is being actively studied in biomedical fields such as cancer and tumor diagnosis. Despite its importance and potential in agriculture, polarimetry for living plants has not been well studied. A Stokes polarimetric imaging system was built to determine the correlation between the polarization states of the light passing through the leaf and the growth states of lettuce. The Stokes parameter s3 associated with circular polarization increased over time and was strongly correlated with the growth of lettuce seedlings. In the statistical analysis, the distribution of s3 followed the generalized extreme value (GEV) probability density function. Salt stress retarded plant growth, and the concentration of treated sodium chloride (NaCl) showed a negative correlation with the location parameter μ of GEV. The clear correlation reported here will open the possibility of polarization measurements on living plants, enabling real-time monitoring of plant health.

Holistic and efficient calibration method for Mueller matrix imaging polarimeter with a high numerical aperture

High-numerical aperture (N A>0.6) Mueller matrix imaging polarimeter (MMIP) (high-NA MMIP) is urgently needed for higher resolution. Usually, the working distance of high-NA MMIP is too short to perform in situ calibration by a usual reference sample, such as polarizer and retarder plates. The polarization effects of the substrate that attach the sample are never calibrated. So, the resolution and accuracy of the MMIP is hard to further promote. In this paper, a holistic and efficient calibration method is innovated for high-NA MMIP. Two film polarizers and a film retarder as well as a blank substrate are first adopted as the reference samples in calibration. Different from the conventional eigenvalue calibration method (ECM), the holistic calibration theory and process are established. All polarimetric errors arising from the devices, subsystems, and the substrate can be calibrated in one process. The normalized measurement error is less than 0.0024 for NA 0.95 MMIP, which is an order of magnitude lower than those of NA 0.1 and 0.2 MMIPs in publications. The excellent performance of calibrated high-NA MMIP is demonstrated by tissue polarimetry with higher resolution, accuracy, and more appropriate dynamic range.

Automatic pseudo-coloring approaches to improve visual perception and contrast in polarimetric images of biological tissues

Imaging polarimetry methods have proved their suitability to enhance the image contrast between tissues and structures in organic samples, or even to reveal structures hidden in regular intensity images. These methods are nowadays used in a wide range of biological applications, as for the early diagnosis of different pathologies. To include the discriminatory potential of different polarimetric observables in a single image, a suitable strategy reported in literature consists in associating different observables to different color channels, giving rise to pseudo-colored images helping the visualization of different tissues in samples. However, previous reported polarimetric based pseudo-colored images of tissues are mostly based on simple linear combinations of polarimetric observables whose weights are set ad-hoc, and thus, far from optimal approaches. In this framework, we propose the implementation of two pseudo-colored methods. One is based on the Euclidean distances of actual values of pixels and an average value taken over a given region of interest in the considered image. The second method is based on the likelihood for each pixel to belong to a given class. Such classes being defined on the basis of a statistical model that describes the statistical distribution of values of the pixels in the considered image. The methods are experimentally validated on four different biological samples, two of animal origin and two of vegetal origin. Results provide the potential of the methods to be applied in biomedical and botanical applications.

Polarimetric observables for the enhanced visualization of plant diseases

This paper highlights the potential of using polarimetric methods for the inspection of plant diseased tissues. We show how depolarizing observables are a suitable tool for the accurate discrimination between healthy and diseased tissues due to the pathogen infection of plant samples. The analysis is conducted on a set of different plant specimens showing various disease symptoms and infection stages. By means of a complete image Mueller polarimeter, we measure the experimental Mueller matrices of the samples, from which we calculate a set of metrics analyzing the depolarization content of the inspected leaves. From calculated metrics, we demonstrate, in a qualitative and quantitative way, how depolarizing information of vegetal tissues leads to the enhancement of image contrast between healthy and diseased tissues, as well as to the revelation of wounded regions which cannot be detected by means of regular visual inspections. Moreover, we also propose a pseudo-colored image method, based on the depolarizing metrics, capable to further enhance the visual image contrast between healthy and diseased regions in plants. The ability of proposed methods to characterize plant diseases (even at early stages of infection) may be of interest for preventing yield losses due to different plant pathogens.

Linear diattenuation imaging of biological samples with digital lensless holographic microscopy

A digital lensless holographic microscope (DLHM) sensitive to the linear diattenuation produced by biological samples is reported. The insertion of a linear polarization-states generator and a linear polarization-states analyzer in a typical DLHM setup allows the proper linear diattenuation imaging of microscopic samples. The proposal has been validated for simulated and experimental biological samples containing calcium oxalate crystals extracted from agave leaves and potato starch grains. The performance of the proposed method is similar to that of a traditional polarimetric microscope to obtain linear diattenuation images of microscopic samples but with the advantages of DLHM, such as numerical refocusing, cost effectiveness, and the possibility of field-portable implementation.

Mueller matrix imaging with a polarization camera: application to microscopy

In this work, we describe the design and implementation of a Mueller matrix imaging polarimeter that uses a polarization camera as a detector. This camera simultaneously measures the first three Stokes components, allowing for the top three rows of the Mueller matrix to be determined after only N = 4 measurements using a single rotating compensator, which is sufficient to fully characterize nondepolarizing samples. This setup provides the polarimetric analysis with micrometric resolution in about 3 seconds and can also perform live birefringence imaging at the camera frame rate by fixing the compensator at a static 45° angle. To further improve the conditioning of the setup, we also give the first experimental demonstration of an optimal elliptical retarder design.

Polarimetric data-based model for tissue recognition

We highlight the potential of a predictive optical model method for tissue recognition, based on the statistical analysis of different polarimetric indicators that retrieve complete polarimetric information (selective absorption, retardance and depolarization) of samples. The study is conducted on the experimental Mueller matrices of four biological tissues (bone, tendon, muscle and myotendinous junction) measured from a collection of 157 ex-vivo chicken samples. Moreover, we perform several non-parametric data distribution analyses to build a logistic regression-based algorithm capable to recognize, in a single and dynamic measurement, whether a sample corresponds (or not) to one of the four different tissue categories.