Chaya Narayan

Polarimetric phenomenology of photons with lung cancer tissue

The objective of this study is to explore the polarimetric phenomenology of light interaction with healthy and early-stage lung cancer tissue samples by applying efficient polarimetric backscattering detection techniques combined with polarimetric exploratory data analysis. Preliminary results indicate that enhanced discrimination signatures can be obtained for certain types of early-stage lung cancers based on their depolarization, backscattered intensity and retardance characteristics.

Integrative paradigms bridging defense and bioscience

The objective of the study is to present integrative paradigms highlighting their applicability of polarimetry to multidisciplinary areas such as space defense and bioscience applications. Polarimetric sensing and imaging offer unique advantages for a wide range of detection and classification problems due to the intrinsic potential for high contrast in different polarization components of the backscattered light. Indeed, polarized imaging can yield high-specificity images under high-dynamic range and extreme condition scenarios, in scattering media, or cluttered environments, offering at the same instance information related to the object material composition and its surface characteristics. In this study, a new imaging approach based on polarimetric detection principles will be introduced and the Mueller matrix formalism will be defined, and will be applied for space applications, such as detection of unresolved objects, as well as for early cancer detection. The design principles of the liquid crystal polarimetric imaging system will be introduced and related to operating conditions and system performance metrics. The depolarization, diattenuation, and retardance of the materials will be estimated using Mueller matrix decomposition for different aspect angles.

Infrared photon discrimination of lung cancer cells

The objective of this study is to explore the polarimetric phenomenology of near infrared light interaction with healthy and lung cancer monolayer cells by using efficient polarimetric transmission detection techniques. Preliminary results indicate that enhanced discrimination between normal and different types of lung cancer cell stages can be achieved based on their transmitted intensities and depolarization properties of the cells. Specifically, the sizes of the nuclei of the cancer cells and the nucleus-to-cytoplasmic ratios appear to have potential impact on the detected polarimetric signatures leading to enhanced discrimination of lung cancer cells.

Near infrared light interaction with lung cancer cells

The objective of this study is to explore the phenomenology of near infrared (NIR) light interaction with healthy and early-lung cancer by combining efficient polarimetric backscattering detection techniques with Polarimetric Exploratory Data Analysis (pEDA). Preliminary results indicate that enhanced discrimination signatures can be obtained for certain types of lung cancers.

Polarimetric wavelet fractal remote sensing principles for space materials

A new remote sensing approach based on polarimetric wavelet fractal detection principles is introduced and the Mueller matrix formalism is defined, aimed at enhancing the detection, identification, characterization, and discrimination of unresolved space objects at different aspect angles. The design principles of a multifunctional liquid crystal monostatic polarimetric ladar are introduced and related to operating conditions and system performance metrics. Backscattered polarimetric signal contributions from different space materials were detected using a laboratory ladar testbed, and then analyzed using techniques based on wavelets and fractals. The depolarization, diattenuation, and retardance of the materials were estimated using Mueller matrix decomposition for different aspect angles. The outcome of this study indicates that polarimetric fractal wavelet principles may enhance the capabilities of the ladar to provide characterization and discrimination of unresolved space objects.

Integrated quantitative fractal polarimetric analysis of monolayer lung cancer cells

Digital diagnostic pathology has become one of the most valuable and convenient advancements in technology over the past years. It allows us to acquire, store and analyze pathological information from the images of histological and immunohistochemical glass slides which are scanned to create digital slides. In this study, efficient fractal, wavelet-based polarimetric techniques for histological analysis of monolayer lung cancer cells will be introduced and different monolayer cancer lines will be studied. The outcome of this study indicates that application of fractal, wavelet polarimetric principles towards the analysis of squamous carcinoma and adenocarcinoma cancer cell lines may be proved extremely useful in discriminating among healthy and lung cancer cells as well as differentiating among different lung cancer cells.

Statistical analysis on polarimetric study of lung cancer cells

The objective of this study is the discrimination and characterization of different lung cancer monoline cells using statistical analysis of polarimetric backscattered signals. The main aspect of this study is the use of the Welchs t-test and the p-value statistics as a representative metric for discriminating distributions based on their mean and standard deviation. The outcome of this study indicates that enhanced discrimination of lung cancer samples can be obtained based on their t-test values between different cancer samples for different geometries.

An automated ladar polarimetric system for remote characterization of space materials

The calibration, testing, and operational principles of an efficient multifunctional monostatic polarimetric ladar are introduced and related to the system performance metrics. The depolarization, diattenuation, and retardance of the materials were estimated using Mueller matrix (MM) decomposition for different aspect angles. The outcome of this study indicates that polarimetric principles may enhance the capabilities of the ladar to provide adequate characterization and discrimination of unresolved space objects.

Lung cancer pathology discrimination techniques using time series analysis

The goal of this study is to discover, analyze, compare, and interpret diffused reflectance polarimetric signatures from lung cancer cells through time series analysis techniques, by using recently invented efficient polarimetric backscattering detection techniques. Specifically, different time series analyses, relying on linear and generalized linear modeling, have been investigated, with special emphasis on the Granger test for the time series. The experimental results indicate that statistically enhanced discrimination between normal and different types of lung cancer cells and stages can be achieved based on the pairwise comparisons of the time series diffused reflectance signal intensities and depolarization properties of the cells.

An automated digital fluorescence imaging system of tumor margins using clustering-based image thresholding

An optical system for efficient fluorescence imaging of cancer margins aiming at enhanced discrimination of the tumor area from the surrounding normal tissue, is presented. Fluorescence imaging was used to acquire grayscale images of brain tumor samples of 10 μm slice thickness. The tumor cells are characterized as Gli36Δ5 cells expressing Green Fluorescent Protein (GFP). An image processing technique involving the clustering-based concept of Otsu segmentation was applied to enhance the contrast and difference between the tumor and the rest of the tissue for improved visualization of tumor margins. Edge detection was performed on these processed images to mark the boundaries of the tumor area. The fluorescence imaging results depict clear demarcation of tumor boundary and a substantial improvement of the contrast, post processing.