George Giakos

Design, Calibration, and Testing of an Automated Near-Infrared Liquid-Crystal Polarimetric Imaging System for Discrimination of Lung Cancer Cells

A newly developed tissue diagnostic method, based on label-free near-infrared (NIR) polarimetric reflectance imaging for the classification of histopathological samples of lung cancer cells, is presented. The design, calibration, and testing of an automated NIR polarimetric imaging system, with emphasis on lung cancer detection, are put forward in detail. The described electro-optical imaging polarimeter system exhibits high degree of accuracy and repeatability. The outcome of this paper indicates that the operational design principles of the NIR polarimetric system may be proved extremely useful in discrimination of label-free normal and lung cancer cell samples as well as differentiation of different lung cancer cells and stages in vitro.

Bioinformatics of Lung Cancer

The objective of this study is to explore novel bioinformatics techniques, namely, the Polarimetric Exploratory Data Analysis (pEDA), for early identification and discrimination of precancerous and cancerous lung tissues. The outcome of this study indicates that the full-width-at half maximum (FWHM) and Dynamic Range (DR) extracted from histograms of inherent (label-free) near infrared (NIR) diffused-polarimetric reflectance signals provide an important metrics for the characterization of cancerous tissue. Application of pEDA on the acquired data has been proved an effective diagnostic tool aimed at discriminating optical information among normal, precancerous, and cancerous lung tissue samples. Therefore, it can eventually be proved a useful diagnostic tool in the early detection of Non-Small Cell Lung Cancer (NSCLC) as well as in classical cytopathology and histopathology.

Application of non-negative matrix factorization for the deconvolution of petroleum mixtures using mid FTIR analysis

The aim of this study was to develop an efficient, in terms of both time and cost, but still reliable methodology, capable of identifying the chemical fractions in complex commercial petroleum products. The performance of a methodology based on Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) analytical signals is demonstrated, combined with a modified factorization algorithm to solve this “mixture problem”. The results of this innovative work, regarding both the application of the adapted deconvolution technique to petroleum analysis and its self-adaptation to data without any former initialization, indicate that it is possible to reveal the content in a chemically complex petroleum mixture, working solely with the infrared signals of a limited number of samples and without any other a priori information. A focus application of the proposed methodology is the quality control of commercial gasoline by identifying and quantifying the individual fractions used for its formulation.

Polarimetric backscattered Mueller matrix bidirectional reflectance distribution function (MmBRDF) for remote inspection of spacecrafts

Thermal insulation of space satellites and space probes often consists of Multi-layer insulation (MLI), composed of multiple layers of Mylar or Kapton coated on one side with a thin layer of metal, typically silver or aluminum. The purpose of this paper is to investigate the optical characteristics of several materials used as integral part in the design of spacecrafts. In this study, preliminary results on the remote interrogation of Mylar layers, using backscattered polarimetric imaging, at different aspect angles, are reported. Specifically, the Mueller matrix of the Bidirectional Reflectance Distribution function (MmBRDF) for Mylar was constructed, based on the estimation of the Mueller matrix (MM) elements, at different aspect angles of the target, under quasi-monostatic geometry. Measurements were performed using an in-house-built, auto calibrated, scalable imaging polarimetric system. The calibration results demonstrate the high quality of the acquired measurements, characterized by stability, and accuracy. The outcome of the study indicates that polarimetric backscattered Mueller matrix enhances significantly the intrinsic merit of our system to monitor, inspect and characterize materials commonly used in space.

Stereo System for Remote Monitoring of River Flows

In this article we present a video-based method for river flow monitoring. The proposed method aims at deriving efficient approximations of the river velocity using natural formations on the river surface. In order to overcome peculiarities of the flow, we propose to uniformly exploit all such structures that appear locally with short temporal duration. Towards this direction we explore the expanded capabilities of a stereoscopic camera layout with the dual observation fields and the potential of reverting projective deformations. By mapping to world coordinates, all spatial locations in the video reflect velocity as a uniform field, except for local flow variations. The velocity estimation is performed by computing the optical flow using a series of video frames, combining the information of the views of both cameras. The novelty of the proposed river flow estimation scheme lies on the fact that the accuracy of motion estimation is increased due to the use of the complementary views, which also enables the transition from a 2-Dimensional image-based velocity estimate to 3-Dimensional estimates. The estimated optical velocity is back-projected to the real world coordinates using the parameters extracted using the stereoscopic layout. The results on simulated and real conditions demonstrate that the proposed method is efficient in the estimation of the surface velocity and robust against locally disappearing formations, since it can compensate for a loss with other formations active in the field of view.

Mesh Free based Variational Level Set Evolution for Breast Region Segmentation and Abnormality Detection using Mammograms

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.

Label-free discrimination of lung cancer cells through mueller matrix decomposition of diffuse reflectance imaging

Abstract In this article, we explore the potential of an original label-free Near-Infrared (NIR) imaging technique, based on Mueller Matrix decomposition reflectance, for efficient detection and classification of histopathological samples of lung cancer cells. Experimental results were acquired, processed, and analyzed by means of an accurate, fully-automated, auto-calibrated liquid-crystal NIR polarimetric imaging system, developed for real-time Mueller matrix analysis and optical characterization of target media. The polarimetric Figure-of-Merits (FOMs), estimated using Mueller matrix decomposition, as well as the statistics associated with the sixteen Mueller matrix elements of each lung cell sample indicate that enhanced discrimination among the samples can be achieved. Similarly, polarimetric Exploratory Data Analysis (pEDA), based on histograms obtained from diffuse reflectance polarimetric signals, has been used to determine if aberrations and/or changes in the spread of the histogram between different stages of lung cancer can be proved effective biomarkers for its progression and also discrimination among different lung pathologies. The outcome of this study indicates that Mueller matrix formalism may be proved extremely useful in discriminating among healthy and malignant lung cells as well as differentiating among the different types of malignancies with high accuracy. As a result, it may contribute positively to the enhancement and implementation of the operational principles of the Whole Slide Imaging (WSI) field.

A novel laboratory design of a passive-imaging target recognition system operating under clutter

To pchieve desired levels of operational performance, air and ground target surveillance systems require enhanced capabilities for identifying targets within the surveillance space. The objective of this study is to present a novel experimental laboratory design of a passive-illumination target recognition system. Polarimetric Exploratory Data Analysis (pEDA) has been applied for the detection and discrimination of targets in the presence of cluttered media, as well as for clutter characterization. The outcome of this study indicates that fusion of (pEDA) and Stokes parameters lead to reliable Figures-of-Merit (FOM)s that can be used as powerful tools for image analysis, characterization, and discrimination of cluttered targets, under passive illumination.

Low-order statistical analysis of 1-D diffuse reflectance signals from cancer cells using 2-D scalogram images

The novelty of this study is seen in the efficient characterization of cancer cells through representation of 1-D backscattered signals from cancer cells using statistical analysis on 2-D scalogram images. The proposed approach allows one to obtain a rapid and accurate visualization of the frequency components associated with the backscattered signals; while enlightening the understanding and the physics of the diffuse reflectance light waves interactions with the lung cells, by performing texture characterization and pattern recognition analysis directly on the scalograms.

Qualitative and quantitative determination of oil base mud using non-negative matrix factorization

The aim of this study was to develop an off-line, fast, efficient and reliable analytical methodology, capable to determine, both qualitatively and quantitatively, the content of a common oil base mud filtrate in contaminated formation fluids samples. We demonstrate the performance of a methodology based on Gel-Permeation Chromatographic (GPC) analytical signals, combined with a modified factorization algorithm to estimate the oil fractions and an optimization procedure to calculate their concentrations within the mixtures. The proposed scheme is innovatively applied for the determination of the mud filtrate in contaminated oil samples and self-adapted to data without any former information. The results of this work indicate the efficiency of the algorithmic framework to reveal the content of a complex petroleum mixture analyzing the chromatograms of a limited number of samples. The presented technique has a great potential in upstream oil industry, enabling the un-mixing of analytical signals from mixtures of oil samples contaminated with specific petroleum fractions.