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At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Optical image analysis for graphene layer detection: Enhanced green channel methodology

Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.

2D materials↗

NASA NTRS1982

Application of digital image analysis techniques to antimisting fuel spray characterization

Pulsed ruby laser sheet illumination of the spray is used for the initial recording of data on very-high-resolution photographic film. The digitization of mosaic elements is effected with a vidicon and video digitizer whose output is stored in computer RAM memory for processing. Highly nonspherical elements and a broad range of drop diameters (8-2000 microns) resulting from the unusual rheological properties of the fuel-additive system are accommodated by the device configuration and algorithms. It is found that the generation of two-dimensional images by means of scattered light also eliminates errors resulting from variations in the index of refraction and from the submicron scattering sites that are often present within the modified fuel. No a priori information on the drop size distribution or on the system response to various drop sizes is required.

Fleeter, R.↗

NASA NTRS1973

Image analysis techniques associated with automatic data base generation.

This paper considers some basic problems relating to automatic data base generation from imagery, the primary emphasis being on fast and efficient automatic extraction of relevant pictorial information. Among the techniques discussed are recursive implementations of some particular types of filters which are much faster than FFT implementations, a 'sequential similarity detection' technique of implementing matched filters, and sequential linear classification of multispectral imagery. Several applications of the above techniques are presented including enhancement of underwater, aerial and radiographic imagery, detection and reconstruction of particular types of features in images, automatic picture registration and classification of multiband aerial photographs to generate thematic land use maps.

Bond, A. D.↗

DOE OSTI2016

Phases and interfaces from real space atomically resolved data: Physics-based deep data image analysis

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ~1–10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. Furthermore, this method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure–property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2015

CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases

Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure vari-ations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagran-gian tracking approach were used to model respiratory airflows and aerosol dynamics. Respira-tions of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug de-livery protocol.

Aerosol breath test↗

NASA NTRS2000

Quantitative image analysis of laminin immunoreactivity in skin basement membrane irradiated with 1 GeV/nucleon iron particles

We previously reported that laminin immunoreactivity in mouse mammary epithelium is altered shortly after whole-body irradiation with 0.8 Gy from 600 MeV/nucleon iron ions but is unaffected after exposure to sparsely ionizing radiation. This observation led us to propose that the effect could be due to protein damage from the high ionization density of the ion tracks. If so, we predicted that it would be evident soon after radiation exposure in basement membranes of other tissues and would depend on ion fluence. To test this hypothesis, we used immunofluorescence, confocal laser scanning microscopy, and image segmentation techniques to quantify changes in the basement membrane of mouse skin epidermis. At 1 h after exposure to 1 GeV/nucleon iron ions with doses from 0.03 to 1.6 Gy, neither the visual appearance nor the mean pixel intensity of laminin in the basement membrane of mouse dorsal skin epidermis was altered compared to sham-irradiated tissue. This result does not support the hypothesis that particle traversal directly affects laminin protein integrity. However, the mean pixel intensity of laminin immunoreactivity was significantly decreased in epidermal basement membrane at 48 and 96 h after exposure to 0.8 Gy 1 GeV/nucleon iron ions. We confirmed this effect with two additional antibodies raised against affinity-purified laminin 1 and the E3 fragment of the long-arm of laminin 1. In contrast, collagen type IV, another component of the basement membrane, was unaffected. Our studies demonstrate quantitatively that densely ionizing radiation elicits changes in skin microenvironments distinct from those induced by sparsely ionizing radiation. Such effects may might contribute to the carcinogenic potential of densely ionizing radiation by altering cellular signaling cascades mediated by cell-extracellular matrix interactions.

Non-NASA Center↗

DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Imaging analysis of nuclear antiviral factors through direct detection of incoming adenovirus genome complexes

Recent studies involving several viral systems have highlighted the importance of cellular intrinsic defense mechanisms through nuclear antiviral proteins that restrict viral propagation. These factors include among others components of PML nuclear bodies, the nuclear DNA sensor IFI16, and a potential restriction factor PHF13/SPOC1. For several nuclear replicating DNA viruses, it was shown that these factors sense and target viral genomes immediately upon nuclear import. In contrast to the anticipated view, we recently found that incoming adenoviral genomes are not targeted by PML nuclear bodies. Here we further explored cellular responses against adenoviral infection by focusing on specific conditions as well as additional nuclear antiviral factors. In line with our previous findings, we show that neither interferon treatment nor the use of specific isoforms of PML nuclear body components results in co-localization between incoming adenoviral genomes and the subnuclear domains. Furthermore, our imaging analyses indicated that neither IFI16 nor PHF13/SPOC1 are likely to target incoming adenoviral genomes. Thus our findings suggest that incoming adenoviral genomes may be able to escape from a large repertoire of nuclear antiviral mechanisms, providing a rationale for the efficient initiation of lytic replication cycle. - Highlights: • Host nuclear antiviral factors were analyzed upon adenovirus genome delivery. • Interferon treatments fail to permit PML nuclear bodies to target adenoviral genomes. • Neither Sp100A nor B targets adenoviral genomes despite potentially opposite roles. • The nuclear DNA sensor IFI16 does not target incoming adenoviral genomes. • PHF13/SPOC1 targets neither incoming adenoviral genomes nor genome-bound protein VII.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1974

Photointerpretation of ERTS-A multispectral images analysis of vegetation and land use for the Valencia Lake Basin region

The area of investigation includes part of the Central and Capital Region of Venezuela. Climatic conditions define the existence of different ecological associations with biological forms well defined in their physiognomic complexity as they reach complete development in environments related to gross geomorphic units: coastal area, northern mountain range, fluvio-lacustrines plains, southern mountain range, southern ridges and parts of Venezuelan central plains. In this area, rainfall varies from 600 mm to 2000 mm and evapotranspiration indexes indicate the existence of vegetation for semiarid conditions ( tropical very dry forest) as well as vegetation for very humid conditions (very humid premontane forest). This project was designed for: (1) Location of areas with representative ecosystems for the purpose of managing planning. (2) Identification of structural and physiognomic characteristics of vegetation. (3) Assessment of human activity effects on environmental debasement. (4) Definition of the content and possibilities of orbital ERTS images for the study of urban and rural land use. (5) Setting photointerpretation patterns applicable to areas of similar gross environmental conditions.

Salas, F.↗

DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Remote image analysis for Mars Exploration Rover mobility and manipulation operations

NASA's Mars Exploration Rovers are two sixwheeled, 175-kg robotic vehicles which have operated on Mars for over a year as of March 2005. The rovers are controlled by teams who must understand the rover's surroundings and develop command sequences on a daily basis. The tight tactical planning timeline and everchanging environment call for tools that allow quick assessment of potential manipulator targets and traverse goals, since command sequences must be developed in a matter of hours after receipt of new data from the rovers. Reachability maps give a visual indication of which targets are reachable by each rover's manipulator, while slope and solar energy maps show the rover operator which terrain areas are safe and unsafe from different standpoints.

image understanding↗

DOE OSTI2018