Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quality control of image sensors using gaseous tritium light sources.

ABSTRACT: We propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (betalights). Along with the gain (conversion factor) and read noise level, the predictable photon flux of the source allows us to gauge the quantum efficiency. The design is easily reproducible with a 3D printer (three-dimensional printer) and three inexpensive parts. Suitable for common image sensors, we believe that the method has the potential to be a useful tool in microscopy facilities and optical laboratories alike. This article is part of the theme issue 'Super-resolution structured illumination microscopy (part 2)'.

SUBMITTER: McFadden D

PROVIDER: S-EPMC7613193 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Quality control of image sensors using gaseous tritium light sources.

McFadden David D Amos Brad B Heintzmann Rainer R

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20220214 2220

We propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<i>betalights</i>). Along with the gain (conversion factor) and read noise level, the predictable photon flux of the source allows us to gauge the quantum efficiency. The design is easily reproducible with a 3D printer (three-dimensional printer) and three inexpensive parts. Suitable for common image sensors, we believe that the method has the potential to be a useful tool ...[more]

PMID: 35152762

Similar Datasets

Using Gas-Phase Air Quality Sensors to Disentangle Potential Sources in a Los Angeles Neighborhood.

Project description:In the late summer of 2016, our team deployed a network of low-cost air quality sensing systems in partnership with community-based organizations in a neighborhood in South Los Angeles, California. Residents of this community were concerned about possible emissions from local oil and gas activity, however in addition to these potential emissions, the neighborhood is also subject to a complex mixture of pollutants from other nearby sources including major highways. For this deployment, metal-oxide VOC sensors were quantified to provide methane (CH4) and total non-methane hydrocarbon (TNMHCs) concentration estimates. This data along with other sensor signals, meteorological data, and community member observations was used to examine the composition and possible origins of observed emissions. The sensor network displayed expected environmental trends and highlighted short-term elevations in CH4 and/or TNMHCs, which we were then able to investigate more closely. The results indicated that sources of both combusted and volatilized hydrocarbons were likely affecting air quality throughout the community, including near the site of the local oil and gas activity. This deployment may serve as a model for how multi-sensor systems deployed in networks can be leveraged to better understand sources in complex areas, potentially supporting future community-based air quality research.

| S-EPMC8248942 | biostudies-literature

A comparative study on image quality of two digital intraoral sensors.

Project description:OBJECTIVES:The aims of this study were to evaluate the subjective image quality and reliability of two digital sensors. In addition, the image quality of the two sensors evaluated by specialists and general dentists were compared. METHODS:30 intraoral bitewings from five patients were included in the study, 15 were exposed with a Dixi sensor (CCD-based) and 15 with a ProSensor (CMOS-based) using modified parallel technique. Three radiologists and three general dentists evaluated the images in pair. A five-point scale was used to register the image quality. Visual grading characteristics (VGC) analysis was performed to compare the image quality and the observer agreement was assessed in terms of intra class correlation co-efficient. RESULTS:No statistically significant difference was found on image quality between the sensors. The average scores of the observer agreement were moderate with an average of 0.66 and an interval of 0.30 to 0.87, suggesting that there was a large variation on preference of image quality. However, there was a statistically significant difference in terms of the area under the VGC- curves between the specialist group and the general dentist group ( p = 0.043), in which the specialist group tended to favor the ProSensor. CONCLUSIONS:Subjective image quality of the two intraoral sensors were comparable when evaluated by both general and oral radiologists. However, the radiologists seemed to prefer the ProSensor to the Dixi as compared to general dentists. Inter- observer conformance showed a large variation on the preference of the image quality.

| S-EPMC6775787 | biostudies-literature

Turning Image Sensors into Position and Time Sensitive Quantitative Colorimetric Data Sources with the Aid of Novel Image Processing/Analysis Software.

Project description:Still images and video images acquired from image sensors are very valuable sources of information. From still images, position-sensitive, quantitative intensity, or colorimetric information can be obtained. Video images made of a time series of still images can provide time-dependent, quantitative intensity, or colorimetric information in addition to the position-sensitive information from a single still image. With the aid of novel image processing/analysis software, extraction of position- and time-sensitive quantitative colorimetric information was demonstrated from still image and video images of litmus test strips for pH tests of solutions. Visual inspection of the color change in the litmus test strips after chemical reaction with chemical solutions is typically exercised. Visual comparison of the color of the test solution with a standard color chart provides an approximate pH value to the nearest whole number. Accurate colorimetric quantification and dynamic analysis of chemical properties from captured still images and recorded video images of test solutions using novel image processing/analysis software are proposed with experimental feasibility study results towards value-added image sensor applications. Position- and time-sensitive quantitative colorimetric measurements and analysis examples are demonstrated.

| S-EPMC7696555 | biostudies-literature

Control of laser plasma accelerated electrons for light sources.

Project description:With gigaelectron-volts per centimetre energy gains and femtosecond electron beams, laser wakefield acceleration (LWFA) is a promising candidate for applications, such as ultrafast electron diffraction, multistaged colliders and radiation sources (betatron, compton, undulator, free electron laser). However, for some of these applications, the beam performance, for example, energy spread, divergence and shot-to-shot fluctuations, need a drastic improvement. Here, we show that, using a dedicated transport line, we can mitigate these initial weaknesses. We demonstrate that we can manipulate the beam longitudinal and transverse phase-space of the presently available LWFA beams. Indeed, we separately correct orbit mis-steerings and minimise dispersion thanks to specially designed variable strength quadrupoles, and select the useful energy range passing through a slit in a magnetic chicane. Therefore, this matched electron beam leads to the successful observation of undulator synchrotron radiation after an 8 m transport path. These results pave the way to applications demanding in terms of beam quality.

| S-EPMC5889396 | biostudies-literature

Giga-pixel lensfree holographic microscopy and tomography using color image sensors.

Project description:We report Giga-pixel lensfree holographic microscopy and tomography using color sensor-arrays such as CMOS imagers that exhibit Bayer color filter patterns. Without physically removing these color filters coated on the sensor chip, we synthesize pixel super-resolved lensfree holograms, which are then reconstructed to achieve ~350 nm lateral resolution, corresponding to a numerical aperture of ~0.8, across a field-of-view of ~20.5 mm(2). This constitutes a digital image with ~0.7 Billion effective pixels in both amplitude and phase channels (i.e., ~1.4 Giga-pixels total). Furthermore, by changing the illumination angle (e.g., ± 50°) and scanning a partially-coherent light source across two orthogonal axes, super-resolved images of the same specimen from different viewing angles are created, which are then digitally combined to synthesize tomographic images of the object. Using this dual-axis lensfree tomographic imager running on a color sensor-chip, we achieve a 3D spatial resolution of ~0.35 µm × 0.35 µm × ~2 µm, in x, y and z, respectively, creating an effective voxel size of ~0.03 µm(3) across a sample volume of ~5 mm(3), which is equivalent to >150 Billion voxels. We demonstrate the proof-of-concept of this lensfree optical tomographic microscopy platform on a color CMOS image sensor by creating tomograms of micro-particles as well as a wild-type C. elegans nematode.

| S-EPMC3440383 | biostudies-literature

Inferring atmospheric sources of gaseous organophosphate esters from spatial patterns.

Project description:Organophosphate esters (OPEs) have emerged as pervasive environmental contaminants, with concentrations often exceeding those of traditional flame retardants and plasticizers by orders of magnitude. Here, we present concentrations of OPEs in the atmospheric gas phase collected using passive air samplers deployed in the coastal regions of Quebec and British Columbia in southern Canada. Four OPEs, i.e., tri-n-butyl phosphate (TBP), tris(2-chloroethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate (TCPP), and tris (phenyl) phosphate (TPhP) were reliably and ubiquitously detected, with TCPP showing the highest level, followed by TBP. Concentration levels of TCPP and TCEP are correlated with each other and with population, possibly indicating emission from consumer products. Spatial patterns of TBP and TPhP are more indicative of industrial usage, with airports possibly being a major source for TBP. The positive relationships between atmospheric OPEs and population are influenced by ambient temperature, whereby the size of the populated area around a sampling site influencing the air concentration appears to be decreasing at higher temperatures.

| S-EPMC11864208 | biostudies-literature

A Rotational Cylindrical fMRI Phantom for Image Quality Control.

Project description:PurposeA novel phantom for image quality testing for functional magnetic resonance imaging (fMRI) scans is described.MethodsThe cylindrical, rotatable, ~4.5L phantom, with eight wedge-shaped compartments, is used to simulate rest and activated states. The compartments contain NiCl2 doped agar gel with alternating concentrations of agar (1.4%, 1.6%) to produce T1 and T2 values approximating brain grey matter. The Jacard index was used to compare the image distortions for echo planar imaging (EPI) and gradient recalled echo (GRE) scans. Contrast to noise ratio (CNR) was compared across the imaging volume for GRE and EPI.ResultsThe mean T2 for the two agar concentrations were found to be 106.5±4.8, 94.5±4.7 ms, and T1 of 1500±40 and 1485±30 ms, respectively. The Jacard index for GRE was generally found to be higher than for EPI (0.95 versus 0.8). The CNR varied from 20 to 50 across the slices and echo times used for EPI scans, and from 20 to 40 across the slices for the GRE scans. The phantom provided a reproducible CNR over 25 days.ConclusionsThe phantom provides a quantifiable signal change over a head-size imaging volume with EPI and GRE sequences, which was used for image quality assessment.

| S-EPMC4666484 | biostudies-literature

Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks.

Project description:Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with "good" and "bad" morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.

| S-EPMC9820636 | biostudies-literature

Using Light Quality for Growth Control of Cucumber Seedlings in Closed-Type Plant Production System.

Project description:During seedling production, growth control of seedlings is an important problem because the overgrowth of seedlings causes a decrease of seedling quality and has disadvantages after transplanting. In this study, we aim to evaluate the possibility of replacing chemical plant growth regulators using light quality in a closed-type plant production system (CPPS) for cucumber seedling production. We used various light treatments, such as monochromatic or combined red (R) and blue (B), and combined R and B with UV-A or Far-red (Fr) light, to compare with a chemical plant growth regulator conventionally using in nursery farms. The combined R and B treatment decreased stem elongation and increased dry matter and compactness. UV-A treatment increased compactness but did not significantly affect the stem elongation or dry matter. Fr increased stem elongation and stem diameter and decreased compactness and dry matter. In leaf growth, combined R and B treatments and UV-A treatments increased leaf area, specific leaf weight, and SPAD value, and decreased leaf shape index. Fr treatments increased leaf area and leaf shape index and decreased specific leaf weight (SLW) and SPAD values. Cucumber seedlings have many different morphological changes, and R5B5 light quality was more effective in growth control due to higher compactness than chemical plant growth regulators. Also, R5B5 light quality has increased seedling quality, such as dry matter and SLW compared with fluorescent lamps. Thus, the use of light quality is a possible alternative to a chemical plant growth regulator.

| S-EPMC7284851 | biostudies-literature

Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.

Project description:Sensor-based food intake monitoring has become one of the fastest-growing fields in dietary assessment. Researchers are exploring imaging-sensor-based food detection, food recognition, and food portion size estimation. A major problem that is still being tackled in this field is the segmentation of regions of food when multiple food items are present, mainly when similar-looking foods (similar in color and/or texture) are present. Food image segmentation is a relatively under-explored area compared with other fields. This paper proposes a novel approach to food imaging consisting of two imaging sensors: color (Red-Green-Blue) and thermal. Furthermore, we propose a multi-modal four-Dimensional (RGB-T) image segmentation using a k-means clustering algorithm to segment regions of similar-looking food items in multiple combinations of hot, cold, and warm (at room temperature) foods. Six food combinations of two food items each were used to capture RGB and thermal image data. RGB and thermal data were superimposed to form a combined RGB-T image and three sets of data (RGB, thermal, and RGB-T) were tested. A bootstrapped optimization of within-cluster sum of squares (WSS) was employed to determine the optimal number of clusters for each case. The combined RGB-T data achieved better results compared with RGB and thermal data, used individually. The mean ± standard deviation (std. dev.) of the F1 score for RGB-T data was 0.87 ± 0.1 compared with 0.66 ± 0.13 and 0.64 ± 0.39, for RGB and Thermal data, respectively.

| S-EPMC9860575 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data