Optimization of Hardware and Image Processing for Improved Image Quality in X-Ray Phase Contrast Imaging.
Abstract not provided.
Dagel, Amber Lynn↗
SEARCH · Engineering Papers
Results for “IMAGE PROCESSING”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Fission gas bubble identification using MATLAB's image processing toolbox
Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fission gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.
36 MATERIALS SCIENCE↗
The Dark Energy Survey Image Processing Pipeline
The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ~5000 deg 2 survey of the southern sky in five optical bands (g, r, i, z, Y) to a depth of ~24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over ~27 deg 2 . DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. In this paper, we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.
79 ASTRONOMY AND ASTROPHYSICS↗
Experimental study on flow characterization of a rectangular spouted bed by image processing
Systematic image processing methods are applied to characterize gas-solid flow under different operating conditions in a 30.2 mm x 101.6 mm rectangular spouted bed. Geldart D particles are imaged using a highspeed video camera. Superficial gas velocity, initial bed height, and nozzle size are investigated as influence factors. By applying adaptive thresholding and logical operation to the video frames, the original color images are transformed into binary images exposing detailed boundary information. By analyzing the binary image using a self-developed algorithm in combination with mask operations, the bed height is successfully determined, through which the variation of the bed expansion ratio with superficial gas velocity can be obtained. Additionally, the application of image morphological operations, i.e. dilation and erosion on the binarized images ensures the accurate identification of the contours of the jet and fountain so that the jet and fountain heights can be determined. Based on the image processing methods, the variation of bed expansion ratio, jet height and fountain height with the three influence factors are discussed. The current study highlights the application of image analysis in investigating the flow characterization and obtaining more hydrodynamic information in a rectangular spouted bed.
42 ENGINEERING↗
Galaxy–Galaxy Weak-lensing Measurements from SDSS. I. Image Processing and Lensing Signals
We present our image processing pipeline that corrects the systematics introduced by the point-spread function (PSF). Using this pipeline, we processed Sloan Digital Sky Survey (SDSS) DR7 imaging data in r band and generated a galaxy catalog containing the shape information. Based on our shape measurements of the galaxy images from SDSS DR7, we extract the galaxy–galaxy (GG) lensing signals around foreground spectroscopic galaxies binned in different luminosities and stellar masses. We estimated the systematics, e.g., selection bias, PSF reconstruction bias, PSF dilution bias, shear responsivity bias, and noise rectification bias, which in total is between −9.1% and 20.8% at 2 σ levels. The overall GG lensing signals we measured are in good agreement with Mandelbaum et al. The reduced χ {sup 2} between the two measurements in different luminosity bins are from 0.43 to 0.83. Larger reduced χ {sup 2} from 0.60 to 1.87 are seen for different stellar mass bins, which is mainly caused by the different stellar mass estimator. The results in this paper with higher signal-to-noise ratio are due to the larger survey area than SDSS DR4, confirming that more luminous/massive galaxies bear stronger GG lensing signals. We divide the foreground galaxies into red/blue and star-forming/quenched subsamples and measure their GG lensing signals. We find that, at a specific stellar mass/luminosity, the red/quenched galaxies have stronger GG lensing signals than their counterparts, especially at large radii. These GG lensing signals can be used to probe the galaxy–halo mass relations and their environmental dependences in the halo occupation or conditional luminosity function framework.
79 ASTRONOMY AND ASTROPHYSICS↗
Process Image Analysis using Big Data, Machine Learning, and Computer Vision
Within the DOE complex, 3013 canisters are used to store Pu-bearing waste. Wall penetration due to corrosion, specifically stress corrosion cracking, is considered to be the most likely cause of failure over the 50-year lifetime of the canisters. This project has 2 objectives: The first is the development of machine learning algorithms to identify the presence of corrosion from a very large set of images generated by laser confocal microscope scanning of 3013 canister used to store Pu oxides. This portion of the LDRD constitutes image analysis of a metal surface for the presence of corrosion. The image processing algorithms developed for this project will provide a suitable basis for analysis of other types of corrosion data, which can be produced in vast quantities using modern devices. The second component of the LDRD consists of the development of machine learning algorithms that obtain molecular mechanics force-fields from ab-initio Density Functional Theory (DFT) calculations for corrosive attack by chlorides on 304L or 316L stainless steel. The goal of this latter component is to determine means for mitigating corrosion on a fundamental level, including coatings, welding methods, metal composition, etc. Force-field modeling is necessary for this endeavor because the incipience and progression of corrosion is the governed by molecular structures, grain boundaries, dislocations and surface structures represented by large numbers of atoms. Although DFT calculations are extremely adept at describing molecular scale processes, they are computationally expensive making them ill-suited for calculations of more than several hundred atoms, especially for the repeated applications inherent in material design. Fortunately, force-field methods provide an avenue for viable molecular-scale calculations involving the numbers of atoms involved in corrosion processes. The accuracy of the force-field calculations depends strongly on the accuracy of the force-field model, which is extremely difficult and time-consuming to derive from either data or ab-initio calculations. The use of machine learning algorithms has the potential to make the calculation of force-fields much more efficient but has not been explored significantly for corrosion processes. If shown to be viable, the technique would have wide -ranging impact on design of corrosion resistant materials and on the mitigation of corrosion in existing process systems.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A proximity‐based image‐processing algorithm for colloid assignment in segmented multiphase flow datasets
Summary Colloidal transport and deposition are of both environmental and engineering importance. Easier access to x‐ray microtomography (XMT) coupled with improved imaging resolution has made XMT a unique and viable tool for visualizing and quantifying these processes. Currently, there is scant information in the literature addressing colloid segmentation and analysis in saturated and unsaturated porous media, in particular related to spatial partitioning of colloids. To support this need, an approach to assign segmented colloidal particles and aggregates to different partitioning classes based on their proximity to different phases is presented here. The method uses different markers for each attachment site (e.g. wetting‐nonwetting phase interfaces). An example XMT dataset from a drainage experiment is used to demonstrate the efficacy of the image processing algorithms. Flow conditions, and fluid and colloid properties, can thus be compared to the behaviour of colloids within the porous medium. This algorithm can help elucidate colloidal deposition mechanisms and the importance of different attachment sites, explore the importance of fluid properties, as well as the arrangement and shape of the colloids.
BRUECK, C. L.↗
Satellite Imaging: Processing & Analysis of Images from Low-Earth Orbit.
Abstract not provided.
Castle, Bryce↗
Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time
The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.
79 ASTRONOMY AND ASTROPHYSICS↗
SU-G-IeP3-09: Investigating the Interplay of Antiscatter Grids with Modern Detectors and Image Processing in Digital Radiography
Purpose: To use phantom and simulation experiments to relate technique factors, patient size and antiscatter grid use to image quality in portable digital radiography (DR), in light of advancements in detector design and image processing. Methods: Image contrast-to-noise ratio (CNR) on a portable DR system (MobileDaRt Evolution, Shimadzu) was measured by imaging four aluminum inserts of varying thickness, superimposed on a Lucite slab phantom using a pediatric abdominal protocol. Three thicknesses of Lucite were used: 6.1cm, 12cm, and 18.2cm, with both 55 and 65 kVp beams. The mAs was set so that detector entrance exposure (DEE) was matched between kVp values. Each technique and phantom was used with and without an antiscatter grid (focused linear grid embedded in aluminum with an 8:1 ratio). The CNR-improvement-factor was then used to determine the thickness- and technique-dependent appropriateness of grid use. Finally, the same experiment was performed via Monte Carlo simulation, integrating incident energy fluence at each detector pixel, so that effects of detector design and image processing could be isolated from physical factors upstream of the detector. Results: The physical phantom experiment demonstrated a clear improvement for the lower tube voltage (55kVp), along with substantial CNR benefits with grid use for 12–18cm phantoms. Neither trend was evident with Monte Carlo, suggesting that suboptimal quantum-detection-efficiency and automated grid-removal could explain trends in kVp and grid use, respectively. Conclusion: Physical experiments demonstrate marked improvement in CNR when using a grid for phantoms of 12 and 18cm Lucite thickness (above ∼10cm soft-tissue equivalent). This benefit is likely due to image processing, as this result was not seen with Monte Carlo. The impact of image processing on image resolution should also be investigated, and the CNR benefit of low kVp and grid use should be weighed against the increased exposure time necessary to achieve adequate DEE.
60 APPLIED LIFE SCIENCES↗
Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images
Phase transformations in materials systems can be tracked using atomic force microscopy (AFM), enabling the examination of surface properties and macroscale morphologies. In situ measurements investigating phase transformations generate large datasets of time-lapse image sequences. The interpretation of the resulting image sequences, guided by domain-knowledge, requires manual image processing using handcrafted masks. Here this approach is time-consuming and restricts the number of images that can be processed. Her in this study, we developed an automated image processing pipeline which integrates image detection and segmentation methods. We examine five time-series AFM videos of various fluoroelastomer phase transformations. The number of image sequences per video ranges from a hundred to a thousand image sequences. The resulting image processing pipeline aims to automatically classify and analyze images to enable batch processing. Using this pipeline, the growth of each individual fluoroelastomer crystallite can be tracked through time. We incorporated statistical analysis into the pipeline to investigate trends in phase transformations between different fluoroelastomer batches. Understanding these phase transformations is crucial, as it can provide valuable insights into manufacturing processes, improve product quality, and possibly lead to the development of more advanced fluoroelastomer formulations.
36 MATERIALS SCIENCE↗
High-Throughput Computing: Case Study of Medical Image Processing Applications
HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.
Predescu, Maria↗
Image Processing Occupancy Sensor
The Image Processing Occupancy Sensor, or IPOS, is a novel sensor technology developed at the National Renewable Energy Laboratory (NREL). The sensor is based on low-cost embedded microprocessors widely used by the smartphone industry and leverages mature open-source computer vision software libraries. Compared to traditional passive infrared and ultrasonic-based motion sensors currently used for occupancy detection, IPOS has shown the potential for improved accuracy and a richer set of feedback signals for occupant-optimized lighting, daylighting, temperature setback, ventilation control, and other occupancy and location-based uses. Unlike traditional passive infrared (PIR) or ultrasonic occupancy sensors, which infer occupancy based only on motion, IPOS uses digital image-based analysis to detect and classify various aspects of occupancy, including the presence of occupants regardless of motion, their number, location, and activity levels of occupants, as well as the illuminance properties of the monitored space. The IPOS software leverages the recent availability of low-cost embedded computing platforms, computer vision software libraries, and camera elements.
Source record↗
TU-FG-209-11: Validation of a Channelized Hotelling Observer to Optimize Chest Radiography Image Processing for Nodule Detection: A Human Observer Study
Purpose: To validate the use of a Channelized Hotelling Observer (CHO) model for guiding image processing parameter selection and enable improved nodule detection in digital chest radiography. Methods: In a previous study, an anthropomorphic chest phantom was imaged with and without PMMA simulated nodules using a GE Discovery XR656 digital radiography system. The impact of image processing parameters was then explored using a CHO with 10 Laguerre-Gauss channels. In this work, we validate the CHO’s trend in nodule detectability as a function of two processing parameters by conducting a signal-known-exactly, multi-reader-multi-case (MRMC) ROC observer study. Five naive readers scored confidence of nodule visualization in 384 images with 50% nodule prevalence. The image backgrounds were regions-of-interest extracted from 6 normal patient scans, and the digitally inserted simulated nodules were obtained from phantom data in previous work. Each patient image was processed with both a near-optimal and a worst-case parameter combination, as determined by the CHO for nodule detection. The same 192 ROIs were used for each image processing method, with 32 randomly selected lung ROIs per patient image. Finally, the MRMC data was analyzed using the freely available iMRMC software of Gallas et al. Results: The image processing parameters which were optimized for the CHO led to a statistically significant improvement (p=0.049) in human observer AUC from 0.78 to 0.86, relative to the image processing implementation which produced the lowest CHO performance. Conclusion: Differences in user-selectable image processing methods on a commercially available digital radiography system were shown to have a marked impact on performance of human observers in the task of lung nodule detection. Further, the effect of processing on humans was similar to the effect on CHO performance. Future work will expand this study to include a wider range of detection/classification tasks and more observers, including experienced chest radiologists.
60 APPLIED LIFE SCIENCES↗
SU-D-209-03: Radiation Dose Reduction Using Real-Time Image Processing in Interventional Radiology
Purpose: To characterize changes in radiation dose after introducing a new real-time image processing technology in interventional radiology systems. Methods: Interventional radiology (IR) procedures are increasingly complex, at times requiring substantial time and radiation dose. The risk of inducing tissue reactions as well as long-term stochastic effects such as radiation-induced cancer is not trivial. To reduce this risk, IR systems are increasingly equipped with dose reduction technologies.Recently, ClarityIQ (Philips Healthcare) technology was installed in our existing neuroradiology IR (NIR) and vascular IR (VIR) suites respectively. ClarityIQ includes real-time image processing that reduces noise/artifacts, enhances images, and sharpens edges while also reducing radiation dose rates. We reviewed 412 NIR (175 pre- and 237 post-ClarityIQ) procedures and 329 VIR (156 preand 173 post-ClarityIQ) procedures performed at our institution pre- and post-ClarityIQ implementation. NIR procedures were primarily classified as interventional or diagnostic. VIR procedures included drain port, drain placement, tube change, mesenteric, and implanted venous procedures. Air Kerma (AK in units of mGy) was documented for all the cases using a commercial radiation exposure management system. Results: When considering all NIR procedures, median AK decreased from 1194 mGy to 561 mGy. When considering all VIR procedures, median AK decreased from 49 to 14 mGy. Both NIR and VIR exhibited a decrease in AK exceeding 50% after ClarityIQ implementation, a statistically significant (p<0.05) difference. Of the 5 most common VIR procedures, all median AK values decreased, but significance (p<0.05) was only reached in venous access (N=53), angio mesenteric (N=41), and drain placement procedures (N=31). Conclusion: ClarityIQ can reduce dose significantly for both NIR and VIR procedures. Image quality was not assessed in conjunction with the dose reduction.
60 APPLIED LIFE SCIENCES↗
Image processing analysis of nuclear track parameters for CR-39 detector irradiated by thermal neutron
CR-39 detector which covered with boric acid (H{sub 3}Bo{sub 3}) pellet was irradiated by thermal neutrons from ({sup 241}Am - {sup 9}Be) source with activity 12Ci and neutron flux 10{sup 5} n. cm{sup −2}. s{sup −1}. The irradiation times -T{sub D} for detector were 4h, 8h, 16h and 24h. Chemical etching solution for detector was sodium hydroxide NaOH, 6.25N with 45 min etching time and 60 C° temperature. Images of CR-39 detector after chemical etching were taken from digital camera which connected from optical microscope. MATLAB software version 7.0 was used to image processing. The outputs of image processing of MATLAB software were analyzed and found the following relationships: (a) The irradiation time -T{sub D} has behavior linear relationships with following nuclear track parameters: i) total track number - N{sub T} ii) maximum track number - MRD (relative to track diameter - D{sub T}) at response region range 2.5 µm to 4 µm iii) maximum track number - M{sub D} (without depending on track diameter - D{sub T}). (b) The irradiation time -T{sub D} has behavior logarithmic relationship with maximum track number - M{sub A} (without depending on track area - A{sub T}). The image processing technique principally track diameter - D{sub T} can be take into account to classification of α-particle emitters, In addition to the contribution of these technique in preparation of nano- filters and nano-membrane in nanotechnology fields.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Measurement and image processing evaluation of surface modifications of dental implants G4 pure titanium created by different techniques
Foreign substances and organic tissue interaction placed into the jaw in order to eliminate tooth loss involves a highly complex process. Many biological reactions take place as well as the biomechanical forces that influence this formation. Osseointegration denotes to the direct structural and functional association between the living bone and the load-bearing artificial implant's surface. Taking into consideration of the requirements in the manufacturing processes of the implants, surface characterizations with high precise measurement techniques are investigated and thus long-term success of dental implant is emphasized on the importance of these processes in this study. In this research, the detailed surface characterization was performed to identify the dependence of the manufacturing techniques on the surface properties by using the image processing methods and using the scanning electron microscope (SEM) for morphological properties in 3D and Taylor Hobson stylus profilometer for roughness properties in 2D. Three implant surfaces fabricated by different manufacturing techniques were inspected, and a machined surface was included into the study as a reference specimen. The results indicated that different surface treatments were strongly influenced surface morphology. Thus 2D and 3D precise inspection techniques were highlighted on the importance for surface characterization. Different image analyses techniques such as Dark-light technique were used to verify the surface measurement results. The computational phase was performed using image processing toolbox in Matlab with precise evaluation of the roughness for the implant surfaces. The relationship between the number of black and white pixels and surface roughness is presented. FFT image processing and analyses results explicitly imply that the technique is useful in the determination of surface roughness. The results showed that the number of black pixels in the image increases with increase in surface roughness.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
WE-G-204-01: BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography
Purpose: To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography. Methods: An anthropomorphic chest phantom was imaged in the posterior-anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x-ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE’s default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, which were the two user-configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest-only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise-free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal-known-exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre-Gauss channel functions. Results: The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO-estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = −0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance. Conclusion: Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.
60 APPLIED LIFE SCIENCES↗