Results for “Image Processing” — Page 57

DOE OSTI2017

Statistical Study of Solar Dimmings Using CoDiT

We present the results from analyzing the physical and morphological properties of 154 dimmings (transient coronal holes) and the associated flares and coronal mass ejections (CMEs). Each dimming in our 2013 catalog was processed with the semi-automated Coronal Dimming Tracker using Solar Dynamics Observatory AIA 193 Å observations and HMI magnetograms. Instead of the typically used difference images, we used our coronal hole detection algorithm to detect transient dark regions “directly” in extreme ultraviolet (EUV) images. This allowed us to study dimmings as the footpoints of CMEs—in contrast with the larger, diffuse dimmings seen in difference images that represent the projected view of the rising, expanding plasma. Studying the footpoint-dimming morphology allowed us to better understand the CME structure in the low corona. While comparing the physical properties of dimmings, flares, and CMEs we were also able to identify relationships between the different parts of this complex eruptive phenomenon. We found that larger dimmings are longer-lived, suggesting that it takes longer to “close down” large open magnetic regions. Also, during their growth phase, smaller dimmings acquire a higher magnetic flux imbalance (i. e., become more unipolar) than larger dimmings. Furthermore, we found that the EUV intensity of dimmings (indicative of local electron density) correlates with how much plasma was removed and how energetic the eruption was. Studying the morphology of dimmings (single, double, fragmented) also helped us identify different configurations of the quasi-open magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

HERMES

HERMES - High-speed Event Retrieval and Management for Enhanced Spectral imaging code. This code is meant to unpack and process neutron imaging data from the TPX3Cam made by Amsterdam Scientific Instruments. The TPX3Cam utilizes the Timepix3 chip in a single photon counting mode of image acquisition. The photon counting data streaming off the TPX3Cam needs to be process and analyzed in order to create final images. Therefore we are developing both python and cpp codes to allow for users of the TPX3Cam to efficiently analyze data and create images.

Long, Alexander↗

DOE OSTI2017

Machine learning to analyze images of shocked materials for precise and accurate measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

A minimum spanning forest based classification method for dedicated breast CT images

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WISE PHOTOMETRY FOR 400 MILLION SDSS SOURCES

We present photometry of images from the Wide-Field Infrared Survey Explorer (WISE) of over 400 million sources detected by the Sloan Digital Sky Survey (SDSS). We use a “forced photometry” technique, using measured SDSS source positions, star–galaxy classification, and galaxy profiles to define the sources whose fluxes are to be measured in the WISE images. We perform photometry with The Tractor image modeling code, working on our “unWISE” coaddds and taking account of the WISE point-spread function and a noise model. The result is a measurement of the flux of each SDSS source in each WISE band. Many sources have little flux in the WISE bands, so often the measurements we report are consistent with zero given our uncertainties. However, for many sources we get 3σ or 4σ measurements; these sources would not be reported by the “official” WISE pipeline and will not appear in the WISE catalog, yet they can be highly informative for some scientific questions. In addition, these small-signal measurements can be used in stacking analyses at the catalog level. The forced photometry approach has the advantage that we measure a consistent set of sources between SDSS and WISE, taking advantage of the resolution and depth of the SDSS images to interpret the WISE images; objects that are resolved in SDSS but blended together in WISE still have accurate measurements in our photometry. Our results, and the code used to produce them, are publicly available at http://unwise.me.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

IOTA : integration optimization, triage and analysis tool for the processing of XFEL diffraction images

Serial femtosecond crystallography (SFX) uses an X-ray free-electron laser to extract diffraction data from crystals not amenable to conventional X-ray light sources owing to their small size or radiation sensitivity. However, a limitation of SFX is the high variability of the diffraction images that are obtained. As a result, it is often difficult to determine optimal indexing and integration parameters for the individual diffraction images. Presented here is a software package, called IOTA , which uses a grid-search technique to determine optimal spot-finding parameters that can in turn affect the success of indexing and the quality of integration on an image-by-image basis. Integration results can be filtered using a priori information about the Bravais lattice and unit-cell dimensions and analyzed for unit-cell isomorphism, facilitating an improvement in subsequent data-processing steps.

Chemistry↗

DOE OSTI2015

Search systems and computer-implemented search methods

Search systems and computer-implemented search methods are described. In one aspect, a search system includes a communications interface configured to access a plurality of data items of a collection, wherein the data items include a plurality of image objects individually comprising image data utilized to generate an image of the respective data item. The search system may include processing circuitry coupled with the communications interface and configured to process the image data of the data items of the collection to identify a plurality of image content facets which are indicative of image content contained within the images and to associate the image objects with the image content facets and a display coupled with the processing circuitry and configured to depict the image objects associated with the image content facets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Search systems and computer-implemented search methods

Search systems and computer-implemented search methods are described. In one aspect, a search system includes a communications interface configured to access a plurality of data items of a collection, wherein the data items include a plurality of image objects individually comprising image data utilized to generate an image of the respective data item. The search system may include processing circuitry coupled with the communications interface and configured to process the image data of the data items of the collection to identify a plurality of image content facets which are indicative of image content contained within the images and to associate the image objects with the image content facets and a display coupled with the processing circuitry and configured to depict the image objects associated with the image content facets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

SU-C-204-06: Surface Imaging for the Set-Up of Proton Post-Mastectomy Chestwall Irradiation: Gated Images Vs Non Gated Images

Purpose: Historically, the set-up for proton post-mastectomy chestwall irradiation at our institution started with positioning the patient using tattoos and lasers. One or more rounds of orthogonal X-rays at gantry 0° and beamline X-ray at treatment gantry angle were then taken to finalize the set-up position. As chestwall targets are shallow and superficial, surface imaging is a promising tool for set-up and needs to be investigated Methods: The orthogonal imaging was entirely replaced by AlignRT™ (ART) images. The beamline X-Ray image is kept as a confirmation, based primarily on three opaque markers placed on skin surface instead of bony anatomy. In the first phase of the process, ART gated images were used to set-up the patient and the same specific point of the breathing curve was used every day. The moves (translations and rotations) computed for each point of the breathing curve during the first five fractions were analyzed for ten patients. During a second phase of the study, ART gated images were replaced by ART non-gated images combined with real-time monitoring. In both cases, ART images were acquired just before treatment to access the patient position compare to the non-gated CT. Results: The average difference between the maximum move and the minimum move depending on the chosen breathing curve point was less than 1.7 mm for all translations and less than 0.7° for all rotations. The average position discrepancy over the course of treatment obtained by ART non gated images combined to real-time monitoring taken before treatment to the planning CT were smaller than the average position discrepancy obtained using ART gated images. The X-Ray validation images show similar results with both ART imaging process. Conclusion: The use of ART non gated images combined with real time imaging allows positioning post-mastectomy chestwall patients in less than 3 mm / 1°.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Integrated multi-wavelength microscope combining TIRFM and IRM modalities for imaging cellulases and other processive enzymes

We describe a multimodal microscope for visualizing processive enzymes moving on immobilized substrates. The instrument combines interference reflection microscopy (IRM) with multi-wavelength total internal reflectance fluorescence microscopy (TIRFM). The microscope can localize quantum dots with a precision of 2.8 nm at 100 frames/s, and was used to image the dynamics of the cellulase, Cel7a interacting with surface-immobilized cellulose. The instrument, which was built with off-the-shelf components and is controlled by custom software, is suitable for tracking other degradative enzymes such as collagenases, as well as motor proteins moving along immobilized tracks.

09 BIOMASS FUELS↗

DOE OSTI2016

Online coupled camera pose estimation and dense reconstruction from video

A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Microstructural Characterization of Irradiated Fuels and Materials for the United States High Performance Research Reactor Fuel Development Program and the Fuel Cycle Research and Development Program

In an effort to contribute to the development of advanced nuclear fuel for research reactors, monolithic and dispersion plate fuels will be examined to determine how irradiation parameters such as fission density, fission rate, and irradiation temperature, as well as mechanical parameters such as composition and fabrication, affect the final microstructure of the fuel, post irradiation. Electron Backscatter Detection (EBSD) analysis capabilities of irradiated materials will be developed as well as improvements on irradiated sample preparation in order to provide higher quality samples for SEM, FIB, and TEM. In addition, image analysis techniques developed at Idaho National Laboratory (INL) will be employed to examine microstructural features such as fission product precipitates and fission gas bubbles. The main goals are to further develop EBSD capabilities on irradiated material; characterize microstructure of irradiated fuel to support qualification; develop a consistent, effective process for image analysis using INL developed software; and provide repeatable, statistically-confident, critically assessed data to further modeling efforts. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Lidar Mapping and Surveillance of Nuclear Infrastructure

Wear is unavoidable, whether it occurs in a nuclear facility or abridge on the highway its detection is crucial to preventing the failure of a system. While tools capable of detecting localized damage exist, industry lacks methods to evaluate the health of larger infrastructure. Routine inspection of nuclear facilities must be undertaken to ensure the safety of personnel and proper containment of nuclear materials. Most systems in a nuclear facility are designated as radioactive and confined spaces. To constantly monitor such systems in person would be hazardous but with a remote inspection tool it can be done in a safer and easier way. Lidar: Operates by emitting a laser pulse and looking for the reflected light. Allows user to gain 3D information of the environment. Project Objective: To develop an inspection tool for evaluating the health of large scale infrastructure using Lidar and machine learning. Autonomous Platform + Lidar + Data Fusion + Big Data. Use SLAM to build a 2D map of the environment. Point cloud created by transforming the points between reference frames. Point cloud to Surface: Inherent representation of surface geometry; Difficult to process with conventional techniques. Point cloud to Image: Easily processed by conventional machine learning algorithms. The quantity of data generated will necessitate a big data framework to manage the data for processing. Additional Sensors: Detecting the temperature (a) chemical makeup (b) and radiation emission (c) of a surface could assist in detecting problems. Human inspection can detect irregularities in both physical objects and their point-cloud representations. This is a good indication that mapping nuclear sites using Lidar will provide qualitative insight into the health of the facility. If deep learning can be used to automatically identify problem areas then the completed tool could become an invaluable asset.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗

DOE OSTI2021

Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

42 ENGINEERING↗

DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗

DOE OSTI2015

WE-D-213-04: Preparing for Parts 2 & 3 of the ABR Nuclear Medicine Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-D-213-00: Preparing for the ABR Diagnostic and Nuclear Medicine Physics Exams

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗