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Results for “segmental refinement”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Segmental Refinement: A Multigrid Technique for Data Locality

In this paper, we investigate a domain decomposed multigrid technique, termed segmental refinement, for solving general nonlinear elliptic boundary value problems. We extend the method first proposed in 1994 by analytically and experimentally investigating its complexity. We confirm that communication of traditional parallel multigrid is eliminated on fine grids, with modest amounts of extra work and storage, while maintaining the asymptotic exactness of full multigrid. We observe an accuracy dependence on the segmental refinement subdomain size, which was not considered in the original analysis. Finally, we present a communication complexity analysis that quantifies the communication costs ameliorated by segmental refinement and report performance results with up to 64K cores on a Cray XC30.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Refined Global Segmentation of X-Ray CT Images for Multi-phase Geomaterials

X-ray computed tomography images of three-phase silica sand and glass bead specimens are analyzed and used to evaluate the segmentation performances of Otsu-, and recursion-based multilevel algorithms. A global image segmentation technique that combines iterative and recursive algorithms, namely a refined statistics-based global segmentation is proposed for segmenting multi-phase granular geomaterials. The performance of the proposed algorithm is tested by segmenting partially saturated silica sand and glass bead specimens. For the tested silica sand specimen, the refined statistics method estimated void ratio and degree of saturation were 0.67 and 39.35%. The estimates for the glass bead specimen yielded 0.64 and 43.49%, respectively. The true void ratio (0.66) and degree of saturation (37.71%) were determined with a user-controlled Image processing software package—Image-Pro. It was found that the proposed method estimated the void ratio and the degree of saturation with 1.52 and 4.35 percent errors for the silica sand and with 15.63 and 0.34 percent errors for the glass bead, respectively. The computational time of the proposed method was found to be shorter than other methods considered. Overall, it is concluded that the proposed technique performed better in segmenting three-phase granular geomaterials.

42 ENGINEERING↗
NASA NTRS1992

Refining image segmentation by integration of edge and region data

An iterative parallel region growing (IPRG) algorithm previously developed by Tilton (1989) produces hierarchical segmentations of images from finer to coarser resolution. An ideal segmentation does not always correspond to one single iteration but to several different ones, each one producing the 'best' result for a separate part of the image. With the goal of finding this ideal segmentation, the results of the IPRG algorithm are refined by utilizing some additional information, such as edge features, and by interpreting the tree of hierarchical regions.

Le Moigne, Jacqueline↗
NASA NTRS1987

Refining image segmentation by polygon skeletonization

A skeletonization algorithm was encoded and applied to a test data set of land-use polygons taken from a USGS digital land use dataset at 1:250,000. The distance transform produced by this method was instrumental in the description of the shape, size, and level of generalization of the outlines of the polygons. A comparison of the topology of skeletons for forested wetlands and lakes indicated that some distinction based solely upon the shape properties of the areas is possible, and may be of use in an intelligent automated land cover classification system.

Clarke, Keith C.↗
DOE OSTI2022

Uncertainty-refined image segmentation under domain shift

A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. The label of any image element with an uncertainty value above a predefined threshold is replaced with a new label corresponding to the class with the next highest value.

Martinez, Carianne↗
DOE OSTI2024

Uncertainty-refined image segmentation under domain shift

Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Martinez, Carianne↗
NASA NTRS1990

Image segmentation by background extraction refinements

An image segmentation method refining background extraction in two phases is presented. In the first phase, the method detects homogeneous-background blocks and estimates the local background to be extracted throughout the image. A block is classified homogeneous if its left and right standard deviations are small. The second phase of the method refines background extraction in nonhomogeneous blocks by recomputing the shoulder thresholds. Rules that predict the final background extraction are derived by observing the behavior of successive background statistical measurements in the regions under the presence of dark and/or bright object pixels. Good results are shown for a number of outdoor scenes.

Rodriguez, Arturo A.↗
DOE OSTI2018

Naturally occurring 32 Si and low-background silicon dark matter detectors

The naturally occurring radioisotope Si-32 represents a potentially limiting background in future dark matter direct-detection experiments. We investigate sources of Si-32 and the vectors by which it comes to reside in silicon crystals used for fabrication of radiation detectors. We infer that the Si-32 concentration in commercial single-crystal silicon is likely variable, dependent upon the specific geologic and hydrologic history of the source (or sources) of silicon “ore” and the details of the silicon-refinement process. The silicon production industry is large, highly segmented by refining step, and multifaceted in terms of final product type, from which we conclude that production of Si-32-mitigated crystals requires both targeted silicon material selection and a dedicated refinement-through-crystal-production process. We review options for source material selection, including quartz from an underground source and silicon isotopically reduced in Si-32. To quantitatively evaluate the Si-32 content in silicon metal and precursor materials, we propose analytic methods employing chemical processing and radiometric measurements. Ultimately, it appears feasible to produce silicon-based detectors with low levels of Si-32, though significant assay method development is required to validate this claim and thereby enable a quality assurance program during an actual controlled silicon-detector production cycle.

assay↗
NASA NTRS1991

Passive range sensor refinement using texture and segmentation

Electrooptical sensors provide a covert way of computing range during helicopter flight. The optical flow-based computation of range provides range information only in certain distinguishable parts of the image. The regions where range information is available can be increased by performing texture analysis and object segmentation in the image. This paper reviews some of the literature on texture segmentation methods with a view towards applying them to images containing both man-made and natural objects at varying ranges. Two algorithmic approaches are given and their application to a real image is demonstrated. Results indicate that it will be necessary to combine several different texture measures and methods in a hierarchical way in order to achieve an object segmentation which is useful in enhancing range information.

Sridhar, Banavar↗
DOE OSTI2017

Volume of Lytic Vertebral Body Metastatic Disease Quantified Using Computed Tomography–Based Image Segmentation Predicts Fracture Risk After Spine Stereotactic Body Radiation Therapy

Purpose: To determine a threshold of vertebral body (VB) osteolytic or osteoblastic tumor involvement that would predict vertebral compression fracture (VCF) risk after stereotactic body radiation therapy (SBRT), using volumetric image-segmentation software. Methods and Materials: A computational semiautomated skeletal metastasis segmentation process refined in our laboratory was applied to the pretreatment planning CT scan of 100 vertebral segments in 55 patients treated with spine SBRT. Each VB was segmented and the percentage of lytic and/or blastic disease by volume determined. Results: The cumulative incidence of VCF at 3 and 12 months was 14.1% and 17.3%, respectively. The median follow-up was 7.3 months (range, 0.6-67.6 months). In all, 56% of segments were determined lytic, 23% blastic, and 21% mixed, according to clinical radiologic determination. Within these 3 clinical cohorts, the segmentation-determined mean percentages of lytic and blastic tumor were 8.9% and 6.0%, 0.2% and 26.9%, and 3.4% and 15.8% by volume, respectively. On the basis of the entire cohort (n=100), a significant association was observed for the osteolytic percentage measures and the occurrence of VCF (P<.001) but not for the osteoblastic measures. The most significant lytic disease threshold was observed at ≥11.6% (odds ratio 37.4, 95% confidence interval 9.4-148.9). On multivariable analysis, ≥11.6% lytic disease (P<.001), baseline VCF (P<.001), and SBRT with ≥20 Gy per fraction (P=.014) were predictive. Conclusions: Pretreatment lytic VB disease volumetric measures, independent of the blastic component, predict for SBRT-induced VCF. Larger-scale trials evaluating our software are planned to validate the results.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1988

PIPS: A Procedure for Interactive Pyramid Segmentation

The Procedure for Interactive Pyramid Segementation was designed to identify regions of spatially connected and spectrally homogeneous pixels in multispectral image data, and to allow these regions to be interactively manipulated without the use of processing parameters. The objective is to provide the user with the capability to easily extract and identify regions corresponding to target objects of interest. The approach is to segment a multispectral image into a set of regions and to allow the analyst to interactively refine the segmentation by direct manipulation. The user can elect to: interactively display maps of the spatial distribution of regions for any designated image subset; display the statistics of a given region; and label, merge, or split regions.

Wharton, S. W.↗
NASA NTRS1991

Refinement of ground reference data with segmented image data

One of the ways to determine ground reference data (GRD) for satellite remote sensing data is to photo-interpret low altitude aerial photographs and then digitize the cover types on a digitized tablet and register them to 7.5 minute U.S.G.S. maps (that were themselves digitized). The resulting GRD can be registered to the satellite image or, vice versa. Unfortunately, there are many opportunities for error when using digitizing tablet and the resolution of the edges for the GRD depends on the spacing of the points selected on the digitizing tablet. One of the consequences of this is that when overlaid on the image, errors and missed detail in the GRD become evident. An approach is discussed for correcting these errors and adding detail to the GRD through the use of a highly interactive, visually oriented process. This process involves the use of overlaid visual displays of the satellite image data, the GRD, and a segmentation of the satellite image data. Several prototype programs were implemented which provide means of taking a segmented image and using the edges from the reference data to mask out these segment edges that are beyond a certain distance from the reference data edges. Then using the reference data edges as a guide, those segment edges that remain and that are judged not to be image versions of the reference edges are manually marked and removed. The prototype programs that were developed and the algorithmic refinements that facilitate execution of this task are described.

Robinson, Jon W.↗
DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗
NASA NTRS1986

Automated remote sensing of sea ice using synthetic aperture radar

Two techniques for automated sea ice tracking: image pyramids (hierarchical correlation) and feature tracking were applied to a pair of SEASAT SAR sea ice images. The results compare well with each other and manually tracked estimates of the ice velocity field. Refinement of the image pyramid technique should include removal of a small number of erroneous velocity vectors using constraints dependent on correlation coefficient, near neighbor velocities, and velocity gradients. Estimates of sea ice velocity are successfully obtained over limited areas using the boundary segment tracking technique. Refinement of the feature tracking technique should include use of pressure ridge features, constrained search, and relaxation techniques.

Vesecky, J. F.↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗