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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Reconciling Basin-Scale Top-Down and Bottom-Up Methane Emission Measurements for Onshore Oil and Gas Development: Cooperative Research and Development Final Report, CRADA Number CRD-14-572

The overall objective of the Research Partnership to Secure Energy for America (RPSEA)-funded research project is to develop independent estimates of methane emissions using top-down and bottom-up measurement approaches and then to compare the estimates, including consideration of uncertainty. Such approaches will be applied at two scales: basin and facility. At facility scale, multiple methods will be used to measure methane emissions of the whole facility (controlled dual tracer and single tracer releases, aircraft-based mass balance and Gaussian back-trajectory), which are considered top-down approaches. The bottom-up approach will sum emissions from identified point sources measured using appropriate source-level measurement techniques (e.g., high-flow meters). At basin scale, the top-down estimate will come from boundary layer airborne measurements upwind and downwind of the basin, using a regional mass balance model plus approaches to separate atmospheric methane emissions attributed to the oil and gas sector. The bottom-up estimate will result from statistical modeling (also known as scaling up) of measurements made at selected facilities, with gaps filled through measurements and other estimates based on other studies. The relative comparison of the bottom-up and top-down estimates made at both scales will help improve understanding of the accuracy of the tested measurement and modeling approaches. The subject of this CRADA is NREL's contribution to the overall project. This project resulted from winning a competitive solicitation no. RPSEA RFP2012UN001, proposal no. 12122-95, which is the basis for the overall project. This Joint Work Statement (JWS) details the contributions of NREL and Colorado School of Mines (CSM) in performance of the CRADA effort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

58 GEOSCIENCES↗
DOE OSTI2025

CMB-S4: Foreground-cleaning Pipeline Comparison for Measuring Primordial Gravitational Waves

We compare multiple foreground-cleaning pipelines for estimating the tensor-to-scalar ratio, r, using simulated maps of the planned CMB-S4 experiment within the context of the South Pole Deep Patch. To evaluate robustness, we analyze bias and uncertainty on r across various foreground suites using map-based simulations. The foreground-cleaning methods include: a parametric maximum likelihood approach applied to auto- and cross-power spectra between frequency maps; a map-based parametric maximum-likelihood method; and a harmonic-space internal linear combination using frequency maps. We summarize the conceptual basis of each method to highlight their similarities and differences. To better probe the impact of foreground residuals, we implement an iterative internal delensing step, leveraging a map-based pipeline to generate a lensing B-mode template from the large aperture telescope frequency maps. Our results show that the performance of the three approaches is comparable for simple and intermediate-complexity foregrounds, with σ(r) ranging from 3–5 ×10 −4 . However, biases at the 1σ–2σ level appear when analyzing more complex forms of foreground emission. By extending the baseline pipelines to marginalize over foreground residuals, we demonstrate that contamination can be reduced to within statistical uncertainties, albeit with a pipeline-dependent impact on σ(r), which translates to a detection significance between 2σ and 4σ for an input value of r = 0.003. These findings suggest varying levels of maturity among the tested pipelines, with the auto- and cross-spectra-based approach demonstrating the best stability and overall performance. Moreover, given the extremely low noise levels, mutual validation of independent foreground-cleaning pipelines is essential to ensure the robustness of any potential detection.

astronomy data analysis↗
DOE OSTI2014

Comparing two correlated C indices with right‐censored survival outcome: a one‐shot nonparametric approach

The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right‐censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right‐censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U ‐statistics‐based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z ‐score test is then constructed to compare the two C indices. We validate our one‐shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one‐shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one‐shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. Copyright © 2014 John Wiley & Sons, Ltd.

Kang, Le↗
DOE OSTI2015

Neutron/Gamma-ray discrimination through measures of fit

Statistical tests and their underlying measures of fit can be utilized to separate neutron/gamma-ray pulses in a mixed radiation field. In this article, first the application of a sample statistical test is explained. Fit measurement-based methods require true pulse shapes to be used as reference for discrimination. This requirement makes practical implementation of these methods difficult; typically another discrimination approach should be employed to capture samples of neutrons and gamma-rays before running the fit-based technique. In this article, we also propose a technique to eliminate this requirement. These approaches are applied to several sets of mixed neutron and gamma-ray pulses obtained through different digitizers using stilbene scintillator in order to analyze them and measure their discrimination quality. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗
DOE OSTI2018

A Revised Capsule Design for the Accelerated Testing of Advanced Reactor Fuels

Testing of fast reactor fuel is currently challenging because of the thermal neutron spectrum of the Advanced Test Reactor (ATR) and the fact that ATR coolant is substantially below typical temperatures found in liquid metal-cooled fast reactors. Despite these issues, since 2003 the Idaho National Laboratory (INL) has successfully tested a variety of metallic, oxide, and nitride fuels for fast reactor applications. There are, however, opportunities to improve the experiments so that the time required to reach a desired burnup can be reduced, and so the sensitivity of the experiments to fabrication tolerances can be minimized. A revised capsule design exploits the use of smaller diameter rodlets to increase power densities and correspondingly reduce the irradiation time required to reach high burnup. Thermal and nuclear analyses indicate that the time to reach 30at% burnup could be reduced from about 12 years to approximately 2-3 years if the fuel diameter is reduced by one-half and to around 1-2 years if the diameter is reduced by one-third. In addition, reducing fuel diameter improves the radial power distribution in the fuel without the use of cadmium shrouding. This observation opens up the possibility of testing advanced reactor fuel systems in Small I locations in the ATR where there is substantial unused capacity. Although not directly tied to a revised capsule design, post-irradiation furnace testing is proposed to provide time-to-failure data that will be helpful in the benchmarking of fuel performance models and in assessing the performance of new fuel designs relative to more standard fast reactor fuel systems. In this paradigm, ATR irradiation experiments will be viewed more as preparation for furnace testing than as experiments that exist in isolation. An added benefit to furnace testing is that uncertainties in temperatures will significantly be reduced relative to non-instrumented capsules in the ATR. The time and cost in developing this experimental capability is expected to be reduced considerably because of the availability of the blister anneal furnace used by the HPRR program. A key issue in the experiment approach is the potential for non-prototypical rodlet dimensions to influence the observed performance of a given fuel design. For example, a qualitative assessment indicates that the effects of fuel-cladding chemical interactions during furnace testing might be more pronounced in reduced diameter rodlets simply because thinner cladding would be more sensitive to a given depth of fission product penetration. In a traditional empirically-driven approach to fuel qualification, any change from prototypical behavior is undesirable because the resulting data cannot easily be assimilated into a statistical correlation. However, in the current approach, systematic variations in fuel diameter provide the opportunity to benchmark fuel performance models over a wider range of parameter space, which in turn is expected to improve understanding and result in better, more physically based models in the long term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2018

Synchrophasor Data Correction under GPS Spoofing Attack: A State Estimation-Based Approach

GPS spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMU). In this paper, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasorbased power system state estimation (SE), and then derive the spoofing-matched algorithms for synchrophasor data correction against GPS spoofing attack. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms’ performance on GSA detection and state estimation. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. The accuracy of state estimation is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.

Phasor measurement units, Global Positioning Syste↗
DOE OSTI2020

Deterministic and Statistical Modeling of a New Thermal Breakout Technology for Measuring the Maximum Horizontal In-Situ Stress

The current state-of-the-art technology for in-situ stress measurements involves an integrated approach that combines borehole breakout observations, drilling-induced tensile fractures, and hydraulic fracturing tests (i.e., “mini-fracs”). This methodology has achieved wide application in the oil and gas industry but has several limitations that often prevent successful in-situ stress measurements. One major limitation is that breakouts do not appear in all boreholes and are generally only a natural occurrence that cannot easily be controlled. Because borehole breakouts are used to directly measure the maximum horizontal in-situ stress magnitude, the absence of borehole breakouts presents a major data gap for in-situ stress measurements. In response to this data gap, a new US Department of Energy (US DOE)-sponsored thermal breakout technology that will provide a method for thermally inducing borehole breakouts and allow the consistent measurement of the maximum horizontal stress magnitude is currently in development. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which can be directly correlated to the maximum horizontal stress magnitude. The first step in this project was an analytical modeling study of the thermal breakout process. Based on the Kirsch solution (Kirsch 1898), a deterministic and statistical analysis was performed on the pertinent parameters that influence the maximum horizontal stress calculation. As a result of the analysis, the findings indicate that the thermal breakout technology is feasible and provides improved accuracy and/or an enhanced ability to measure the maximum horizontal stress. Future work as part of this US DOE-sponsored project includes additional validation through more detailed numerical modeling, laboratory testing, and field testing of the thermal breakout technology.

58 GEOSCIENCES↗
DOE OSTI2017

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Development of High Alumina Glass Property Data for Hanford High-Level Waste Glass Models - 16231

Roughly half of the projected Hanford high-level waste batches will have waste loadings limited by relatively high concentration of Al2O3. Individual glasses have been formulated and tested to demonstrate that it is possible to increase the loading of these high-Al2O3 wastes in glass by as much as 50%. To implement such increases in waste loading in the Hanford Tank Waste Treatment and Immobilization Plant, the impact of composition on the properties of high-Al2O3 waste glasses must be quantified in the form of validated glass property-composition models. To collect the data necessary for glass property-composition models, a multi-phase experimental approach was developed. In the first phase of the study, a set of 46 glass compositions were statistically designed to most efficiently backfill existing data in the composition region for high-Al2O3 (15 to 30 wt%) waste glasses. The glasses were fabricated and key glass properties were tested: •Product Consistency Test (PCT) on quench (Q) and canister centerline cooled (CCC) samples •Toxicity Characteristic Leaching Procedure (TCLP) on Q and CCC samples •Crystallinity as a function of temperature (T) at equilibrium and of CCC samples •Viscosity and electrical conductivity as a function of T The measured properties of these glasses were compared to predictions from previously existing models developed over lower Al2O3 concentration ranges. Areas requiring additional testing and modeling were highlighted.

Vienna, John D.↗
DOE OSTI2019

Multi-‘omic’ analyses of the dynamics, mechanisms, and pathways for carbon turnover in grassland soil under two climate regimes

Soil microbial activity drives the carbon and nitrogen cycles and is an important determinant of atmospheric trace gas turnover, yet most soils are dominated by microorganisms with unknown metabolic capacities. This knowledge gap precludes meaningful predictions of relationships between microorganism types and their biogeochemical functions. Specifically, as climate change physically alters terrestrial ecosystems, the impact on microbial consortia and potential consequences for release of carbon and other nutrients from soils are difficult to predict. In this project, we investigated the identities, activity, and functional potential of microorganisms residing in a well characterized grassland ecosystem (the Angelo Coast Range Reserve) undergoing a controlled 14-year rainfall extension climate change experiment. The research was achieved using a combination of “omics”-based technologies, including genome-resolved metagenomics, transcriptomics, and proteomics. The approach allowed us to capture multiple facets of community ecology and activity in situ. Supported by this award, significant progress was made in addressing the grand challenge of achieving genome-resolution for very complex soil microbial communities. In addition to improvements in the methodology, the research required new approaches to define the 3-dimentional distributions of microorganisms and their metabolic traits in soils and to decipher the impact of climate. Underpinning such analyses was the need for sufficient statistical power to test for geographic, soil depth and rainfall controls on community composition and metabolic properties. This required that we scale up throughput substantially. We overcame significant technical barriers to achieve complex microbial community resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Statistical Characterization of Fracture in CFRP-Concrete Interfaces Due to Wet-Dry Cycling

The experimental fracture mechanics Single Contoured-Cantilever Beam (SCCB) specimen was implemented to evaluate environmentally conditioned concrete-CFRP interfaces. Fracture studies were conducted on specimens having undergone 10, 20 and 30 cycles of alternate wetting and drying. Half of the 24 conditioned specimens studied were placed in a 10% sodium sulfate solution (pH = 6.2–6.5) and half were soaked in sodium hydroxide (pH = 12) to simulate naturally occurring acidic and alkaline field exposure-conditions, respectively. At the end of each target age, SCCB fracture testing was conducted. Two approaches were used in determining the fracture toughness of the CFRP-concrete interface bond that depend on the proper identification of loads reaching a critical state, P{sub c}. In the first, the topmost, or “head”, −initiation and -arrestment loads were averaged, and in the second, the mean of the entire “set” of -initiation and -arrestment loads was taken. This raw-data was then reduced by a statistical-based method from which a range of permissible critical loads was designated. The final G{sub Ic} values cited are based on an averaging of this refined data. Considerable degradation of the CFRP-concrete interface bond resulted especially for cases of latter-stage cycling in the aggressive sodium-sulfate and -hydroxide solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Estimating the CO 2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

Abstract The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO 2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO 2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO 2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m −2 yr −1 ppm −1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO 2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO 2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO 2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO 2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO 2 concentrations.

Environmental Sciences & Ecology↗
DOE OSTI2021

Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework

A theoretical framework which unifies the conventional Mori--Zwanzig formalism and the approximate Koopman learning of deterministic dynamical systems from noiseless observation is presented. In this framework, the Mori--Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that, similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori--Zwanzig formalism with Mori's linear projection operator. We have developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We have adopted the Lorenz `96 system as a test problem and solved for the above operators. These operators exhibit complex behaviors, which are unlikely to be captured by traditional modeling approaches in Mori--Zwanzig analysis. The nontrivial generalized fluctuation-dissipation relationship, which relates the memory kernel with the two-time correlation statistics of the orthogonal dynamics, was numerically verified as a validation of the solved operators. Here we present numerical evidence that the generalized Langevin equation, a key construct in the Mori--Zwanzig formalism, is more advantageous in predicting the evolution of the reduced set of observables than the conventional approximate Koopman operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Efficient Pipeline for Abdomen Segmentation in CT Images

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient’s diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline’s optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗