Morphological Functions with Parallel Sets for the Pore Space of X-ray CT Images of Soil Columns

Summary High-resolution X-ray-computed-tomography (CT) images are increasingly used to numerically derive petrophysical properties of interest at the pore scale—in particular, effective permeability. Current micro-X-ray-CT facilities typically offer a resolution of a few microns per voxel, resulting in a field of view of approximately 5 mm3 for a 2,0482 charge-coupled device. At this scale, the resolution is normally sufficient to resolve pore-space connectivity and calculate transport properties directly. For samples exhibiting heterogeneity above the field of view of such a single high-resolution tomogram with resolved pore space, a second low-resolution tomogram can provide a larger-scale porosity map. This low-resolution X-ray-CT image provides the correlation structure of porosity at an intermediate scale, for which high-resolution permeability calculations can be carried out, forming the basis for upscaling methods dealing with correlated heterogeneity. In this study, we characterize spatial heterogeneity by use of overlapping registered X-ray-CT images derived at different resolutions spanning orders of magnitude in length scales. A 38-mm-diameter carbonate core is studied in detail and imaged at low resolution—and at high resolution by taking four 5-mm-diameter subsets, one of which is imaged by use of full-length helical scanning. Fine-scale permeability transforms are derived by use of direct porosity/permeability relationships, random sampling of the porosity/permeability scatter plot as a function of porosity, and structural correlations combined with stochastic simulation. A range of these methods is applied at the coarse scale. We compare various upscaling methods, including renormalization theory, with direct solutions by use of a Laplace solver and report error bounds. Finally, we compare with experimental measurements of permeability at both the small-plug and the full-plug scale. We find that both numerically and experimentally for the carbonate sample considered, which displays nonconnecting vugs and intrafossil pores, permeability increases with scale. Although numerical and experimental results agree at the larger scale, the digital core-analysis results underestimate experimentally measured permeability at the smaller scale. Upscaling techniques that use basic averaging techniques fail to provide truthful vertical permeability at the fine scale because of large permeability contrasts. At this scale, the most accurate upscaling technique uses Darcy's law. At the coarse scale, an accurate permeability estimate with error bounds is feasible if spatial correlations are considered. All upscaling techniques work satisfactorily at this scale. A key part of the study is the establishment of porosity transforms between high-resolution and low-resolution images to arrive at a calibrated porosity map to constrain permeability estimates for the whole core.

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Numerical modelling based saturation conductivity estimation uncertainty – influence of the quality of the pore space geometry representation based on X-ray CT images

10.5194/egusphere-egu2020-19695 ◽

2020 ◽

Author(s):

Krzysztof Lamorski ◽

Bartłomiej Gackiewicz ◽

Cezary Sławiński ◽

Shao-Yiu Hsu ◽

Liang-Cheng Chang

Keyword(s):

Ct Scan ◽

Cell Size ◽

Stokes Equations ◽

Pore Space ◽

Ct Scanning ◽

Ct Images ◽

X Ray ◽

Space Geometry ◽

The Impact ◽

Mesh Cell

X-ray computational tomography (CT) is becoming more and more popular research tool in geosciences. Estimation of the saturated conductivity of the porous media based on X-ray CT images is an example of its application.&#160;In case of simulations for the pore media, which are approximated by the very complicated meshes, problems might arise when mesh does not follow the shape of pore-space ideally, which may happen due to limitations imposed (e.g. due to some technical constraints) on minimum mesh cell size which usually is bigger than CT scan resolution used for determination of the pore space. If this is the case, the mesh can&#8217;t be generated properly in the narrow regions of the pore-space.The work tries to quantify the impact of the limited mesh quality on estimation of the saturated conductivity coefficient. Four mesh generation parameters, resulting in different sizes of the minimum mesh cell size, were compared. For comparison five different pore media (three sandpacks prepared from different sand fractions and two types of sandstones) were used, all of them were used in two repetitions which resulted in 10 studied samples in total. First samples were X-ray CT scanned with resolution 2um. Than images were thresholded to obtain information about pore-space. In the next step, for all of 10 3D images of pore-space, mesh was generated in four repetitions differing with minimum mesh cell size: 2.56, 3.41, 5.12 and 10.25 times greater than voxel size used for CT scanning.Saturated conductivity was simulated based on prepared meshes using finite volume based solver of the Navier-Stokes equations. Estimated for each sample saturated conductivity differed from 12% for coarse media to 200% for fine grain media for different numerical meshes representing with different accuracy pore space geometry.Based on samples studied, one may conclude that for optimal results of saturated conductivity numerical estimation, the smallest numerical mesh&#8217;s cell size should be of the level of pore media CT scan resolution. &#160;&#160;Acknowledgments:This work was partially supported by a grant from the Polish National Centre for Research and Development within the contract no.: PL-TW/IV/5/2017.

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Quality Improvement of X-ray CT Images by Estimation of the Mechanical Misalignment in a Projection Image Using an Iterative Method

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.137.213 ◽

2017 ◽

Vol 137 (3) ◽

pp. 213-219

Author(s):

Kenji Noguchi ◽

Satoshi Takahashi ◽

Daisuke Suzuki ◽

Atsushi Teramoto

Keyword(s):

Quality Improvement ◽

Iterative Method ◽

Ct Images ◽

X Ray ◽

Projection Image

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

Applied Sciences ◽

10.3390/app11094233 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4233

Author(s):

Biprodip Pal ◽

Debashis Gupta ◽

Md. Rashed-Al-Mahfuz ◽

Salem A. Alyami ◽

Mohammad Ali Moni

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Chest Radiography ◽

High Sensitivity ◽

Ct Images ◽

Classification Performance ◽

Learning Models ◽

X Ray ◽

Image Capturing ◽

Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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Pore Structure Evaluation of Quaternary Highly Vuggy Limestone by a Combination of X-ray CT Images of Differently Sized Cores

Rock Mechanics and Rock Engineering ◽

10.1007/s00603-020-02352-4 ◽

2021 ◽

Author(s):

Kentaro Masuoka ◽

Shinji Nakaya

Keyword(s):

Pore Structure ◽

Ct Images ◽

X Ray ◽

Structure Evaluation

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THE COMPARISION OF RADIOLOGICAL DISTANCE DIFFERENCE BETWEEN THE PARS INTER-ARTICULARIS AND PEDICLE, AS A REPRODUCIBLE AND CONSISTENT GUIDE FOR PEDICULAR SCREW FIXATION TECHNIQUE, IN SPINAL LUMBAR SURGERIES.

10.36106/9713874 ◽

2021 ◽

pp. 61-63

Author(s):

Bharath. V ◽

Hemanth Kumar ◽

Ashwanth Narayan ◽

Venkatachalam .K ◽

Ashwin. VY ◽

...

Keyword(s):

Ct Images ◽

Steady Increase ◽

Lateral Border ◽

X Ray ◽

Screw Insertion ◽

Pedicular Screw ◽

Pars Interarticularis ◽

Upward Direction ◽

Anatomical Relationship ◽

Distance Difference

The Inter-Pedicular and Inter-Pars distance was measured in a plain AP radiography (X-Ray) of 150 and 75 CT images normal patients between 18- 47 years of age. The aim of the study is to measure the normal Inter-Pedicular and Inter-Pars distance. We found that by studying the anatomical relationship between the inner or medial Pedicular border and the Pars outer or lateral border, gives the Orthopaedic Surgeon a reproducible and consistent guide towards exacting a pedicular screw placing. We found that both X-Ray and CT images shows steady increase in the Ipr and Ipd from L1 to L5, there is a minimal difference from L1-L2 and marked difference seen from L3 to L5, and showing the differences in distances are more in the males, compared to females. The Means of all the groups compared also proves that there is steady raise in the diameter of the IPR and IPD from L1 to L5, where there is dramatical and signicant change in the upward direction, noted from L3 to L5. The mean difference is almost constant from L1to L2. So this study, did essentially to help, establish that, the inner medial border of pedicle, is in near relationship to, the outer lateral border of the Pars-Interarticularis, which helps in establishing the latero-medial entry point for the pedicular screw insertion in the lumbar spine.

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Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

10.2118/208665-ms ◽

2021 ◽

Author(s):

Khalid Labib Alsamadony ◽

Ertugrul Umut Yildirim ◽

Guenther Glatz ◽

Umair bin Waheed ◽

Sherif M. Hanafy

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Transfer Learning ◽

Temporal Resolution ◽

Exposure Time ◽

Ct Images ◽

Loss Functions ◽

High Quality ◽

X Ray ◽

Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

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A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.721.783 ◽

2014 ◽

Vol 721 ◽

pp. 783-787

Author(s):

Shao Hu Peng ◽

Hyun Do Nam ◽

Yan Fen Gan ◽

Xiao Hu

Keyword(s):

Multiscale Analysis ◽

Automatic Segmentation ◽

Local Variation ◽

Recognition System ◽

Ct Images ◽

Gray Level ◽

Ct Image ◽

Image Pyramid ◽

Level Variation ◽

X Ray

Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

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