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

2021 ◽  
Author(s):  
Kentaro Masuoka ◽  
Shinji Nakaya
Keyword(s):  
Pore Structure ◽  
Ct Images ◽  
X Ray ◽  
Structure Evaluation

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

Energies ◽  
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.

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

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.

scholarly journals Freeze–Thaw Resistance and Air-Void Analysis of Concrete with Recycled Glass–Pozzolan Using X-ray Micro-Tomography

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 154
Author(s):  
Marija Krstic ◽  
Julio F. Davalos ◽  
Emanuele Rossi ◽  
Stefan C. Figueiredo ◽  
Oguzhan Copuroglu
Keyword(s):  
Fly Ash ◽  
Pore Structure ◽  
The United States ◽  
Adsorption Method ◽  
X Ray ◽  
Freeze Thaw ◽  
Void System ◽  
Spatial Parameters ◽  
Micro Tomography ◽  
Air Void

Recent studies have shown promising potential for using Glass Pozzolan (GP) as an alternative supplementary cementitious material (SCM) due to the scarcity of fly ash and slag in the United States. However, comprehensive studies on the freeze–thaw (FT) resistance and air void system of mixtures containing GP are lacking. Therefore, this study aimed to evaluate GP’s effect on FT resistance and characterize mixtures with different GP contents, both macro- and microscopically. In this study, six concrete mixes were considered: Three mixes with 20%, 30% and 40% GP as cement replacements and two other comparable mixes with 30% fly ash and 40% slag, as well as a mix with 100% Ordinary Portland cement (OPC) as a reference. Concrete samples were prepared, cured and tested according to the ASTM standards for accelerated FT resistance for 1000 cycles and corresponding dynamic modulus of elasticity (Ed). All the samples showed minimal deterioration and scaling and high F/T resistance with a durability factor of over 90%. The relationships among FT resistance parameters, air-pressured method measurements of fresh concretes and air void analysis parameters of hardened concretes were examined in this study. X-ray micro-tomography (micro-CT scan) was used to evaluate micro-cracks development after 1000 freeze–thaw cycles and to determine spatial parameters of air voids in the concretes. Pore structure properties obtained from mercury intrusion porosimetry (MIP) and N2 adsorption method showed refined pore structure for higher cement replacement with GP, indicating more gel formation (C-S-H) which was verified by thermogravimetric analysis (TGA).

Comparative Studies of Ultra Low-κ Porous Silica Films with 2-D Hexagonal and Disordered Pore Structures

2004 ◽  
Vol 812 ◽  
Author(s):  
Nobutoshi Fujii ◽  
Kazuhiro Yamada ◽  
Yoshiaki Oku ◽  
Nobuhiro Hata ◽  
Yutaka Seino ◽  
...  
Keyword(s):  
Pore Structure ◽  
Nonionic Surfactants ◽  
Porous Silica ◽  
Silica Film ◽  
X Ray Diffraction ◽  
Silica Films ◽  
X Ray ◽  
X Ray Scattering ◽  
Porous Silica Film ◽  
Diffraction Peaks

AbstractPeriodic 2-dimensional (2-D) hexagonal and the disordered pore structure silica films have been developed using nonionic surfactants as the templates. The pore structure was controlled by the static electrical interaction between the micelle of the surfactant and the silica oligomer. No X-ray diffraction peaks were observed for the disordered mesoporous silica films, while the pore diameters of 2.0-4.0 nm could be measured by small angle X-ray scattering spectroscopy. By comparing the properties of the 2-D hexagonal and the disordered porous silica films which have the same porosity, it is found that the disordered porous silica film has advantages in terms of the dielectric constant and Young's modulus as well as the hardness. The disordered porous silica film is more suitable for the interlayer dielectrics for ULSI.

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.

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.

Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

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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