• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.143-149
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• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.93-101
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• 2011

High quality X-ray computed microtomography (micro-CT) imaging of internal microstructures and pore space in geomaterials is often hampered by some inherent noises embedded in the images. In this paper, we introduce image calibration techniques for removing the most common noises in X-ray micro-CT, cupping (brightness difference between the periphery and central regions) and ring artifacts (consecutive concentric circles emanating from the origin). The artifacts removal sequentially applies coordinate transformation, normalization, and low-pass filtering in 2D Fourier spectrum to raw CT-images. The applicability and performance of the techniques are showcased by describing extraction of 3D pore structures from micro-CT images of porous basalt using artifacts reductions, binarization, and volume stacking. Comparisions between calibrated and raw images indicate that the artifacts removal allows us to avoid the overestimation of porosity of imaged materials, and proper calibration of the artifacts plays a crucial role in using X-ray CT for geomaterials.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.45-52
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• 2021

In this paper, we propose a prediction model for osteopenia and osteoporosis based on a convolutional neural network(CNN) using computed tomography(CT) images. In a single CT image, CNN had a limitation in utilizing important local features for diagnosis. So we propose a compound model which has two identical structures. As an input, two different texture images are used, which are converted from a single normalized CT image. The two networks train different information by using dissimilarity loss function. As a result, our model trains various features in a single CT image which includes important local features, then we ensemble them to improve the accuracy of predicting osteopenia and osteoporosis. In experiment results, our method shows an accuracy of 77.11% and the feature visualize of this model is confirmed by using Grad-CAM.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.83-92
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• 2011

The random and heterogeneous pore structure is a significant factor that dominates physical and mechanical behaviors of soils such as fluid flow and geomechanical responses driven by loading. The characterization method using non-destructive testing such as micro X-ray CT technique which has a high resolution with micrometer unit allows to observe internal structure of soils. However, the application has been limited to qualitatively observe 2D and 3D CT images and to obtain the void ratio at macro-scale although the CT images contain enormous information of materials of interests. In this study, we constructed the 3D particle and pore structures based on sequentially taken 2D images of glass beads and quantitatively defined complex pore structure with void cell and void channel. This approach was enabled by implementing image processing techniques that include coordinate transformation, binarization, Delaunay Triangulation, and Euclidean Distance Transform. It was confirmed that the suggested algorithm allows to quantitatively evaluate the distribution of void cells and their connectivity of heterogeneous pore structures for glass beads.

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.455-463
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• 2018

This study developed a method to quantitatively measure the size of cracks in concrete using X-ray CT images. We prepared samples with a diameter of 50 mm and a length of 100 mm by coring cracked concrete block that was obtained by chipping. We used a micro-focus X-ray CT, then applied the 3DMA method (3 Dimensional Medial axis Analysis) to the 3D CT images to find effective parameters for damage assessment. Finally, we quantitatively assessed the damage based on sample locations, using the damage assessment parameter. Results clearly show that the area near the chipping surface was damaged to a depth of 3 cm. Furthermore, X-ray methods can be used to evaluate the porosity index, burn number, and medial axis, which are used to estimate the damage to the area near the chipping surface.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.227-235
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• 2012

The void distribution in concrete materials strongly affects its material properties. Therefore, the identification of spatial distribution of void is important to understand and estimate material behavior. To examine and quantify the void distribution inside lightweight aggregates, CT(computed tomography) image is used. 3D lightweight aggregate images are generated by stacking of cross-sectional images from CT. Spatial distribution of void of aggregate along the direction is visualized on the sphere using probability distribution function. Stiffness of lightweight aggregate for the directions is also examined. It is confirmed that direction-based probability distribution and stiffness from CT images are effective in characterizing void distributions of aggregates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.121-127
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• 2011

Spatial distribution of void space in concrete materials strongly affects mechanical and physical behaviors. Therefore, the identification of characteristic void distribution helps understand material properties and is essential to estimate the integrity of material performance. The 3D micro CT (X-ray microtomography) is implemented to examine and to quantify the void distribution of a lightweight aggregate concrete using an image analysis technique and probabilistic approach in this study. The binarization and subsequent stacking of 2D cross-sectional images virtually create 3D images of targeting void space. Then, probability distribution functions such as two-point correlation and lineal-path functions are applied for void characterization. The lightweight aggregates embedded within the concrete are individually analyzed to construct the intra-void space. Results shows that the low-order probability functions and the density distribution based on the 3D micro CT images are applicable and useful methodology to characterize spatial distribution of void space and constituents in concrete.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.542-557
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• 2019

The behavior of rock mass is influenced by its microscopic feature of internal structure generating from forming and metamorphic process. This study investigated a new methodology for characterization of rock based on the X-ray CT (computed tomography) images reflecting the spatial distribution characteristics of internal constituent materials. The X-ray image based analysis is capable of quantification of heterogeneity and anisotropy of rock fabric, size distribution and shape parameter analysis of rock mineral grains, fluid flow simulation based on pore geometry image and roughness evaluation of unexposed joint surface which are hardly acquired by conventional rock testing methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1A
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• pp.31-37
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• 2012

The phase distribution in concrete materials strongly affects its material properties. It is important to identify the spatial distribution of void in concrete because the void in concrete materials affects mechanical behavior and permeability significantly. Therefore, a proper method to describe the void distribution of a material is needed. In this research, CT(computed tomography) is used to examine and to quantify the void distribution of porous concrete specimens. 3D concrete digital specimens are created by subsequent stacking of 2D cross-sectional images from CT. Then, probability distribution functions such as two-point correlation, lineal-path and two-point cluster functions are used for void distribution characterization. It is confirmed that probability distribution functions obtained from CT images are effective in characterizing void distributions including the anisotropy and percolation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.