• Imaging Science in Dentistry
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• v.41 no.3
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• pp.101-106
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• 2011

Purpose : This study evaluated the potential use of the computed tomography indices (CTI) on cone beam CT (CBCT) images for an assessment of the bone mineral density (BMD) in postmenopausal osteoporotic women. Materials and Methods : Twenty-one postmenopausal osteoporotic women and 21 postmenopausal healthy women were enrolled as the subjects. The BMD of the lumbar vertebrae and femur were calculated by dual energy X-ray absorptiometry (DXA) using a DXA scanner. The CBCT images were obtained from the unilateral mental foramen region using a PSR-$9000N^{TM}$ Dental CT system. The axial, sagittal, and coronal images were reconstructed from the block images using $OnDemend3D^{TM}$. The new term "CTI" on CBCT images was proposed. The relationship between the CT measurements and BMDs were assessed and the intra-observer agreement was determined. Results : There were significant differences between the normal and osteoporotic groups in the computed tomography mandibular index superior (CTI(S)), computed tomography mandibular index inferior (CTI(I)), and computed tomography cortical index (CTCI). On the other hand, there was no difference between the groups in the computed tomography mental index (CTMI: inferior cortical width). Conclusion : CTI(S), CTI(I), and CTCI on the CBCT images can be used to assess the osteoporotic women.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.877-881
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• 2005

In this paper, a novel method for analyzing a textile fabric structure is proposed to segment each yarn of the textile fabric from voxel data made out of its X-ray computed tomography (CT) images. In order to segment the each yarn, directions of fibers, of which yarn consists, are firstly estimated by correlating the voxel with a fiber model. Second, each fiber is reconstructed by clustering the voxel of the fiber using the estimated fiber direction as a similarity. Then, each yarn is reconstructed by clustering the reconstructed fibers using a distance which is newly defined as a dissimilarity. Consequently, each yarn of the textile fabric is segmented from the voxel data. The effectiveness of the proposed method is confirmed by experimentally applying the method to voxel data of a sample plain woven fabric, which is made of polyester two folded yarn. The each two folded yarn is correctly segmented by the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.859-861
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• 2005

X-ray를 이용한 CT(Computed Tomography : 이하 CT)영상은 사물에 대해 회전하면서 X-ray가 투과하여 감약 정도에 따라서 영상을 획득하지만 검사 목적과는 관계없이 발생되는 통계적인 오차로 인해 정확한 CT영상의 구성을 교란하거나 방해하여 영상의 질을 저하시키고 미세 부분의 관찰 능력을 감소시키는 장해 음영인 아티팩트(artifact)라는 노이즈가 발생한다. 이러한 노이즈를 제거하는 필터를 설계 할 때는 두 가지 고려해야 할 사항이 있는데 첫째는 영상내의 노이즈을 정확히 판단하여 효과적으로 제거해야 하며, 둘째로는 원래의 영상에 가깝도록 경계와 같은 세부 영역을 보존해야 한다는 점이다. 기존에는 mean 필터나 median 필터, 그리고 Gaussian 필터 등을 사용했지만 상세한 부분을 보존하기에는 실패하는 단점이 있다. 따라서 본문에서는 wavelet 변환을 하여 영상의 주파수 대역을 저주파 영역과 고주파 영역으로 분리하여 각각의 영역에서 노이즈를 제거할 수 있도록 적합한 필터를 설계하고 방법을 제안하여 그 필터를 CT 3차원 뇌혈관 영상에 적용하여 많은 노이즈를 제거하였고 낮은 Threshold값에서도 작은 혈관을 관찰 할 수 있었다.

• Structural Engineering and Mechanics
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• v.74 no.2
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• pp.283-296
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• 2020

The coupled hydro-mechanical loading conditions commonly occur in the geothermal and petroleum engineering projects, which is significantly important influence on the stability of rock masses. In this article, the influence of flaw inclination angle of fracture behaviors in rock-like materials subjected to both mechanical loads and internal hydraulic pressures is experimentally studied using the 3-D X-ray computed tomography combined with 3-D reconstruction techniques. Triaxial compression experiments under confining pressure of 8.0 MPa are first conducted for intact rock-like specimens using a rock mechanics testing system. Four pre-flawed rock-like specimens containing a single open flaw with different inclination angle under the coupled hydro-mechanical loading conditions are carried out. Then, the broken pre-flawed rock-like specimens are analyzed using a 3-D X-ray computed tomography (CT) scanning system. Subsequently, the internal damage behaviors of failed pre-flawed rock-like specimens are evaluated by the 3-D reconstruction techniques, according to the horizontal and vertical cross-sectional CT images. The present experimental does not only focus on the mechanical responses, but also pays attentions to the internal fracture characteristics of rock-like materials under the coupled hydro-mechanical loading conditions. The conclusion remarks are significant for predicting the rock instability in geothermal and unconventional petroleum engineering.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.91-96
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• 2007

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present standard for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. A forward model of a simplified mammography geometry for EIT imaging was proposed earlier. In this paper, we propose an iterative algorithm for computing the current pattern that will be applied to the electrodes. The current pattern applied to the electrodes influences the voltages measured on the electrodes. Since the measured voltage data is going to be used in the impedance imaging computation, it is desirable to apply currents that result in the largest possible voltage signal. We compute the eigenfunctions for a homogenous medium that will be applied as current patterns to the electrodes. The algorithm for the computation of the eigenfunctions is presented. The convergence of the algorithm is shown by computing the eigencurrent of the simplified mammography geometry.

• Coupled systems mechanics
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• v.11 no.1
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• pp.15-32
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• 2022

The comprehensive understanding of the fiber reinforced polymer behavior requires the use of advanced non-destructive testing methods due to its heterogeneous microstructure and anisotropic mechanical proprieties. In addition, the material response under load is strongly associated with manufacturing defects (e.g., voids, inclusions, fiber misalignment, debonds, improper cure and delamination). Such imperfections and microstructures induce various damage mechanisms arising at different scales before macrocracks are formed. The origin of damage phenomena can only be fully understood with the access to underlying microstructural features. This makes X-ray Computed Tomography an appropriate imaging tool to capture changes in the bulk of fibrous materials. Moreover, Digital Volume Correlation (DVC) can be used to measure kinematic fields induced by various loading histories. The correlation technique relies on image contrast induced by microstructures. Fibrous composites can be reinforced by different fiber architectures that may lead to poor natural contrast. Hence, a priori analyses need to be performed to assess the corresponding DVC measurement uncertainties. This study aimed to evaluate measurement resolutions of global and regularized DVC for glass fiber reinforced polymers with different fiber architectures. The measurement uncertainties were evaluated with respect to element size and regularization lengths. Even though FE-based DVC could not reach the recommended displacement uncertainty with low spatial resolution, regularized DVC enabled for the use of fine meshes when applying appropriate regularization.

• Journal of the Korean Geotechnical Society
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• v.39 no.8
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• pp.17-28
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• 2023

Laboratory-scale hydraulic fracturing experiments were conducted on granite specimens at various viscosities and injection rates of the fracturing fluid. A series of cross-sectional computed tomography (CT) images of fractured specimens was obtained via a three-dimensional X-ray CT imaging method. Pixel-level fracture segmentation of the CT images was conducted using a convolutional neural network (CNN)-based Nested U-Net model structure. Compared with traditional image processing methods, the CNN-based model showed a better performance in the extraction of thin and complex fractures. These extracted fractures extracted were reconstructed in three dimensions and morphologically analyzed based on their fracture volume, aperture, tortuosity, and surface roughness. The fracture volume and aperture increased with the increase in viscosity of the fracturing fluid, while the tortuosity and roughness of the fracture surface decreased. The findings also confirmed the anisotropic tortuosity and roughness of the fracture surface. In this study, a CNN-based model was used to perform accurate fracture segmentation, and quantitative analysis of hydraulic stimulated fractures was conducted successfully.

• Computers and Concrete
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• v.31 no.5
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• pp.433-442
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• 2023

This study presents the visually observed behavior of fibers embedded in concrete samples that were subjected to a flexural bending test. Three types of fibers such as macro polypropylene, macro polyethylene, and the hybrid of steel and polyvinyl alcohol were mixed with cement by a designated mix ratio to prepare a total of nine specimens of each. The bending test was conducted by following ASTM C1609 with a net deflection of 2, 4, and 7 mm. The X-ray computed tomography (XCT) was carried out for 7 mm-deflection specimens. The original XCT images were post-processed to denoise the beam-hardening effect. Then, fiber, crack, and void were semi-manually segmented. The hybrid specimen showed the highest toughness compared to the other two types. Debonding based on 2D XCT sliced images was commonly observed for all three groups. The cement matrix near the crack surface often involved partially localized breakage in conjunction with debonding. The pullout was predominant for steel fibers that were partially slipped toward the crack. Crack bridging and rupture were not found presumably due to the image resolution and the level of energy dissipation for poly-fibers, while the XCT imaging was advantageous in evaluating the distribution and behavior of various fibers upon bending for fiber-reinforced concrete beam elements.

• Radiation Oncology Journal
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• v.3 no.1
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• pp.59-64
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• 1985

Computation of three dimensional dose distribution using CT image and RT plan was applied to a case of pituitary adenoma. Algorithm was based on two dimensional Tissue Maximun Ratio model extended to the third dimension. The resulting isodose curve of transeverse, coronal and sagittal section was demonstrated. This RT plan allows computation of dose distribution in any arbitarily defined plane in addition to conventional cross sectional view.