• Imaging Science in Dentistry
• /
• v.51 no.2
• /
• pp.149-154
• /
• 2021

Purpose: This study aimed to investigate the computed tomography and magnetic resonance imaging features of giant cell tumors in the temporomandibular joint region to facilitate accurate diagnoses. Materials and Methods: From October 2007 to June 2020, 6 patients (2 men and 4 women) at Yonsei University Dental Hospital had histopathologically proven giant cell tumors in the temporomandibular joint. Their computed tomography and magnetic resonance imaging findings were reviewed retrospectively, and the cases were classified into 3 types based on the tumor center and growth pattern observed on the radiologic findings. Results: The age of the 6 patients ranged from 25 to 53 years. Trismus was found in 5 of the 6 cases. One case recurred. The mean size of the tumors, defined based on their greatest diameter, was 32 mm (range, 15-41 mm). The characteristic features of all cases were a heterogeneously-enhancing tumorous mass with a lobulated margin on computed tomographic images and internal multiplicity of signal intensity on T2-weighted magnetic resonance images. According to the site of origin, 3 tumors were bone-centered, 2 were soft tissue-centered, and 1 was peri-articular. Conclusion: Computed tomography and magnetic resonance imaging yielded a tripartite classification of giant cell tumors of the temporomandibular joint according to their location on imaging. This study could help clinicians in the differential diagnosis of giant cell tumors and assist in proper treatment planning for tumorous diseases of the temporomandibular joint.

• Journal of Biomedical Engineering Research
• /
• v.42 no.3
• /
• pp.100-106
• /
• 2021

Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.2
• /
• pp.187-192
• /
• 2021

This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.85-92
• /
• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Journal of Space Technology and Applications
• /
• v.1 no.2
• /
• pp.268-281
• /
• 2021

Recently, the synthetic aperture radar (SAR) has been increasingly in demand due to its advantage of being able to observe desired points regardless of time and weather. To utilize SAR data, first of all, many pre-processing such as satellite orbit correction, radiometric calibration, multi-looking, and geocoding are required. For analysis of SAR imagery such as object detection, change detection, and DEM(Digital Elevation Model), additional processings are needed. These pre-processing and additional processes are very complex and require a lot of time and computational resources. In order to handle the SAR images easily, the institutions that use SAR images develop analysis tools and provide users. This paper introduces the function and characteristics of representative SAR imagery analysis tools.

• Journal of the Korean Institute of Rural Architecture
• /
• v.23 no.4
• /
• pp.1-12
• /
• 2021

Recently, the use of unmanned aerial vehicles (UAVs) is increasing in the field of land information acquisition and terrain exploration through high-altitude aerial photography. High-altitude aerial photography is suitable for large-scale geographic information collection, but has the disadvantage that it is difficult to accurately collect small-scale geographic information. Therefore, this study used low-altitude UAV to monitor changes in small rural spaces around rural resources, and the results are as follows. First, the low-altitude aerial imagery had a very high spatial resolution, so it was effective in reading and analyzing topographic features. Second, an area with a large number of aerial images and a complex topography had a large amount of point clouds to be extracted, and the number of point clouds affects the three-dimensional quality of rural space. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. In this study, the possibility of rural space analysis of low-altitude UAV was verified through aerial photography and analysis, and the effect of 3D mapping on rural space monitoring was visually analyzed. If data acquired by low-altitude UAV are used in various forms such as GIS analysis and topographic map production it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of Internet of Things and Convergence
• /
• v.8 no.1
• /
• pp.59-64
• /
• 2022

Existing edge discovery techniques only found edges of defined shapes based on precise definitions of edges. Therefore, there are many limitations in finding edges for images of complex and diverse shapes that exist in the real world. A method for solving these problems and discovering various types of edges is a cost minimization method. In this method, the cost function and cost factor are defined and used. This cost function calculates the cost of the candidate edge model generated according to the candidate edge generation strategy. If a satisfactory result is obtained, the corresponding candidate edge model becomes the edge for the image. In this study, a new candidate edge generation strategy was proposed to discover edges for images of more diverse shapes in order to improve the disadvantage of only finding edges of a defined shape, which is a problem of the cost minimization method. In addition, the contents of improvement were confirmed through a simple simulation that reflected these points.

• International Journal of Advanced Culture Technology
• /
• v.11 no.3
• /
• pp.65-77
• /
• 2023

Chinese martial arts culture is a kind of Chinese kung fu culture, a cultural category that uses martial arts kung fu for chivalry and justice. Chinese martial arts MMORPG online game is the embodiment of Chinese martial arts culture in online games, which is a unique Chinese online game. The image of beggar chivalry is a special chivalrous image in Chinese martial arts culture, and in the top 3 martial arts MMORPG online games, all of them have the image of beggar chivalry, which shows that this image has a wide player base. The Q methodology is an approach that endeavors to discover complex issues in human subjectivity, unlike existing empirical studies. In order to determine the type of beggar chivalry image preference of the game players, 32 beggar chivalry images were selected in the study and three types of beggar chivalry images were found through the Q method: Type 1 is the type of gorgeous and noble beggar chivalry; Type 2 is a competent type and is good at fighting the beggar's chivalry; and Type 3 is comparable relatively refined type. The result of this study is that the image of beggar chivalry preferred by game players is the opposite of the traditional Chinese image of beggar chivalry. The traditional image of beggar is the image of wearing plain and begging in the street, but the image of beggar chivalry that is liked in online games is luxurious, noble, exquisite and about the image of good at fighting. This research result has some value and significance in the development and design of beggar chivalrous image in future martial arts MMORPG online games.

• Archives of Plastic Surgery
• /
• v.50 no.3
• /
• pp.254-263
• /
• 2023

Background The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method of 3D analysis using a single software, demonstrating high accuracy and repeatability. Methods This retrospective cohort study included 12 patients with skeletal class 3 malocclusion who underwent bimaxillary surgery without any presurgical orthodontics. Computed tomography (CT)/cone-beam CT images of each patient were obtained at three different time points (preoperation [T0], immediately postoperation [T1], and 1 year after surgery [T2]) and reconstructed into 3D images. After automatic surface-based alignment of the three models based on the anterior cranial base, five easily located anatomical landmarks were defined to each model. A set of angular and linear measurements were automatically calculated and used to define the amount of movement (T1-T0) and the amount of relapse (T2-T1). To evaluate the reproducibility, two independent observers processed all the cases, One of them repeated the steps after 2 weeks to assess intraobserver variability. Intraclass correlation coefficients (ICCs) were calculated at a 95% confidence interval. Time required for evaluating each case was recorded. Results Both the intra- and interobserver variability showed high ICC values (more than 0.95) with low measurement variations (mean linear variations: 0.18 mm; mean angular variations: 0.25 degree). Time needed for the evaluation process ranged from 3 to 5 minutes. Conclusion This approach is time-saving, semiautomatic, and easy to learn and can be used to effectively evaluate stability after SFOA.

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