• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.565-572
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• 2023

This study was conducted to analyze the status radiological imaging examinations assessment in Korea medical institutions conducted in the public sector and suggest a direction for improvement. Among the assessment of medical institutions, the main assessment related to radiographic imaging examinations are the certification evaluation of medical institutions and the adequacy assessment of radiographic imaging examinations. The certification evaluation of medical institutions evaluates the image inspection operation process, provision of accurate results, and compliance with safety management procedures. In the assessment of adequacy of radiographic imaging examinations, structural indicators related to manpower and equipment, patient evaluation implementation rate, and exposure reduction programs were included. However, for safer and higher-quality radiological imaging examinations, it is necessary to increase the participation rate of medical institutions in certification evaluations. In addition, it is necessary to improve the manpower indicator, and incentive payments can be considered to induce quality improvement of medical institutions in the future. Integrated management of radiation exposure at the national level should also be carried out simultaneously.

• Journal of the Korean Geotechnical Society
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• v.24 no.2
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• pp.59-65
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• 2008

Digital close-range photogrammetric technique can measure and describe 3D geometric farm from 2D image. This technique is increasingly applied in the field of sciences. In the fields of civil and mechanical engineering, which need precise measurements for design, expensive measuring equipments are widely used. In occasions where visual inspection is required in addition to other forms of measurements, appropriate measuring equipments have not been yet available. This study utilizes digital close-range photogrammetric technique to quantitatively analyze behavior patterns before and after destruction from test model of reinforced-soil wall. Then the results are compared with the measurements obtained using digital theodolite to verify the reliability of the proposed method.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Journal of Digestive Cancer Research
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• v.12 no.2
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• pp.90-101
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• 2024

The incidence of colon cancer in South Korea has recently been the highest among gastrointestinal cancers. Early diagnosis is critical, and image-enhanced endoscopy (IEE) is a key diagnostic method. Colon tumors primarily include serrated polyps, adenomatous polyps, and colon cancer. Early endoscopic techniques relied on simple visual inspection for diagnosis, with tumor size and shape being the primary considerations. Low-resolution images made these methods ineffective for detecting small or early-stage lesions. IEE now enables detailed examination using high-resolution images and various color and structure analyses. Techniques like narrow band imaging (NBI) allow precise observation of vascular patterns and surface structures. Hyperplastic polyps often appear similar in color to the surrounding mucosa, with no visible vascular pattern. Sessile serrated lesions have a cloudy surface with distinct boundaries and irregular patterns, often with black spots in the crypts. Adenomatous polyps are darker brown, with a visible white epithelial network and various pit patterns. Magnified images help differentiate between low- and high-grade dysplasia, with low-grade showing regular patterns and high-grade showing increased irregularities. The NBI International Colorectal Endoscopic classification identifies malignant colon tumors as brown or dark brown with disorganized vascular patterns. The Japan NBI Expert Team classification includes loose vascular areas and disrupted thick vessels. The Workgroup serrAted polypS and Polyposis classification aids in differentiating between hyperplastic polyps and sessile serrated lesions/adenomas when deciding whether to resect polyps larger than 5 mm. Suspected high-grade dysplasia warrants endoscopic submucosal dissection and follow-up. Future advancements in IEE are expected to further enhance early detection and diagnostic accuracy.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.595-605
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• 2012

Various methods for measuring strabismus have been developed and used in clinical diagnosis. However, most of them are based on the visual inspection by clinicians. For this reason, there is a high possibility of subjective evaluation in clinical decisions and they are only useful for cooperative patients. Therefore, the development of a more objective and reproducible method for measuring strabismus is needed. In this paper, we introduce a new software to complement the limitations of previous diagnostic methods. Firstly, we simply obtained facial images of patients and performed several preprocessing steps based on the spherical RGB color model with them. Then, the measurement of strabismus was performed automatically by using our 3D eye model and mathematical algorithm. To evaluate the validity of our software, we performed statistical correlation analysis of the results of the proposed method and the Krimsky test by two clinicians for ten patients. The coefficients of correlation for two clinicians were very high, 0.955 and 0.969, respectively. The coefficient of correlation between two clinicians also showed 0.968. We found a statistically significant correlation between two methods from our results. The newly developed software showed a possibility that it can be used as an alternative or effective assistant tool of previous diagnostic methods for strabismus.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.110-114
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• 2017

Because the arm can't be sutured due to fracture during a elbow CT scan, a CT scan is proceeded in a state of abdomen and L-spire are overlapped which beam hardening artifact is done many times, and it often lowers the quality of elbow CT images. So there are many difficulties in reading and due to increase in radiation dose from it, the number of patient's exposure keeps increasing. In this research, it plans to improve the quality of the images by avoiding overlap with abdomen, and increasing the number of photon overlapped with lung field which the line attenuation is relatively small. The way of experiment is based on patient's right elbow and place him as head first position, then place his elbow at L2-3 level in supine position, turn about 30 degrees to the left in non-control breathing and in supine position, and compared with full inspiration after overlapping with lung. After figuring out the average value and standard deviation data using Image J program 5 times each for 16, 128 channels, the evaluation is proceeded by measuring each of CNR, MSR are statistically analyzed using SPSS program. Therefore, through positioning and inspiration during elbow CT scan, the way of inspection minimized the exposure radiation dose, and seems to be meaningful in a way to improve the quality of the images.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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• pp.222-232
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• 2004

The sample collection room(SCR) will have much more influence than all the other departments for the improvement of hospital image, if anyone coming to the SCR in the hospital goes back with the perfect complacency and because most clients who have much stresses and fatigues pay a final visit to the SCR via receipt-diagnosis- acceptance process. SCR has improved its image for the purpose of gratifying clients, in order for clients to visit the hospital again, the quality improvement(QI) team in the Diagnosis Inspection Medical Department has come to a conclusion as follows. The degree of client gratification before improvement marks 65.9 point, but the degree after improvement was 74.2 point. Therefore, satisfaction has increased by 8.3 points. The degree of client gratification in groups before improvement marks (1) service parts-89.2 points (2) facilities and environments-49.1 point (3) toilet facilities-46.3 point. But its gratification after improvement marks (1) 92.5 point (2) 60.1 point (3) 61.0 point. Therefore the degree of satisfaction has increased by (1) 3.3 point, (2) 11.0 point, (3) 14.7 point. The progress of facility improvement plans and the exclusion of improvement on the facility contents in the hospital have made facilities and environments of SCR and toilet facilities to be poorly improved. Although service parts have a good mark, and the facilities and environments are not scoring well, the whole degree clients' gratification of SCR couldn't be helped by the low grade. Therefore the bottom line for the clients' gratification of SCR in the future is to ameliorate the facilities and environments. SCR will take the clients' gratification survey every year and if any items get low marks, that is, below 90 point throughout the survey, SCR will immediately starts the improvement work for the clients' gratification with operating the programs of controlling quality continually, and SCR should induce the operation of services, participating in the kind campaign drive for clients. So SCR will adopt the incentive system for the best staff members who perform these kinds of services.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.209-216
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• 2019

The purpose of this study was to suggest the optimum ambient illumination level for proper visualization in image inspection and reading on CRT and LCD monitors used for ultrasound and reading. The evaluators were divided into 4 groups: 20 (Ultra-sonographer: 20 groups (4 groups: ultra-sonographer, 1-5 years, 5 ultra-sonographers, 6 to 10 years, 5 ultra-sonographers, 11 to 15 years, The subjects were 32 questions. The evaluation method was image evaluation of ultrasonic soft copy images for 30 seconds per 10, 25, 100Lux ambient illumination. The evaluation results were evaluated as 6 points (Normal = Definitely no lesion), 2 points = possibly not a lesion, 3 points = probably not a lesion, 4 points = possibly a lesion, 5 points = probably a lesion, 6 points = Definitely a lesion). In this study, the results of ROC analysis according to ambient light illumination reading softcopy images used for lesion detection of all ultrasound images showed the highest sensitivity, specificity, and AUC results at 10Lux. It was found that optimal use of 10Lux for ambient light illumination would provide optimal detection of lesions in ultrasound soft copy images. Based on the future research data, it will be presented as basic data for designing ambient light brightness of ultrasound imaging laboratory and reading room.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.105-111
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• 2019

Core technologies that lead the Fourth Industrial Revolution era, such as artificial intelligence, big data, and autonomous driving, are implemented and serviced through the rapid development of computing power and hyper-connected networks based on the Internet of Things. In this paper, we implement two different models for drivable area segmentation in various environment, and propose a better model by comparing the results. The models for drivable area segmentation are using DeepLab V3+ and Mask R-CNN, which have great performances in the field of image segmentation and are used in many studies in autonomous driving technology. For driving information in various environment, we use BDD dataset which provides driving videos and images in various weather conditions and day&night time. The result of two different models shows that Mask R-CNN has higher performance with 68.33% IoU than DeepLab V3+ with 48.97% IoU. In addition, the result of visual inspection of drivable area segmentation on driving image, the accuracy of Mask R-CNN is 83% and DeepLab V3+ is 69%. It indicates Mask R-CNN is more efficient than DeepLab V3+ in drivable area segmentation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.