• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.689-693
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• 2023

Domestic bridges with a service life of more than 30 years are expected to account for approximately 54% of all bridges within the next 10 years. As bridges rapidly deteriorate, it is necessary to establish an appropriate maintenance plan. Recent domestic and international research have focused on the integration of BIM to digitize bridge maintenance information and then enhance accessibility and usability of the information. Accordingly, this study developed a BIM-FEM interoperability algorithm for bridge decks to convert maintenance information into data and efficiently manage the history of maintenance. After creating an initial crack BIM based on an exterior damage map, bridge specification and damage information were linked to a numerical analysis that performs damage analysis considering damage scenarios and design loads. The spread of cracks obtained from the analysis results were updated into the BIM. Based on the damage spread information on the BIM, an automated technology was also developed to assess both the current and future condition ratings of the bridge deck. This approach can enable an efficient maintenance of the deck using the history data from bridge inspection and diagnosis as well as future information on cracks and defects. The expected early detection and prevention would ultimately improve the lifespan and safety of bridges.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.81-91
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• 2024

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.3
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• pp.254-259
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• 2014

A pressurized heavy water reactor (PHWR) core has 380 fuel channels contained and supported by a horizontal cylindrical vessel known as the calandria, whereas a pressurized water reactor (PWR) has only a single reactor vessel. The pressure tube, which is a pressure-retaining component, has a 103.4 mm inside diameter ${\times}$ 4.19 mm wall thickness, and is 6.36 m long, made of a zirconium alloy (Zr-2.5 wt% Nb). This provides support for the fuel while transporting the $D_2O$ heat-transfer fluid. The simple tubular geometry invites highly automated inspection, and good approach for all inspection. Similar to all nuclear heat-transfer pressure boundaries, the PHWR pressure tube requires a rigorous, periodic inspection to assess the reactor integrity in accordance with the Korea Nuclear Safety Committee law. Volumetric-based nondestructive evaluation (NDE) techniques utilizing ultrasonic and eddy current testing have been adopted for use in the periodic inspection of the fuel channel. The eddy current testing, as a supplemental NDE method to ultrasonic testing, is used to confirm the flaws primarily detected through ultrasonic testing, however, eddy current testing offers a significant advantage in that its ability to detect surface flaws is superior to that of ultrasonic testing. In this paper, effectiveness of flaw detection and the depth sizing capability by eddy current testing for the inside surface of a pressure tube, will be introduced. As a result of this examination, the ET technique is found to be useful only as a detection technique for defects because it can detect fine defects on the surface with high resolution. However, the ET technique is not recommended for use as a depth sizing method because it has a large degree of error for depth sizing.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• Progress in Medical Physics
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• v.26 no.4
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• pp.258-266
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• 2015

The modern radiotherapy technique which delivers a large amount of dose to patients asks to confirm the positions of patients or tumors more accurately by using X-ray projection images of high-definition. However, a rapid increase in patient's exposure and image information for CT image acquisition may be additional burden on the patient. In this study, by introducing structural similarity (SSIM) index that can effectively extract the structural information of the image, we analyze the differences between daily acquired x-ray images of a patient to verify the accuracy of patient positioning. First, for simulating a moving target, the spherical computational phantoms changing the sizes and positions were created to acquire projected images. Differences between the images were automatically detected and analyzed by extracting their SSIM values. In addition, as a clinical test, differences between daily acquired x-ray images of a patient for 12 days were detected in the same way. As a result, we confirmed that the SSIM index was changed in the range of 0.85~1 (0.006~1 when a region of interest (ROI) was applied) as the sizes or positions of the phantom changed. The SSIM was more sensitive to the change of the phantom when the ROI was limited to the phantom itself. In the clinical test, the daily change of patient positions was 0.799~0.853 in SSIM values, those well described differences among images. Therefore, we expect that SSIM index can provide an objective and quantitative technique to verify the patient position using simple x-ray images, instead of time and cost intensive three-dimensional x-ray images.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7825-7829
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• 2015

Background: Egypt has the highest prevalence of HCV infection in the world (~14.7%). Around 10-15% of HCV-infected persons will advance to cirrhosis within the first 20 years. The incidence of HCC is expected to grow in the next two decades, largely due to HCV related cirrhosis, and detection of HCC at an early stage is critical for a favorable clinical outcome. No simple reliable non-invasive marker has been available till now. B2M, a non-glycosylated polypeptide composed of 99 amino acids, is one of the components of HLA class I molecules on the surfaces of all nucleated cells. It has been reported that the level of serum B2M is elevated in patients with chronic hepatitis C and HCV-related HCC when compared to HCV-negative patients or healthy donors. Determining the clinical utility of serum B2M as a marker for disease progression in Egyptian patients with HCV related chronic hepatitis, cirrhosis and hepatocellular carcinoma was the aim of the present study. Materials and Methods: In this analytical cross sectional study 92 participants were included in 4 equal groups: Group (1) non cirrhotic chronic HCV; Group (2) HCV related liver cirrhosis; Group (3) HCC on top of HCV,; and Group (4) healthy controls. History taking, clinical examination, routine labs and abdominal ultrasound were conducted for all patients, PCR and Metavir scores for group (1) patients, and triphasic CT abdomen and AFP for Group (3) patients. B2M levels were measured in serum with a fully-automated IMX system. Results: The mean serum B2M level of Group (1) was $4.25{\pm}1.48{\mu}g/ml$., Group (2) was $7.48{\pm}3.04$, Group (3) was $6.62{\pm}2.49$ and Group (4) was $1.62{\pm}0.63$. Serum B2M levels were significantly higher in diseased than control group (p<0.01) being significantly higher in cirrhosis ($7.48{\pm}3.04$) and HCC groups ($6.62{\pm}2.49$) than the HCV group ($4.25{\pm}1.48$) (p<0.01). There was a significant correlation between B2M Level and ALK, total and direct bilirubin and INR (p<0.05), and a significant inverse correlation between B2M level and albumin, total proteins, HB andWBCS values (p<0.05). There was no significant correlation between B2M level and viral load or Metavir score, largest tumour size or AFP (p>0.05). The best B2M cut-off for HCV diagnosis was 2.6 with a sensitivity of 100%, a specificity of 92%, a positive predictive value (PPV) of 97% and a negative predictive value (NPV) of 100%. The best B2M cut-off for HCC diagnosis was 4.55 which yielded sensitivity, specificity, positive predictive value, negative predictive values of 74%, 62%, 39.5, 87.8% respectively (p-value <0.01) while best cut-off for cirrhosis was 4.9, with sensitivity 74 % and specificity 74%.The sensitivity for HCC diagnosis increased upon B2M and AFP combined estimation to 91%, specificity to 79%, NPV to 95% and accuracy to 83%. Conclusions: Serum B2M level is elevated in HCV related chronic liver diseases and may be used as a marker for HCV disease progression towards cirrhosis and carcinoma.

• Tuberculosis and Respiratory Diseases
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• v.53 no.6
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• pp.635-643
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• 2002

Background : The Aim of this study was to compare the recovery of mycobacteria from sputum samples of pulmonary tuberculosis patients using the MB/BacT rapid culture system(Organon Teknika, USA) with that obtained using Lowenstein-Jensen solid medium. Methods : The two culture systems were compared using sputum samples of 99 pulmonary tuberculosis patients. Culture media were incubated at $35-37^{\circ}C$ for six weeks in the MB/BacT system and for 12 weeks in Lowenstein-Jensen solid medium. Solid media were examined macroscopically once a week, and the MB/BacT system positive vials were unloaded from the machine as soon as possible after positive signal from the connected computer was detected Confirmation of growth for mycobacteria was done by Ziehl-Neelson stained smears. Isolates were identified to differentiate Mycobacterium tuberculosis from mycobacterium other than tuberculosis(MOTT) by phenotypic and molecular methods. Results : Of the sputum samples of the 99 patients, 58 samples were smear positive and 41 in negative smear. Mycobacteria were recovered from 67(67.7%) samples by using both culture systems. The yield with MB/BacT was higher than that with Lowenstein-Jensen [67(67.7%) vs. 52(52.5%), p<0.001]. Moreover, 15(15.2%) samples were positive only in the MB/BacT, whereas none of samples was positive only in Lowenstein-Jensen. In smear-positive and smear-negative samples, the recovery rate with MB/BacT was also higher than that with Lowenstein-Jensen [sputum-positive; 56/58(96.6%) vs. 46/58(79.3%), p=0.005, sputum-negative; 6/41(14.6%) vs. 5/41(12.2%), p<0.001]. The mean times to detection of Mycobacteria were 13.3 and 27.2 days with MB/BacT and Lowenstein-Jensen respectively(p<0.001). Conclusion : This results indicate that the the MB/BacT is more efficient and faster than Lowenstein-Jensen for the recovery of mycobacteria.