• Journal of radiological science and technology
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• v.44 no.6
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• pp.623-627
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• 2021

Cervical foraminal stenosis is a disease in which the nerves that pass from the spinal canal to the limbs are narrowed and the nerves are compressed or damaged. Due to the lack of an imaging method that provides quantitatively stenosis, this study attempted to evaluate the area of the cervical vertebrae by reconstructing a three-dimensional computed tomography image, and to determine the area of the neural foramen in normal adults to calculate the stenosis rate. Using a three-dimensional image processing program, the surrounding bones including the posterior spinous process, lateral process, and lamellar bones of the cervical vertebra were removed so that the neural foramen could be observed well. A region of interest including the neural foraminal area of the three-dimensional image was set using ImageJ, and the number of pixels in the neural foraminal area was measured. The neural foraminal area was calculated by multiplying the number of measured pixels by the pixel size. To measure the largest neural foraminal area, it was measured between 40~50 degrees in the opposite direction and 15~20 degrees toward the head. The average area of the right C2-3 foramen was 44.32 mm², C3-4 area was 34.69 mm², C4-5 area was 36.41 mm², C5-6 area was 35.22 mm², C6-7 area was 36.03 mm². The average area of the left C2-3 foramen was 42.71 mm², C3-4 area was 32.23 mm², C5-6 area was 34.56 mm², and C6-7 area was 31.89 mm². By creating a reference table based on the neural foramen area of normal adults, the stenosis rate of patients with neural foraminal stenosis could be quantitatively calculated. It is expected that this method can be used as basic data for the diagnosis of cervical vertebral foraminal stenosis.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.57-64
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• 2022

Chloride, which is one of the main deterioration factors in reinforced concrete structures, can degrade the performance of the structure due to chloride-induced corrosion of steel. Chloride content at steel depth or the rate of chloride penetration is necessary to determine deterioration of reinforced concrete or to calculate initiation time of steel corrosion caused by chloride attack. Chlorides in concrete are generally identified with typical two methods including chloride profiling using potentiometric titration method and discoloration method using AgNO₃ solution. The former is advantageous to estimate chloride penetration rate (diffusion coefficient in general) with measured chloride contents directly, but it is laborious. In the case of latter, while the result is obtained easily with the range of discoloration, the error may occur depending on workmanship when the depth of chloride ingress is measured. This study shows that chloride penetrated depth is evaluated with the results obtained from discoloration method through image analysis, thereby the error is minimized by workmanship. In addition, the effect of micro-crack in concrete is studied on chloride penetration. In conclusion, the depth of chloride penetration was quantified with image analysis and as it was confirmed that chlorides can rapidly penetrate through micro-cracks, caution is especially required for cracks in concrete structure.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.131-144
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• 2015

This research proposes a prototype for visualizing radar rainfall data using the unity 3D engine. The mashup of radar data with topographic information is necessary for the 3D visualization of the radar data with high quality. However, the mashup of a huge amount of radar data and topographic data causes the overload of data processing and low quality of the visualization results. This research utilized the Unitiy 3D engine, a widely used engine in the game industry, for visualizing the 3D topographic data such as the satellite imagery/the DEM(Digital Elevation Model) and radar rainfall data. The satellite image segmentation technique and the image texture layer mashup technique are employed to construct the 3D visualization system prototype based on the topographic information. The developed protype will be applied to the disaster-prevention works by providing the radar rainfall data with the 3D visualization based on the topographic information.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.

• Journal of dental hygiene science
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• v.19 no.4
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• pp.238-244
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• 2019

Background: Recently, three-dimensional (3D) printing has been hailed as a disruptive technology in dentistry. Among 3D printers, a digital light processing (DLP) 3D printer has certain advantages, such as high precision and relatively low cost. Therefore, the latest trend in resin crown manufacturing is the use of DLP 3D printers. However, studies on the internal fitness of such resin crowns are insufficient. The recently introduced 3D evaluation method makes it possible to visually evaluate the error of the desired area. The purpose of this study is to evaluate the internal fitness of resin crowns fabricated a by DLP 3D printer using the 3D evaluation method. Methods: The working model was chosen as the maxillary molar implant model. A total of 20 resin crowns were manufactured by dividing these into two groups. One group was manufactured by subtractive manufacturing system (PMMA), while the other group was manufactured by additive manufacturing system, which uses a DLP 3D printer. Resin crowns data were measured using a 3D evaluation program. Internal fitness was calculated by root mean square (RMS). The RMS was calculated using the Geomagic Verify software, and the mean and standard deviation (SD) were measured. For statistical analysis, IBM SPSS Statistics for Windows ver. 22.0 (IBM Corp., USA) was used. Then, independent t-test was performed between the two groups. Results: The mean±SD of the RMS were 41.51±1.51 and 43.09±2.32 for PMMA and DLP, respectively. There was no statistically significant difference between PMMA and DLP. Conclusion: Evaluation of internal fitness of the resin crown made using a DLP 3D printer and subtractive manufacturing system showed no statistically significant differences, and clinically acceptable results were obtained.

• Design & Manufacturing
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• v.14 no.2
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• pp.14-20
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• 2020

In OLED panel production process, it is necessary to cut a part of protective film as a preprocess for lighting inspection. The current method is to recognize only the fiducial mark of the cut-out panel. Bare Glass Cutting does not compensate for machining cumulative tolerances. Even though process defects still occur, it is necessary to develop technology to solve this problem because only the Align Mark of the panel that has already been cut is used as the reference point for alignment. There is a lot of defective lighting during panel lighting test because the correct protective film is not cut on the panel power and signal application pad position. In laser cutting process to remove the polarizing film / protective film / TSP film of OLED panel, laser processing is not performed immediately after the panel alignment based on the alignment mark only. Therefore, in this paper, we performed real time inspection which minimizes the mechanism tolerance by correcting the laser cutting path of the protective film in real time using Machine Vision. We have studied calibration algorithm of Vision Software coordinate system and real image coordinate system to minimize inspection resolution and position detection error and edge detection algorithm to accurately measure edge of panel.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.110-117
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• 2007

The recent developments of audio/video technology in the ubiquitous media have resulted in a variety of formal feature such as screen sizes for media and the new viewing environment can alter television viewers' experience of mediated communication. So the concern about formal feature of media itself is increasing in the recent media effects studies. From this point of view, this study is conducted to test the effect of the different monitor size of DMB Phone(2'1"), PMP(4'3") and PC monitor(19') on the viewers' memory and emotion of content by experimental research. The results showed that the effect by monitor size on the view's memory and emotion is significant. And watching moving image on a bigger monitor resulted in better memory of visual message elements contained in the content and more favorable emotion of the content.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.89-94
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• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.