• Progress in Medical Physics
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• v.14 no.2
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• pp.131-140
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• 2003

As the importance of accuracy in measurings of 3-D anatomical structures continues to be stressed, an objective and quantitative of assessing image quality and accuracy of 3-D volume-rendered images is required. The purpose of this study was to evaluate the quantitative accuracy of 3-D rendered images obtained with MDCT, scanned at various scanning parameters (scan modes, slice thicknesses and reconstruction slice thickness). Twelve clinically significant points that play an important role for the craniofacial bone in plastic surgery and dentistry were marked on the surface of a dry human skull. The direct distances between the reference points were defined as gold standards to assess the measuring errors of 3-D images. Then, we scanned the specimen with acquisition parameters of 300 mA, In kVp, and 1.0 sec scan time in axial and helical scan modes (pitch 3:1 and 6:1) at 1,25 mm, 2.50 mm, 3.75 mm and 5.00 mm slice thicknesses. We performed 3-D visualizations and distance measurements with volumetric analysis software and statistically evaluated the quantitative accuracy of distance measurements. The accuracy of distance measurements on the 3-D images acquired with 1.25, 2.50, 3,75 and 5.00 mm slice thickness were 48%, 33%, 23%, 14%, respectively, and those of the reconstructed 1.25 mm were 53%, 41%, 43%, 36% respectively. Meanwhile, there were insignificant statistical differences (P-value<0.05) in the accuracy of the distance measurements of 3-D images reconstructed with 1.25 mm thickness. In conclusion, slice thickness, rather than scan mode, influenced the quantitative accuracy of distance measurements in 3-D rendered images with MDCT. The quantitative analysis of distance measurements may be a useful tool for evaluating the accuracy of 3-D rendered images used in diagnosis, surgical planning, and radiotherapeutic treatment.

• Atmosphere
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• v.24 no.3
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• pp.365-378
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• 2014

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.12
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• pp.1669-1674
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• 2005

Maximum likelihood methodology was applied to analyze the efficiency and statistical power of interval mapping by using a threshold model. The factors that affect QTL detection efficiency (e.g. QTL effect, heritability and incidence of categories) were simulated in our study. Daughter design with multiple families was applied, and the size of segregating population is 500. The results showed that the threshold model has a great advantage in parameters estimation and power of QTL mapping, and has nice efficiency and accuracy for discrete traits. In addition, the accuracy and power of QTL mapping depended on the effect of putative quantitative trait loci, the value of heritability and incidence directly. With the increase of QTL effect, heritability and incidence of categories, the accuracy and power of QTL mapping improved correspondingly.

• The Korean Journal of Nuclear Medicine Technology
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• v.28 no.1
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• pp.57-69
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• 2024

Purpose: Quantitative analysis through count measurement in nuclear medicine planar images is limited by analysis techniques that are useful for obtaining various clinical information or by organ overlap or artifacts in actual clinical practice. On the other hand, the use of SPECT tomography images is quantitative analysis using volume rather than planar, which is not only free from problems such as projection overlap, but also has excellent quantitative accuracy. In the use of developing SPECT quantitative analysis technology, this study aims to compare the accuracy of quantitative analysis between ROI of the conventional planar images and VOI of the SPECT tomographic images in evaluating the count change happened by the volume change of the source. Materials and Methods: A ^99mTcO₄^- source(200.17 MBq) was filled with sterilized water in the syringe to create a phantom with an inner diameter volume of 60 cc, and a planar image and a SPECT image were obtained by reducing the volume by 15 cc (25%) respectively. ROI and VOI(threshold: 1~45%, 5% interval) were set for each image obtained to estimate true count and measure the total count, and compared with the preseted volumetric change rate(%). Results: When volume changes of 25%, 50%, and 75% occurred in the initial volume of 60 cc(100%) of the phantom, the average count changes of the measured planar image were 26.8%, 53.2%, 77.5%, and the average count changes of the SPECT image were 24.4%, 50.9%, and 76.8%. In this case, the VOI size(cm³) set showed an average change rate of 25.4%, 51.1%, and 76.6%. The highest threshold value for the accuracy of radioactive concentration by VOI size (average error -1.03%) was 35%, and the VOI size of the same threshold had an error of -17.1% on average compared to the actual volume. Conclusion: On average, the count-based volumetric change rate in nuclear medicine images was able to track changes more accurately using VOI than ROI, but there was no significant difference with relatively similar value. However, the accuracy of radioactive concentration according to individual VOI sizes did not match, but it is considered that a relatively accurate quantitative analysis can be expected when the size of VOI is set smaller than the actual volume.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.293-294
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• 2010

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.235-235
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• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.335-346
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• 2017

This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin. As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved through the adjustment technique and blending with NWP.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6331-6335
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• 2013

Objective:To evaluate the performance of combined quantitative analysis of thyroid blood flow and static imaging data in the differential diagnosis of thyroid nodules. Method: Thyroid blood flow and static imaging were performed in 165 patients with thyroid nodules. Patients were divided into a benign thyroid nodule group (BTN, n=135) and a malignant thyroid nodule group (MTN, n=30) based on the results of post-surgical pathologic examination. Carotid artery thyroid transit times (CTTT), perfusion ratio of thyroid nodule blood/thyroid blood (TNB/TB), and perfusion ratio of thyroid nodule blood/carotid artery blood (TNB/CAB) were measured using thyroid blood flow imaging. The ratios between thyroid nodule and ipsilateral submandibular gland (TN/SG) and thyroid nodule and normal thyroid tissue (TN/T) were measured from thyroid static imaging. The differences between the BTN and MTN groups were compared. Results: 1) CTTT was markedly lower in the MTN group than the BTN group, the difference being statistically significant. 2) TNB/TB and TNB/CAB were both significantly higher in MTN than BTN groups. 3) TN/T was significantly lower in MTN group than BTN group. 4) TN/SG was lower in MTN group than BTN group, but the difference was not statistically significant. 5) Using the combination of CTTT and TN/T, the sensitivity, specificity and accuracy were 93.1%, 95.3% and 94.9% respectively for the diagnosis of MTN. Using the combination of CTTT, TNB/TB and TN/T, the sensitivity, specificity and accuracy changed to 89.7%, 100%, and 98.1% respectively. 6) Correlation analysis demonstrated a significant correlation between TN/T and TNB/TB (r=-0.384, P=0.036) and TNB/CAB (r=-0.466, P=0.009) in the MTN group. Conclusion: The combination of quantitative markers from thyroid blood flow and thyroid static imaging had high specificity and accuracy in differential diagnosis of benign and malignant thyroid nodules, thus providing an important imaging diagnostic approach.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.669-678
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• 2023

Recently, spatial information has been applied to various fields and its usability is increasing day by day. In particular, in the field of civil engineering and construction, BIM based on spatial information is being applied to all construction industries and related research has been conducted. BIM is a technology that utilizes spatial information from the design phase and aids in the construction and maintenance of buildings, including the management of their attributes. However, to apply BIM technology to existing buildings, it takes a lot of time and money to produce models based on design drawings along with current surveying. In this study, quantitative and qualitative analysis was conducted to determine the applicability of the acquired data and the applicability of BIM by generating data and analyzing the accuracy using UAV images and ground lidar, which are representative spatial information acquisition methods. Quantitative analysis revealed that TLS (Terrestrial Laser Scanner) showed reliable accuracy in both planar and elevation measurements, whereas unmanned aerial images exhibited lower accuracy in elevation measurements, resulting in reduced reliability. Qualitative analysis indicated that neither TLS nor unmanned aerial images alone provided perfect completeness. However, the combination of both spatial information sources, tailored to specific needs, resulted in the most comprehensive completeness. Therefore, it is concluded that the appropriate utilization of spatial information acquired through unmanned aerial images and TLS holds the potential for application in the fields of BIM and reverse engineering.

• Journal of radiological science and technology
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• v.46 no.6
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• pp.535-542
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• 2023

This study aimed to implement a deep learning model that can perform quantitative quality control through ACTS software used for quantitative evaluation of ACR phantom in CT quality control and evaluate its usefulness. By changing the scanning conditions, images of three modules of the ACR phantom's slice thickness (ST), low contrast resolution (LC), and high contrast resolution (HC) were obtained and classified as ACTS software. The deep learning model used ResNet18, implementing three models in which ST, HC, and LC were learned with epoch 50 and an integrated model in which three modules were learned with Epoch 10, 30, and 50 at once. The performance of each model was evaluated through Accuracy and Loss. When comparing and evaluating the accuracy and loss function values of the deep learning models by ST, LC, and HC modules, the Accuracy and Loss of the HC model were the best with 100% and 0.0081, and in the integrated model according to the Epoch value, Accuracy and Loss with epoch 50 were the best with 96.29% and 0.1856. This paper showed that quantitative quality control is possible through a deep learning model, and it can be used as a basis and evidence for applying deep learning to the CT quality control.