• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.487-501
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• 2017

A machine vision system (MVS) is a computer system that utilizes one or more image-capturing devices to provide image data for analysis and interpretation. Recently there have been a number of industrial- and medical-device applications where control charts have been proposed for use with image data. The use of image-based control charting is somewhat different from traditional control charting applications, and these differences can be attributed to several factors, such as the type of data monitored and how the control charts are applied. In this paper, we investigate the adjustment effect of image size and region of interest (ROI) size, when we use control charts to monitor grayscale image data in industry.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.29-38
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• 2012

The noise generated in the process of obtaining the medical image acts as the element obstructing the image interpretation and diagnosis. To restore the true image from the image polluted from the noise, the total variation optimization algorithm was proposed by the R.O. F (L.Rudin, S Osher, E. Fatemi). This method removes the noise by fitting the balance of the regularity and fidelity. However, the blurring phenomenon of the border area generated in the process of performing the iterative operation cannot be avoided. In this paper, we propose the adaptive total variation method by mapping the control parameter to the proposed transfer function for minimizing boundary error. The proposed transfer function is determined by the noise variance and the local property of the image. The proposed method was applied to 464 tooth images. To evaluate proposed method performance, PSNR which is a indicator of signal and noise's signal power ratio was used. The experimental results show that the proposed method has better performance than other methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.217-219
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• 2019

In response to the increased use of digital video device, more researches are actively made on the image processing technologies. Image processing is practically used on various applied fields such as medical photographic interpretation, and object recognition. The types of image noise include Gaussian Noise, Impulse Noise, and Salt and Pepper. Noise refers to the unnecessary information which damages the video and the noise is mainly removed by a filter. Typical noise removal methods are Median Filter and Average Filter. While Median Filter is effective for removing Salt and Pepper noise, the noise removal performance is relatively lower in the environment with high noise density. To address such issue, this study suggested an algorithm which utilizes neighboring pixels to remove noise.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• The Korean Journal of Nuclear Medicine
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• v.34 no.5
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• pp.393-402
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• 2000

Purpose: Tc-99m-MIBI (MIBI) and Tc-99m-Tetrofosmin (TF) are commonly used for scintimammog (SMM). We compared the diagnostic ability of SMM using Tc-99m-MIBI and Tc-99m-TF for the diagnosis of breast mass. Materials and Methods: The study subjects were comprised of 123 breast lesior 86 normal breasts of 114 patients who underwent SMM. Bilateral prone images and anterior supine images obtained at 5 minutes and 1 or 3 hours after intravenous injection of 740 MBq of either MIBI or TF. of tumors were not significantly different between the MIBI and TF groups. First, two observers read the SMM without clinical information (1st interpretation), then read again with information about location (2nd interpretation). Sensitivity and specificity of each radiopharmaceutical for the diagnosis of cancer were evaluated in terms of image acquisition time, tumor size, and location. Results: The SMM a good agreement between two observers for 1st and 2nd interpretation, except for TF SMM at 3 hr. first interpretation, the sensitivities at 5 min, 1 hr, and 3 hr were not significantly different between MIBI TF SMM (81.6%, 80.0%, 60.9% in MIBI vs. 88.9%, 80.6%, 42.9% in TF), although the sensitivities of images were significantly lower than 5 min images in both MIBI and TF SMM. The specificity of TF at was superior to that of MIBI (81.5%, 90.0%, 82.9% in MIBI vs. 96.7%, 100%, 90.0% in TF, p<0.01 MIBI TF at 5 min). For the second interpretation with information of mass location, the sensitivities at 3 hr were significantly lower than 5 min images (86.8%, 86.7%, 78.3% in MIBI vs. 88.9%, 93.5%, 57.1% between MIBI and TF SMM. However, there was no significant difference in the specificity (60.0%, 75.0% for MIBI vs. 86.7%, 100%, 100% for TF). MIBI and TF SMM showed lower sensitivities for the with less than 1 cm than tumors with more than 1 cm. However, the location of tumors did not sensitivity and specificity between MIBI and TF SMM. Conclusion: The ability for the differential of breast tumor is similar between MIBI and TF SMM, and delayed image is not necessary. TF may be than MIBI considering the specificity of SMM without clinical information and labeling convenience.

• The Korean Journal of Nuclear Medicine
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• v.23 no.2
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• pp.195-200
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• 1989

The authors studied to evaluate the difference of the diagnostic performance between reading from digital image on the video CRT of PACS (Picture Archiving and Communication System) and from analogue image of conventional film mode. We compared interpretative accuracy of above two reading modes by having two observers read a series of liver scintigrams. Images were read once from film and a second time from video CRT of elementary PACS. The concordance rate of interpretation of the two modes was in the range from 61.0% to 93.2%. The diagnostic accuracies of digital image reading and analogue image reading were 72.9% and 74.6% respectively in one observer, and 72.9% and 76.3% in another one. No significant difference in interpretative accuracy could be found between two modes of reading.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.345-362
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• 2024

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

• Korean Journal of Digital Imaging in Medicine
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• v.11 no.2
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• pp.63-68
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• 2009

Among various physical or subjective assessments of the quality of X-ray images, physical assessments can be quantitative but they are eventually judged by the view of observers thus subjective assessments including the aspect of observers are required. The changes in the ability to detect lesions caused by changes in the thickness of acrylic plates were tested with the ROC interpretation method that has taken into consideration, all the features of physical assessments as well as observers' ability to observe and mental stages and even surrounding environments using an Alvim phantom and the result indicated that as the thickness of acrylic plates increased, the amount of noises occurred increased compared to signals and thus the ability to detect signals as well as the sensitivity that is an ability to signals accurately and the ability to distinguish noises from signals thus it is considered that more efforts of radiologic technologists will be required to detect small lesions of fat patients with diagnostic X-ray generating apparatus.

• Clinics in Shoulder and Elbow
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• v.25 no.3
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• pp.182-187
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• 2022

Background: This study was designed to evaluate and compare the diagnostic value of magnetic resonance imaging (MRI) and indirect magnetic resonance arthrography (I-MRA) imaging with those of arthroscopy and each other. Methods: This descriptive-analytical study was conducted in 2020. All patients who tested positive for labrum lesions during that year were included in the study. The patients underwent conservative treatment for 6 weeks. In the event of no response to conservative treatment, MRI and I-MRA imaging were conducted, and the patients underwent arthroscopy to determine their ultimate diagnosis and treatment plan. Imaging results were assessed at a 1-week interval by an experienced musculoskeletal radiologist. Image interpretation results and arthroscopy were recorded in the data collection form. Results: Overall, 35 patients comprised the study. Based on the kappa coefficient, the results indicate that the results of both imaging methods are in agreement with the arthroscopic findings, but the I-MRA consensus rate is higher than that of MRI (0.612±0.157 and 0.749±0.101 vs. 0.449±0.160 and 0.603±0.113). The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of MRI in detecting labrum tears were 77.77%, 75.00%, 91.30%, 50.00%, and 77.14%, respectively, and those of I-MRA were 88.88%, 75.00%, 92.30%, 66.66%, and 85.71%. Conclusions: Here, I-MRA showed higher diagnostic value than MRI for labral tears. Therefore, it is recommended that I-MRA be used instead of MRI if there is an indication for potential labrum lesions.