• Journal of radiological science and technology
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• v.23 no.1
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• pp.13-19
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• 2000

We have presented with the "A study on overexposure rate according to over-density in chest X-ray radiography(I)" last year. In this report, We could calculate the entrance skin dose from chest X-ray film density the formula $I_0=Ix/e^{-{\mu}x}{\times}mG$, (mG is Bucky factor) was used to deliver the skin dose. At that time, There was two problems that the Bucky factor from maker was not equal to field experience and the field size influenced on the Attenuation Rate. The experiment of Bucky factor was done from film method and retried the Attenuation Rate of Acryle phantom according to Good & Poor geometry. As the results, The Bucky factor from maker higher than in this experiments $30{\sim}40%$. The Attenuation Rate in good geometric condition brings about a little alteration compare with poor geometric condition. In the field experiment, we could get the chest image with very low entrance skin radiation dose $29.3{\mu}Sv$, especially with air gap methode, the entrance skin dose was detected $10{\mu}Sv$.

• Journal of Radiation Industry
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• v.10 no.4
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• pp.249-255
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• 2016

This study, Seoul City shelter, you are trying to seek medical cooperation and cure rate increase proposal Yu findings's current situation and tuberculosis of homeless tuberculosis. Inspector, and has a total 591 people is targeted to implement an interview after acquiring utilization agreement in studies conducted chest X-ray photography. Of the interview questions, three or more protons, it is determined that the TB symptomatic conducted sputum examination, chest X-ray examination confirms the physician radiology, when sputum examination primarily chromatic findings the double implemented and conducted by requesting the ship inspection also said inspection sputum acid-fast bacteria if it is true one, respectively. confirmed case result of checking whether there is a difference due to risk factors(Jb) at the chi square black, it was found that there is no statistically significant difference at 95% confidence level. (${\chi}^2=0.276$, p>0.05), suspected case (Ac, Ae) results of examining whether there is a difference due to risk factors in chi square black, that there is a statistically significant difference at 99% confidence level is I found (${\chi}^2=9.414$, p<0.01). The nature of the homeless tuberculosis screening and directed to the distance homeless specific location are likely to evaluate the actual incidence low and aggressive or management needs, the rationale is allowed insufficient reality is. Through this research, future, for tuberculosis high risk tuberculosis patient, such as homeless to expand the tuberculosis screening of infectious tuberculosis patients in private medical institutions, and one-stop service that chest X-ray examination and sputum examination is carried out at the same time introduced immediately to prevent the inspection and examination, cure, and should establish a foundation that can be up to post administration.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.212-218
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• 2020

There are dozens of different types of lesions that can be diagnosed through chest X-ray images, including Atelectasis, Cardiomegaly, Mass, Pneumothorax, and Effusion. Computed tomography(CT) test is generally necessary to determine the exact diagnosis and location and size of thoracic lesions, however computed tomography has disadvantages such as expensive cost and a lot of radiation exposure. Therefore, in this paper, we propose a deep learning algorithm for judging the presence or absence of lesions in chest X-ray images as the primary screening tool for the diagnosis of thoracic lesions. The proposed algorithm was designed by comparing various configuration methods to optimize the judgment of presence of lesions from chest X-ray. As a result, the evaluation rate of lesion presence of the proposed algorithm is about 1% better than the existing algorithm.

• The Korean Journal of Nuclear Medicine
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• v.33 no.4
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• pp.381-387
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• 1999

Purpose: The International Labor Organization (ILO) has established an international standard for chest X-ray diagnosis of pneumoconiosis since 1980. However, there is a need for improved diagnosis and staging in occupational disease. We evaluated Ga-67 citrate scintigraphy quantitatively and correlated the scintigraphic findings with pulmonary function tests and chest X-ray results. Materials and Methods: Twenty-five patients underwent whole body scintigraphy with additional chest and abdomen images 48 hrs after intravenous injection of 185 MBq of Ga-67 citrate. Ten normal controls were also studied. Regions of interest (ROI) were drawn on the posterior image to measure counts from the liver and lungs (Lung/Liver Ratio). Results: L/L ratio according to the stages of chest X-ray classification were as follows; stage 0 (normal, n=10): $0.3948{\pm}0.0692$, stage 1 (n=10): $0.5763{\pm}0.1537$, stage 2 (n=11), $0.6849{\pm}0.1459$, stage 3 (n=4) $0.9913{\pm}0.0712$. There was a significant correlation between the scintigraphic L/L ratio and the X-ray stage (r=0.618, p<0.05). However, no significant correlation between L/L ratio and pulmonary function tests were observed (p>0.05). Conclusion: Quantitative Ga-67 scintigraphy can be a useful method for staging of silicosis. However, it is not a method to assess pulmonary functional impairment.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.139-145
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• 2021

This study is to confirm the range of tube voltage for Chest X-ray in DR system by comparing with dose area product (DAP) and effective dose in efficient detector exposure index (DEI) range. GE definium 8000 was used to for the phantom study. The range of tube voltage is 60~130 kVp and of mAs is 2.5~40 mAs. The acquired images were classified into efficient DEI groups, then calculated effective dose with DAP by using a PC-Based Monte Carlo Program 2.0. The signal to noise ratio (SNR) was measured at 4 regions, including the thoracic spine, the lung area with the ribs, the lung area without the ribs, and the liver by using Picture Archiving and Communication System. The significance of the group for each tube voltage was verified by performing the kruskal-wallis test and the mann-whitney test as a post-test. When set to 4 groups dependned on the tube voltage, DAP showed significant differences; 60 kVp and 80 kVp, and 60 kVp and 90 kVp (p= 0.034, 0.021). Effective dose exhibited no statistically significant differences from the all of the group (p>0.05). SNR exhibited statistically significant differences from the all of the group in the liver except compared to 80 kVp and 90 kVp (p<0.05). Therefore, high tube voltages of 100 kVp or more need to be reconsidered in terms of patient dose and imaging in order to represent an appropriate chest X-ray image in a digital system.

• Journal of Chest Surgery
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• v.19 no.2
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• pp.347-351
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• 1986

Soft-tissue chondroma was very rare in incidence and thought to be benign. Recently, we operated upon a 13 year-old female with a chondroma of the middle mediastinum, which was incidentally detected in chest X-ray as mediastinal mass, measured about 10x8x7 cm in size and completely resected via thoracotomy.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.553-555
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• 2002

컴퓨터지원진단(Computer Aided Diagnosis; CAD) 시스템은 방사선 의사들이 흉부 X-ray 영상에서 결절을 탐지하는데 있어 실제적으로 발생할 수 있는 오진율을 줄이고, 폐 결절이 존재하는 폐야에서 결절의 존재 유무를 판단하여 검출을 표시함으로써 진단율을 개선시킬 수 있도록 하였다. 본 논문은 흉부 X-ray 영상에서의 폐 결절을 추출하는데 유전자 알고리즘(Genetic Algorithm)을 이용한 템플릿 매칭(Template Matching) 방법을 제안한다. 제안한 방법은 흉부 X-ray 영상에 존재하는 결절과 레퍼런스 이미지를 매칭시켜 적합도를 계산한 후, 그 값을 통하여 수치가 낮은 개체를 선택하여 높은 개체와 교차시킨다. 그리고 레퍼런스 이미지는 결절이 존재하는 환자 X-ray 영상에서 샘플 노듈을 추출한 후 가우시안 분포를 갖는 512개의 레퍼런스 이미지를 생성하였다. 본 논문에서 사용된 영상은 결절 50개, 비결절 30개와 흉부 X-ray 영상에서 육안으로 판별이 가능한 결절 영상을 20개를 포함하여 총 100개 영상을 사용하였다. 실험 결과 83%의 결절을 자동 추출 하였으며, 가장 적절한 레퍼런스 이미지를 발견하고 이를 흉부영상에 매칭시켜 정확한 결절의 위치를 확인하였다.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1062-1075
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• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.