• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.97-107
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• 2015

The initial X-ray images obtained from a digital X-ray machine have a wide data range and uneven brightness level than normal images. In particular, in Chest X-ray images, it is necessary to improve naturally all of the parts such as ribs, spine, tissue, etc. These X-ray images can not be improved enough from conventional image quality enhancement algorithms because their characteristics are different from ordinary images'. This paper proposes to eliminate unnecessary background from an input image and expand the histogram range of the image. Then, we adjust the weight per frequency band of the image for improvement of contrast and sharpness. Finally, jointly taking the advantages of global contrast enhancement and local contrast enhancement methods we obtain an improved X-ray image suitable for effective diagnosis in comparison with the existing methods. Experimental results show quantitatively that the proposed algorithm provides better X-ray images in terms of the discrete entropy and saturation than the previous works.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.73-74
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• 2021

코로나로 인해 X-ray, CT, MRI와 같은 의료영상 분야에서 딥러닝을 많이 접목시키고 있다. 간단히 접할 수 있는 X-ray 영상으로 코로나 진단을 위해 CNN, R-CNN 등과 같은 영상 딥러닝 분야에서 많은 연구가 진행되고 있다. 의료영상 기반 딥러닝 학습은 바이오마커를 정확히 찾아내고, 최소한의 손실률과 높은 정확도를 필요로한다, 따라서 본 논문에서는 높은 정확도를 위한 학습 모델을 선정하고 실험을 진행하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.105-114
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• 2015

This paper proposes methods to remove background area and segment region of interest (ROI) in chest X-ray images. Conventional algorithms to improve detail or contrast of images normally utilize brightness and frequency information. If we apply such algorithms to the entire images, we cannot obtain reliable visual quality due to unnecessary information such as background area. So, we propose two effective algorithms to remove background and segment ROI from the input X-ray images. First, the background removal algorithm analyzes the histogram distribution of the input X-ray image. Next, the initial background is estimated by a proper thresholding on histogram domain, and it is removed. Finally, the body contour or background area is refined by using a popular guided filter. On the other hand, the ROI, i.e., lung segmentation algorithm first determines an initial bounding box using the lung's inherent location information. Next, the main intensity value of the lung is computed by vertical cumulative sum within the initial bounding box. Then, probable outliers are removed by using a specific labeling and the pre-determined background information. Finally, a bounding box including lung is obtained. Simulation results show that the proposed background removal and ROI segmentation algorithms outperform the previous works.

• Tuberculosis and Respiratory Diseases
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• v.62 no.1
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• pp.5-10
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• 2007

Background: Korean national guidelines for examining contacts with active pulmonary tuberculosis (TB) are a tuberculin skin test (TST) and chest radiographs. The treatment of a latent TB infection as performed only in those younger than six years of age who test positive for TST. Although there is a high incidence of active TB in young Korean soldiers, the current national guidelines for controlling contacts with active TB in soldiers are insufficient. This study highlights the problems with the Korean guidelines for controlling a TB outbreak in a small military unit. Material and Methods: In December of 2005, there was a tuberculosis outbreak in a military unit with a total of 464 soldiers in Kyung Gi province. The chest radiographs were taken of all the soldiers, and TST were carried out on 408 candidates. Results: In the first screening of the chest radiographs, two active TB patients were detected. By August of 2006, four additional cases were detected, making a total of six cases after the outbreak. All the patients showed active pulmonary TB or TB pleuritis. When the results of TST in the close contacts and non-close contacts were compared, there was a significant difference in the absolute size of the induration($9.70{\pm}7.50mm$ vs. $6.26{\pm}7.02mm$, p<0.001) as well as the ratio of patients showing an induration > 10mm (50.0% vs. 32.0%, p<0.001) and 15mm (33.2% vs. 20.9%, p= 0.005). Conclusion: Although the national guidelines for managing a TB outbreak in a military unit were followed, there were continuous instances of new active TB cases. This highlights the need for new guidelines to prevent the spread of TB.

• Clinical and Experimental Pediatrics
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• v.52 no.1
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• pp.124-128
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• 2009

Pulmonary arteriovenous malformations (PAVMs) are direct communications between pulmonary arteries and pulmonary veins, resulting in right-to-left shunts that may cause cyanosis, dyspnea, and digital clubbing. Neurological complications such as intracerebral hemorrhage or brain abscess may result from cerebral thrombosis or emboli. In most cases, they remain unrecognized until the late teenage years. Here, we report a case of a 6-year-old boy who presented with perioral cyanosis, digital clubbing, and dyspnea on exertion. A plain chest X-ray showed a focal nodular opacity in the right lower lobe (RLL), and a diagnosis of a large PAVM in the RLL was confirmed by chest computed tomography. A right lower lobectomy was successfully performed without any complications. Although their incidence in children is low, PAVMs should be suspected as a possible cause of cyanosis and dyspnea of non-cardiac origin, and should be treated promptly to prevent further neurological complications.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.939-945
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• 2023

This study analyzes the 'performance efficiency' of AI-based reading assistance systems in the field of radiology by measuring their 'time behavior' properties. Due to the increase in medical images and the limited number of radiologists, the adoption of AI-based solutions is escalating, stimulating a multitude of studies in this area. Contrary to the majority of past research which centered on AI's diagnostic precision, this study underlines the significance of time behavior. Using 50 chest X-ray PA images, the system processed images in an average of 15.24 seconds, demonstrating high consistency and reliability, which is on par with leading global AI platforms, suggesting the potential for significant improvements in radiology workflow efficiency. We expect AI technology to play a large role in the field of radiology and help improve overall healthcare quality and efficiency.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.331-339
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• 2016

Chronic diseases are increasing nowadays as daily habits changed due to economic growth. Among chronic diseases, heart cerebrovascular disease is one of the major causes of death in South Korea that accounts for approximately 20% of mortality. Tests to measure anomaly of the heart is ECG tests, which measures and analyzes the electrical heart activity. Any mistakes in lead attachment location critically affects ECG testings, and statistical facts showed that only 2.8% of the nurses properly located leads to patients. As a solution, this paper proposes a system based on a projection-based augmented reality technology to generate X-ray images to the patient's chest to point out exact attachment locations of ECG leads. Evaluation comparison results showed a 2.6 cm difference between the conventional system and the proposed system. ECG test results also showed significant signal differences between the systems in leads V1, V2, and V3. The ECG measured accurately by the proposed system would help greatly in patient management and clinical decisions of clinicians.