• Tuberculosis and Respiratory Diseases
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• v.40 no.4
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• pp.353-356
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• 1993

Advent of new imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound contributed greately to the specific imaging diagnosis. However plain chest X-ray is still most prequently used for imaging diagnosis of respiratory disease in clinical pratic and it is important to make a good quality of X-ray film and good interpretation. The optimal chest X-ray should be taken with full inspiration without rotation and motion and the exposure is at the level of barely demonstrable thoracic vertebral disc space. It is recommended that higk KVP technique for detection of lesions which is overlaped by mediastinum, heart and rib cage. It is better to examine chest X-ray film start at some distance(6-8 feet) and closer to the film later on and the reader should not read a film in fatigue condition. The reading room should be quiet and relately dark illumination. It is important, to make a good X-ray film and good interpretation to reduce the observer error.

• Korean Journal of Digital Imaging in Medicine
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• v.11 no.1
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• pp.5-13
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• 2009

There are several reasons to take X-ray in case of inpatients. Some of them who cannot ambulate or have any risk if move are taken portable X-ray at their wards. Usually, in this case, many other people-patients unneeded X-ray test, family, hospital workers etc-are indirectly exposed to X-ray by scatter ray. For that reason I try to be aware of free space scatter dose accurately and make the point at issue of portable X-ray better in this study. kVp dose meter is used for efficiency management of portable X-ray equipment. Mobile X-ray equipment, ionization chamber, electrometer, solid water phantom are used for measuring of free space scatter dose. First of all the same surroundings condition is made as taken real portable X-ray, inquired amount of X-ray both chest AP and abdomen AP most frequently examined and measured scatter ray distribution of two tests individually changing distance. In the result of measuring horizontal distribution with condition of chest AP it is found that the mAs is decreased as law of distance reverse square but no showed mAs change according to direction. Vertical distribution showed the mAs slightly higher than horizontal distribution but it isnt found out statistical characteristic. In abdomen AP, compare with chest AP, free space scatter dose is as higher as five-hundred times and horizontal, vertical distribution are quite similar to chest AP in result. In portable X-ray test, in order to reduce the secondary exposure by free space scatter dose first, cut down unnecessary portable order the second, set up the specific area at individual ward for the test the third, when moving to a ward for the X-ray test prepare a portable shielding screen. The last, expose about 2m apart from patients if unable to do above three ways.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.581-585
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• 2010

A grid was sandwiched between two cascaded imaging plates. Using a fan-beam X-ray tube and a single exposure scheme, the two imaging plates, respectively, recorded grid-less and grid type information of the object. Referring to the mathematical model of the Grid-less and grid technique, it was explained that the collected components whereas that of imaging plates with grid was of high together with large scattered components whereas that of imaging plate with grid was of low and suppressed scattered components. Based on this assumption and using a Gaussian convolution kernel representing the effect of scattering, the related data of the imaging plates were simulated by computer. These observed data were then employed in the developed post-processing estimation and restoration (kalman-filter) algorithms and accordingly, the quality of the resultant image was effectively improved.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• 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.

• 전자공학회논문지 IE
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• v.46 no.3
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• pp.62-67
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• 2009

In this paper, ROC curves were designed by using Fuzzy Logic Systems. ROC curve is used for diagnostic evaluation and the person evaluating ROC curve is chosen as a first-level diagnostician. For rating diagnostic capability on ROC curve through learning, the chest X-ray image is used. The images used for making a diagnosis are X-ray film being both noise and signal. The result over diagnostic capability difference between the male and the female represented a man had better than a woman but that difference can be ignored.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.1
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• pp.5-8
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• 2010

Opened a court in February 10, 2006, a rule of safety management of the diagnosis radiation system was promulgated for safety of the radiation worker, patients and patients' family members. The purpose of this rule is to minimize the risk of being exposed to radiation during the process of handling X-ray. For this reason, we manufactured shielding device of mobile X-ray unit collimator for diminution of skin dose. Shielding device is made to a thickness of Pb 0.375mm. For portable chest radiography, we measured skin dose 50cm from center ray to 200cm at intervals of 20cm by Unfors Xi detector. As a result, a rule of safety management of the diagnosis radiation system has been strengthened. But there are exceptions, such as ER, OR, ICU to this rule. So shielding device could contribute to protect unnecessary radiation exposure and improve nation's health.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.235-238
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• 2017

This study was to develop a portable digital radiography (PDR) system with a function measuring the X-ray source-with-detector angle (SDA) and to evaluate the imaging performance for the diagnosis of chest imaging. The SDA device consisted of an Arduino, an accelerometer and gyro sensor, and a Bluetooth module. According to different angle degrees, five anatomical landmarks on chest images were assessed using a 5-point scale. Mean signal-to-noise ratio and contrast-to-noise ratio were 182.47 and 141.43. Spatial resolution (10% MTF) and entrance surface dose were 3.17 lp/mm ($157{\mu}m$) and 0.266mGy. The angle values of SDA device were not significant difference as compared to those of the digital angle meter. In chest imaging, SNR and CNR values were not significantly different according to different angle degrees (repeated-measures ANOVA, p>0.05). The visibility scores of the border of heart, 5th rib and scapula showed significant differences according to different angles (rmANOVA, p<0.05), whereas the scores of the clavicle and 1st rib were not significant. It is noticeable that the increase in SDA degree was consistent with the increase of visibility score. Our PDR with SDA device would be useful to be applicable to clinical radiography setting according to the standard radiography guideline at various fields.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.482-491
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• 2005

In this paper, we proposed the microcalcification detection algorithm which is based on wavelet transform and automatic thresholding method in the X-ray mammographic images. Digital X-ray imaging system is essential equipment in the field diagnosis and is widely used in the various fields such as chest, fracture of a bone, and dental correction. Especially, digital X-ray mammographic imaging is known as the most important method to diagnose the breast cancer, many researches to develop the imaging system are processing in country. In this paper, we proposed a microcalcifications detection algorithm necessary in the early phase of breast cancer diagnosis and showed that a algorithm could effectively detect microcalfication and could aid diagnosis-radiologist.