• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.1
• /
• pp.49-55
• /
• 2000

Recently, X-ray chest rediography is showing a tendency to take an image of digital radiography so as to diagnose the pathology of chest in a usual. When the radiologist observes the chest image derived from digital radiography system on the monitor, he feels difficult to find out the pathological pattern because the quality of chest radiography is unequal. It takes amount of time to adjust the proper image for diagnosis. Therefore, we propose the method of the chest image equalization using neural networks and provide the compared result with histogram equalization method.

• Journal of Internet Computing and Services
• /
• v.24 no.1
• /
• pp.49-59
• /
• 2023

Recently, attention to the pandemic situation represented by COVID-19 emerged problems caused by unexpected shortage of medical personnel. In this paper, we present a method for diagnosing the presence or absence of lesional sign on PA chest X-ray images as computer vision solution to support diagnosis tasks. Method for visual anomaly detection based on feature modeling can be also applied to X-ray images. With extracting feature vectors from PA chest X-ray images and divide to patch unit, region-specific abnormality can be detected. As preliminary experiment, we created simulation data set containing multiple objects and present results of the comparative experiments in this paper. We present method to improve both efficiency and performance of the process through hard masking of patch features to aligned images. By summing up regional specificity and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to previous studies. By aggregating region-specific and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to our last study.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.2
• /
• pp.371-378
• /
• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Journal of the Korean Society of Radiology
• /
• v.15 no.5
• /
• pp.613-620
• /
• 2021

The purpose of this study is to propose a convolutional neural network model that can classify normal and abnormal(cardiomegaly) in chest X-ray images. The training data and test data used in this paper were used by acquiring chest X-ray images of patients diagnosed with normal and abnormal(cardiomegaly). Using the proposed deep learning model, we classified normal and abnormal(cardiomegaly) images and verified the classification performance. When using the proposed model, the classification accuracy of normal and abnormal(cardiomegaly) was 99.88%. Validation of classification performance using normal images as test data showed 95%, 100%, 90%, and 96% in accuracy, precision, recall, and F1 score. Validation of classification performance using abnormal(cardiomegaly) images as test data showed 95%, 92%, 100%, and 96% in accuracy, precision, recall, and F1 score. Our classification results show that the proposed convolutional neural network model shows very good performance in feature extraction and classification of chest X-ray images. The convolutional neural network model proposed in this paper is expected to show useful results for disease classification of chest X-ray images, and further study of CNN models are needed focusing on the features of medical images.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.04c
• /
• pp.247-249
• /
• 2003

일반적으로 방사선 의사들(radialogists)이 폐 결절(pulmonary nodule)을 탐지하는 데는 실제적으로 30% 의 실패율을 가진다고 알려져 있다. 만약 자동화된 시스템이 흉부 영상에서 의심스런 결절들의 위치들을 방사선 의사에게 알려줄 수 있다면 잘못 판단되는 결절들의 수를 잠재적으로 줄일 수 있다. 그리하여 기존의 시스템들은 처리가 어려운 X 선 영상을 좀 더 다루기 쉬운 상태로 바꿔주기 위하여 원래의 영상에 수축(erosion)과 확장(dilation)을 연이어서 행하는 형태학적 필터링(morphological filtering) 처리를 행한다. 그런 다음 결절의 특징을 가려낼 수 있는 추출 기술들을 나름대로 행한다. 그러나, 이 형태학적 필터링 처리는 상당한 처리 시간을 필요로 한다. 이에 본 논문은 형태학적 필터링 처리를 행하지 않고 원래의 흉부 X 선 영상으로부터 직접 결절의 의심지역들을 추출한 후 두가지 특징 추출기술들을 적용시킨다. 그리하여 본 시스템은 결절의 정확한 판독이 어려운 폐의 X 선 영상에 적용되어 false-positive 들을 효과적으로 줄임으로써 보다 효율적인 폐 결절의 추출를 가능하게 하였다.

• Journal of the Korean Society of Radiology
• /
• v.18 no.5
• /
• pp.541-549
• /
• 2024

The various structures of artificial neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been extensively studied and served as the backbone of numerous models. Among these, a transformer architecture has demonstrated its potential for natural language processing and become a subject of in-depth research. Currently, the techniques can be adapted for image processing through the modifications of its internal structure, leading to the development of Vision transformer (ViT) models. The ViTs have shown high accuracy and performance with large data-sets. This study aims to develop a ViT-based model for detecting pneumonia using chest X-ray images and quantitatively evaluate its performance. The various architectures of the ViT-based model were constructed by varying the number of encoder blocks, and different patch sizes were applied for network training. Also, the performance of the ViT-based model was compared to the CNN-based models, such as VGGNet, GoogLeNet, and ResNet. The results showed that the traninig efficiency and accuracy of the ViT-based model depended on the number of encoder blocks and the patch size, and the F1 scores of the ViT-based model ranged from 0.875 to 0.919. The training effeciency of the ViT-based model with a large patch size was superior to the CNN-based models, and the pneumonia detection accuracy of the ViT-based model was higher than that of the VGGNet. In conclusion, the ViT-based model can be potentially used for pneumonia detection using chest X-ray images, and the clinical availability of the ViT-based model would be improved by this study.

• Journal of Korea Multimedia Society
• /
• v.16 no.7
• /
• pp.829-840
• /
• 2013

In this paper, we propose a new lung segmentation method for chest x-ray images which can take both global and local properties into account. Firstly, the initial lung segmentation is computed by applying the active shape model (ASM) which keeps the shape of deformable model from the pre-learned model and searches the image boundaries. At the second segmentation stage, we also applied the localizing region-based active contour model (LRACM) for correcting various regional errors in the initial segmentation. Finally, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by a radiologist. The comparison experiments were performed using 5 lung x-ray images. In our experiment, the Dice coefficient with manually segmented area was $95.33%{\pm}0.93%$ for the proposed method. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for a more accurate early diagnosis and prognosis regarding lung cancer in chest x-ray images.

• Journal of radiological science and technology
• /
• v.38 no.4
• /
• pp.395-401
• /
• 2015

The purpose is to determine the degree of contamination of the equipment for infection control in chest radiography of the radiology department. We confirmed by chemical and bacterial identification of bacteria of the equipment and established a preventive maintenance plan. Chest X-ray radiography contact area on the instrument patients shoulder, hand, chin, chest lateral radiography patient contact areas with a 70% isopropyl alcohol cotton swab were compared to identify the bacteria before and after sterilization on the patient contact area in the chest radiography equipment of the department. The gram positive Staphylococcus was isolated from side shoots handle before disinfection in the chest radiography equipment. For the final identification of antibiotic tested that it was determined by performing the nobobiocin to the sensitive Staphylococcus epidermidis. Chest radiography equipment before disinfecting the handle side of Staphylococcus epidermidis bacteria were detected using a disinfectant should be to prevent hospital infections.

• Journal of the Korean Society of Radiology
• /
• v.7 no.3
• /
• pp.165-173
• /
• 2013

The purpose of this study is to examine how much the movement at X-ray examinations like breathing or the positioning affects the image during chest or abdomen X-ray examination so as to create an image containing information as much as possible. The study method adopted is doing the X-ray in each of the states including breathing (inspiration & expiration) and movement in the standing chest PA X-ray and simple abdomen X-ray among the kinds of examination selected the most in hospitals and then evaluating them by applying the standards of image evaluation for each region. According to the study result, about the standing chest PA X-ray, the images taken at inspiration contain more information than those taken at expiration or having subtle movement during the examination. About the simple abdomen X-ray, the images taken at expiration contain more information than those taken at inspiration or movement. The above study results imply that regarding general X-ray examination, information we can find from the images may differ significantly according to the region examined, examination purpose, or movement during the examination like breathing.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.4
• /
• pp.195-201
• /
• 2020

This study studied scoliosis, one of the most common modern diseases caused by lifestyle patterns of office workers sitting in front of computers all day and modern people who use smart phones frequently. Scoliosis is a typical complication that takes more than 80% of the nation's total population at least once. X-ray are used to test for these complications. X-ray, a non-destructive testing method that allows scoliosis to be easily performed and filmed in various areas such as the chest, abdomen and bone without contrast agents or other instruments. We uses NI DAQ to miniaturize digital X-ray imaging devices and image intensifier in self-shielding housing with Vision Assistant for drawing lines to the top and the bottom of the spine to acquire angles, i.e. curvature in real-time. In this way, the research was conducted to see scoliosis patients and their condition easily and to help rapid treatment for solving the problem of posture correction in modern people.