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

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.829-840
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• 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 Broadcast Engineering
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• v.25 no.1
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• pp.1-12
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• 2020

In the medical field, numerical imbalance of data due to differences in disease prevalence is a common problem. It reduces the performance of a artificial intelligence network, leading to difficulties in learning a network with good performance. Recently, generative adversarial network (GAN) technology has been introduced as a way to address this problem, and its ability has been demonstrated by successful applications in various fields. However, it is still difficult to achieve good results in solving problems with performance degraded by numerical imbalances because the image resolution of the previous studies is not yet good enough and the structure in the image is modeled locally. In this paper, we propose a multi-scale conditional generative adversarial network based on attention mechanism, which can produce high resolution images to solve the numerical imbalance problem of chest X-ray image data. The network was able to produce images for various diseases by controlling condition variables with only one network. It's efficient and effective in that the network don't need to be learned independently for all disease classes and solves the problem of long distance dependency in image generation with self-attention mechanism.

• Progress in Medical Physics
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• v.22 no.4
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• pp.163-171
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• 2011

The purpose of this study was to investigate the effect of various technical parameters for the dose optimization in pediatric chest radiological examinations by evaluating effective dose and effective detective quantum efficiency (eDQE) including the scatter radiation from the object, the blur caused by the focal spot, geometric magnification and detector characteristics. For the tube voltages ranging from 40 to 90 kVp in 10 kVp increments at the FDD of 100, 110, 120, 150, 180 cm, the eDQE was evaluated at the same effective dose. The results showed that the eDQE was largest at 60 kVp when compares the eDQE at different tube voltage. Especially, the eDQE was considerably higher without the use of an anti-scatter grid on equivalent effective dose. This indicates that the reducing the scatter radiation did not compensate for the loss of absorbed effective photons in the grid. When the grid is not used the eDQE increased with increasing FDD because of the greater effective modulation transfer function (eMTF). However, most of major hospitals in Korea employed a short FDD of 100 cm with an anti-scatter grid for the chest radiological examination of a 15 month old infant. As a result, the entrance surface air kerma (ESAK) values for the hospitals of this survey exceeded the Korean DRL (diagnostic reference level) of $100{\mu}Gy$. Therefore, appropriate technical parameters should be established to perform pediatric chest examinations on children of different ages. The results of this study may serve as a baseline to establish detailed reference level of pediatric dose for different ages.

• Journal of Biomedical Engineering Research
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• v.11 no.2
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• pp.209-216
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• 1990

Pulmonary nodules in chest X-Ray images were detected using the symmetric property of human lung and its performance was evaluated. Thls algorithm reduced the effect of background components and enhanced the nodule signals relatively. The image was divided and processed separately, the half with matched filter only, and the other half with warping and matched filter. This algorithm increased the entire detection rate by reducing False-Positive error and improving True-Positive detectability. Result shows 10-25 % improvement in detection rate compared with the conventional alsorithm for nodules size of 10mm.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.725-733
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• 2023

The purpose of this study is to evaluate the effectiveness of virtual grid software (VGS). The purpose of this study is to evaluate the changes in energy and object thickness by dividing the use of VGS into two cases (Without-VGS) without using a movable grid. We attempted to determine the effectiveness of VGS by acquiring images using a chest phantom and a thigh phantom and analyzing SNR and CNR. In the chest phantom and femoral phantom, the tube flow was fixed at 2.5 mAs, and the tube voltage was changed by 10 kVp from 60 to 100 kVp to measure SNR and CNR, and SNR was about 1.09 to 8.86% higher in the chest phantom than in Without-VGS, and CNR was 4.18 to 14.56% higher in the VGS than in Without-VGS. And in the femoral phantom, SNR was about 9.78 to 18.05% higher in VGS than in Without-VGS, and CNR was 21.07 to 44.44% higher in VGS than in Without-VGS. The tube voltage was fixed at 70 kVp in the chest phantom and the femoral phantom, and the amount of tube current was changed at 2.5 to 16 mAs, respectively, and after X-ray irradiation, SNR and CNR were measured in the chest phantom, which was about 1.49 to 11.11% higher in VGS than in Without-VGS, and CNR was 4.76 to 13.40% higher in VGS than in Without-VGS. And in the femoral phantom, SNR was about 2.22 to 17.38% higher in VGS than in Without-VGS, and CNR was 13.85 to 40.46% higher in VGS than in Without-VGS. Therefore, if an inspection is required with a mobile X-ray imaging device, it is believed that good image quality can be obtained by using VGS in an environment where it is difficult to use a mobile grid, and it is believed that the use of mobile X-ray devices can be increased.

• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.301-306
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• 2021

The use of tin filters as a way to reduce the medical radiation in computed tomography (CT). However, due to the changed X-ray spectrum with the use of tin filters, disease diagnosis could be affected because it appears as images of different impressions from previous images. Therefore, this study evaluates the changes in images when using tin filter and high pitch in chest low-dose CT. In this study, images were acquired in groups of three for comparison. Group 1 did not apply to tin filter, and used the existing pitch 0.8. Group 2 used a tin filter, pitch 0.8, Group 3 used a tin filter, and pitch 2.5. To compare the image quality, the natural image quality evaluator (NIQE) and the blind/referenceless image quality evaluator (BRISQUE) were used among the blind quality evaluation factors depended on a no-reference basis. As a result, the NIQE values were low in the order of Group 1, Group 3, and Group 2. BRISQUE values were low in the order of Group 3, Group 2 and Group 1. This study confirms the superiority of images of tin filter and high pitch techniques in chest low-dose CT, which is considered to be a fundamental study for acquiring accurate images of patients with difficult breathing control.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.293-294
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• 2007

The purpose of this study is prove the effectiveness of an energy subtraction image for the detection of pulmonary nodules and the effectiveness of multi-resolutional filter on an energy subtraction image to detect pulmonary nodules. Also we study influential factors to the accuracy of detection of pulmonary nodules from viewpoints of types of images, types of digital filters and types of evaluation methods. As one type of images, we select an energy subtraction image, which removes bones such as ribs from the conventional X-ray image by utilizing the difference of X-ray absorption ratios at different energy between bones and soft tissue. Here we select two evaluation methods and make clear the effectiveness of multi-resolutional filter on an energy subtraction image.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.257-265
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• 2024

In this study, we applied AEC(Auto Exposure Control), which is used in many chest examinations, to evaluate whether medical devices inserted into the body affect the dose and image quality of chest images. After attaching three HIMD(Human implantable medical devices) to the ion chamber, the Monte Carlo methodology-based program PCXMC(PC Program for X-ray Monte Carlo) 2.0 was applied to measure the effective dose by inputting the DAP(Dose Ares Product) value derived from the Pacemaker and CRT and Chemoport Additionally, to evaluate image quality, we set three regions of interest and one noise region on the chest and measured SNR and CNR. The final study results showed significant differences in DAP and Effective dose. There was a significant difference between Pacemaker and CRT when AEC was applied and not applied. (p<0.05) When applied, the dose increased by 37% for Pacemaekr and 52% for CRT. Chemoport showed a 10% increase in effective dose depending on whether AEC was applied, but there was no significant difference. (p>0.05) In the image quality evaluation, there was no significant difference in image quality between all HIMD insertions and AEC applied or not. (p>0.05) Therefore, when the HIMD was inserted into the chest during a chest x ray and overlapped with the ion chamber sensor, the effective dose increased, and there was no difference in image quality even at a low dose without AEC. Therefore, when performing a chest X-ray examination of a patient with a HIMD inserted, it is considered that performing the examination without applying AEC is a method that can be considered to reduce the patient's radiation exposure.