• Journal of radiological science and technology
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• v.46 no.4
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• pp.287-294
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• 2023

This study was conducted to ultimately reduce unnecessary radiation exposure by emphasizing the need and importance of correct positioning by examining the positioning relationship of anatomical structures in the human body and changes in X-ray images according to changes in patient positioning during the left lateral chest X-ray examination. This study investigated and analyzed previously published papers and books on the left lateral chest X-ray examination to find out the importance of positioning in the left lateral chest X-ray examination. To find out the importance of correct positioning in the left lateral chest X-ray, we compared three images of incorrectly positioned right thorax and left thorax rotated forward and the lower median surface of the body leaning against the image receptor. In the left lateral chest examination, a distorted image was obtained in which the shape of the anatomical structure observed in the image was changed according to the presence or absence of rotation of the patient and the inclination of the median visual surface. X-ray images with the most accurate and large amount of information were obtained from X-ray images with the correct positioning performed during left lateral chest X-ray examination. Therefore, It is believed that the left lateral chest X-ray examination will have beneficial effects such as providing accurate medical information, preventing misdiagnosis, reducing social costs, and ultimately reducing radiation exposure.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2482-2484
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• 2000

In this paper, we have developed an automated computer aided diagnostic (CAD) scheme by using artificial neural networks(ANN) on guantitative analysis of chest photofluorography. The first ANN performs the detection of suspicious regions in a low resolution image. This was trained specifically on the problem of detecting abnormal regions digitized chest photofluorography. The second space matching method was used to distinguish between normal and abnormal regions of interest(ROI). If the ratio of the number of abnormal ROI to the total number of all ROI in a chest image was greater than a specified threshold level, the image was classified as abnormal.

• Journal of the Korean Society of Radiology
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• v.8 no.2
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• pp.75-79
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• 2014

The aim of this retrospective research was to investigate the influence of the patient's arm position on radiation dose and scanning during CT. Chest CT image created image degradation, artifact and overdose to the patient due to the difference of the chest thickness. Therefore, the patient's arm should up position during the CT chest examination. In 2012, 1,642 patients underwent chest CT examination in Seoul K hospital. 118 chest CT examination performed hands down position. The average DLP value of the CT chest arm up examination was 275 $mGy{\cdot}cm$. The average DLP value of the CT chest arm down examination was 312.46 $mGy{\cdot}cm$. In the retrospective study with same patient, The average DLP value of the CT chest examination arm up vigorously was 267.5 $mGy{\cdot}cm$. The average DLP value of the CT chest arm down examination was 307.5 $mGy{\cdot}cm$. Chest CT scan without raising arm created linear artifact due to the lack of X-ray photons which is the thickest part of the human body of shoulder area. In conclusion, arm positioning patients' arms above the shoulders at CT of the chest increases image quality and substantially reduces effective radiation dose.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Journal of radiological science and technology
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• v.28 no.4
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• pp.273-277
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• 2005

The rapid development in digital acquisition technology in radiography has not been accompanied by information regarding optimum radiolographic technique for use with an amorphus silicon flat panel detector. The purpose of our study was to compared image quality and radiation dose of an amorphus silicon flat panel detectors for digital chest radiography. All examinations were performed by using an amorphus silicon flat panel detector. Chest radiographs of an chest phantom were obtained with peak kilovoltage values of $60{\sim}150kVp$. Published data on the effect of x-ray beam energy on image quality and patient dose when using an amorphus silicon flat panel detector. It is important that radiographers are aware of optimum kVp selection for an amorphus silicon flat panel detector system, particularly for the commonly performed chest examination.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.7-13
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• 2022

This study compared the aortic root image by using the ECG gating and non-ECG gating methods. We observed the presence or absence of progression of the aortic root image in the images examined by the high pitch (flash) chest pain protocol method and in the patients tested without ECG gating by the conventional method. The AAPM phantom was scanned by using high pitch (flash) chest pain protocol and general chest pain protocol. CTDI values were compared. By ECG gating, the blurring of ascending aorta was significantly reduced compared to the existing non-ECG gating test method, and the image quality of the aortic root was improved. Within the parametar range that did not show differences in noise, uniformity, and high contrast resolution, CTDI values were lower when tested with the high-pitch chest pain protocol. It was found that there is an advantage in dose reduction, and if it is applied and applied to diagnostic fields such as dissection using the dose reduction mode in the cardiac field, it is a very important test for patients who need rapid diagnosis and prompt treatment as well as a dramatic reduction in exposure dose. It is presumed to be a method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2163-2170
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• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

• Journal of radiological science and technology
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• v.41 no.6
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• pp.533-538
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• 2018

As the use of digital radiographic system has been expanded, there are some concerns an increase about in patient of radiation dose. Therefore, International Electro-technical Commission (IEC) has been proposed a standard foe exposure index (EI). In this study, the EI was measured on human chest model using computed radiography (CR). Radiation quality used RQA5 of IEC62494-1. After acquiring the chest anterior posterior image (Chest AP) by using the phantom, the EI was obtained by applying the system response. In this study, we have analyzed the images with the detector size (Full filed ROI) and the optimized image (Fit filed ROI). The EI increased proportionally with radiation dose increase. Due to the discrete increase in pixel value, the EI showed an exponential increase. The discrete increase in noise equivalent quanta (NEQ) resulted in a discrete increase in the EI. The EI of the two images used in this study increased with increasing NEQ but showed different increments. For the measurement of the EI, IEC standards must be followed. The EI should be used as an index to evaluate the image quality for quality control of X-ray image rather than as an indicator of exposure dose. When calculating the EI, the system response should be applied depending on whether or not the grid is used. The size of the field should be obtained by including only the necessary parts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2298-2315
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• 2024

This paper introduces a method aimed at diagnosing the presence or absence of lesions by detecting anomalies in Chest X-ray images. The proposed approach is based on the PatchCore anomaly detection method, which extracts a feature vector containing location information of an image patch from normal image data and calculates the anomaly distance from the normal vector. However, applying PatchCore directly to medical image processing presents challenges due to the possibility of diseases occurring only in specific organs and the presence of image noise unrelated to lesions. In this study, we present an image alignment method that utilizes affine transformation parameter prediction to standardize already captured X-ray images into a specific composition. Additionally, we introduce a region-specific abnormality detection method that requires affine-transformed chest X-ray images. Furthermore, we propose a method to enhance application efficiency and performance through feature map hard masking. The experimental results demonstrate that our proposed approach achieved a maximum AUROC (Area Under the Receiver Operating Characteristic) of 0.774. Compared to a previous study conducted on the same dataset, our method shows a 6.9% higher performance and improved accuracy.

• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.541-547
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• 2018

To evaluate the effect of dose and image quality for Chest Digital Tomosynthesis(CDT) using sensitivity and tube voltage(kV). CDT images of the phantom were acquired varying sensitivity 200, 320, 400 according to set tube voltage of 125 kV and 135 kV. The dose and Dose Area Product(DAP) according to change of sensitivity and kV were evaluated and Image quality was evaluated by PSNR, CNR, SNR using Image J. Dose were lowered 14~23% less than sensitivity 200, 125 kV and DAP were lowered 13~26% less than sensitivity 200, 125 kV. PSNR were over 27 dB, which were significant value and CNR, SNR were better as sensitivity value was lower. But there were different statistical significant to each item. CNR and SNR were not statistically significant at sensitivity 320, 135 kV(P>0.05). CDT can improve image quality with lower radiation dose using better than quality and correction power at digital radiography system.