• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.131-136
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• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• Journal of radiological science and technology
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• v.26 no.3
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• pp.13-17
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• 2003

By converting movable indirect mass chest X-ray devices for vehicles into digital systems and upgrading it to share information with the hospital's medical image information system, excellencies have been confirmed as a result of installing and running this type of system and are listed hereinafter. 1. Upgrading analog systems, such as indirect mass chest X-ray devices dependent on printed film, to digital systems allows them to be run and managed much more efficiently, contributing to the increase in the stability and the efficiency of the system. 2. Unlike existing images, communication based on DICOM standards allow images to be compatible with the hospital's outer and inner network PACS systems, extending the scope of the radiation departments information system. 3. Assuming chest-exclusive indirect mass chest X-rays, a linked development of CAD (Computer Aided Diagnosis, Detector) becomes possible. 4. By applying wireless Internet, Web-PACS for movable indirect mass chest X-ray devices for vehicles will become possible. Research in these fields must continue and if the superior image quality and convenience of digital systems are confirmed, I believe that the conversion of systems still dependent on analog images to modernized digital systems is a must.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.741-745
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• 1991

This paper describes a feature extraction in digitized chest X-ray image and CT head Image. There are Extraction, Thresholding, Region G rowing, Split-Merge and Relaxation in feature extraction technique. In this study, Region Growing System was realized and Fuzzy Set Theory was applied in order to extract the vague region which the conventional method has difficulties in extracting. The performance of proposed algorithm was proved by being applied to chest X-ray image and CT head image.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.165-171
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• 2024

In this study, we present a novel approach for enhancing chest X-ray image classification (normal, Covid-19, edema, mass nodules, and pneumothorax) by combining contrastive learning and machine learning algorithms. A vast amount of unlabeled data was leveraged to learn representations so that data efficiency is improved as a means of addressing the limited availability of labeled data in X-ray images. Our approach involves training classification algorithms using the extracted features from a linear fine-tuned Momentum Contrast (MoCo) model. The MoCo architecture with a Resnet34, Resnet50, or Resnet101 backbone is trained to learn features from unlabeled data. Instead of only fine-tuning the linear classifier layer on the MoCopretrained model, we propose training nonlinear classifiers as substitutes for softmax in deep networks. The empirical results show that while the linear fine-tuned ImageNet-pretrained models achieved the highest accuracy of only 82.9% and the linear fine-tuned MoCo-pretrained models an increased highest accuracy of 84.8%, our proposed method offered a significant improvement and achieved the highest accuracy of 87.9%.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.243-250
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• 2023

Chest X-ray is radiological examination for xeamining the lungs and haert, and is particularly widely used for diagnosing lung disease. Since the quality of these chest X-rays can affect the doctor's diagnosis, the process of evaluating the quality must necessarily go through. This process can involve the subjectivity of radiologists and is manual, so it takes a lot of time and csot. Therefore, in this paper, based on the chest X-ray quality assessment guidelines used in clinical settings, we propose a tool that automates the five quality assessments of artificial shadow, coverage, patient posture, inspiratory level, and permeability. The proposed tool reduces the time and cost required for quality judgment, and can be further utilized in the pre-processing process of selecting high-quality learning data for the development of a learning model for diagnosing chest lesions.

• 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 the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.829-840
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• 2001

In this study, we propose a method for quantifying the degree of advance of pulmonary emphysema by using chest X-ray images. With this method, we devise two schemes for this purpose. One is for detecting blood vessels by using a deformable model with the tree-like structure and using an evaluation function specialized by knowledge about blood vessels appeared in chest X-ray images, and the other is for quantifying the degree of advance by using several features, which were extracted from blood vessels, and the equation of quantitative evaluation. In order to evaluate the performance, we applied the proposed method to 189 ROIs(Regions of Interest) of ten chest X-ray images and compared the values by the proposed method with those by a medical doctor.

• Journal of the Korean Society of Radiology
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• v.14 no.1
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• pp.39-44
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• 2020

The purpose of this study was learning about chest X-ray image classification and accuracy research through Deep Learning using big data technology with Convolution Neural Network. Normal 1,583 and Pneumonia 4,289 were used in chest X-ray images. The data were classified as train (88.8%), validation (0.2%) and test (11%). Constructed as Convolution Layer, Max pooling layer size 2×2, Flatten layer, and Image Data Generator. The number of filters, filter size, drop out, epoch, batch size, and loss function values were set when the Convolution layer were 3 and 4 respectively. The test data verification results showed that the predicted accuracy was 94.67% when the number of filters was 64-128-128-128, filter size 3×3, drop out 0.25, epoch 5, batch size 15, and loss function RMSprop was 4. In this study, the classification of chest X-ray Normal and Pneumonia was predictable with high accuracy, and it is believed to be of great help not only to chest X-ray images but also to other medical images.

• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.165-173
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• 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 radiological science and technology
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• v.45 no.4
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• pp.299-304
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• 2022

This study uses the 'S-align' function to present a reference value of the X-ray tube angle for the realization of an image similar to that of the chest PA image during chest AP radiography. This study targeted dummy phantom and used a 17"×17" DR image receptor. The irradiation conditions were 110 kVp, 160 mA, 50 ms, and the distance between the central X-ray and the image receptor was set to 180 cm and 110 cm, respectively. The end of the catheter was placed at the 11th thoracic height to indicate the nasogastric tube. In the case of lung apex length measurement, the mean value of measurement was 30.53±0.47 in PA. T 0°, TCA 5~25°, TCE 5~15° were 21.07±0.29, 27.60±0.21, 34.13±0.44, 39.86±0.31, 45.96±0.61 mm, 54.13±0.37 mm, 16.16±0.46 mm, 9.81±0.35 mm, 2.75±0.30 mm, respectively. For the depth of the catheter end, the average value measured at PA was 6.70±0.31 mm. T 0°, TCA 5~25°, TCE 5~15° were 15.72±0.38 mm, 24.10±0.50 mm, 29.24±0.86 mm, 34.35±0.35 mm, 41.06±1.08 mm, 48.07±0.38 mm, 12.85±0.25 mm, 7.92±0.36 mm, 3.01±0.39 mm, respectively. The length of the lung apex was similar to that of chest PA when the angle of incidence was adjusted from 5° to 10° in the leg direction, and the depth of the catheter tip was most similar when the X-ray tube angle was incident at 10° in the head direction. Therefore, To change the X-ray tube angle according to the purpose of the examination during the chest AP radiography using 'S-align' function is considered necessary.