• 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.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.293-301
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• 2023

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.1
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• pp.61-66
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• 2015

Chest X-ray images is the most common and widely used in clinical findings for a wide range of anatomical information about the prognosis of the disease in patients with cardiopulmonary rehabilitation. Many analysis algorithm was developed by a number of studies regarding the region segmentation and image analysis, there are specific differences due to the complexity and diversity of the image. In this paper, a diagnosis support system of the chest X-ray image based on image processing and analysis methods to detect the cardiopulmonary disease. The threshold value and morphological method was applied to segment the pulmonary region in a chest X-ray image. Anatomical measurements and texture analysis was performed on the segmented regions. The effectiveness of the proposed method is shown through experiments and comparison with diagnosis results by clinical experts to show that the proposed method can be used for decision support system.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1062-1075
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• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.61-66
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• 1988

Because the amount of radiation emerging from the thorax behind the lungs is often literally thousands of times that exiting behind the mediastinum, the dynamic range of X-ray chest image is very large. In order to solve the dynamic range problem, we propose a signal adaptive algorithm which enhances the local contrast and contracts the enhancement of quantum noise by local mean/valiance estimator.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.243-252
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• 1989

In this study, medical images, which are X-ray image and CT image, are compressed by the adam live coding technique. The medical images may be treated as special ones, because they are different from general images in many respects. The statistical characteristics that medical images only have in transform domain are analyzed, and then the improved quantization method is proposed for medical images. For chest X-ray image and CT head image, the better results are obtained by the improved adaptive coding technique.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.865-872
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• 2023

The purpose of this study was to analyze fabrics suitable for use as examination gowns to determine whether examination gowns affect imaging during anterior to posterior chest examinations(Chest AP) on a digital X-ray system. Examination gowns in use at five medical centers in Seoul were collected and included modal, tencel, cotton, and rayon fabrics. The selection of fabrics was based on studies that reported fabrics with good tactile, absorbent, stretchable, and wrinkle resistance. Phantoms of five hospital gowns and four fabrics, arranged in overlapping layers from one to eight, were created and examined on a digital X-ray system in both Chest AP examination. The images examined were subjected to a first-step profile analysis, a second-step signal intensity averaging analysis, and a third-step microscopic analysis. The results showed that all nine materials had an increasing impact on the image as the number of layers of fabric increased, with the modal fabric having the least impact on the image in the first, second, and third analyses. In conclusion, as the resolution of digital x-ray systems increases, the impact of examination clothing on the image will increase, and research to find suitable materials for examination clothing will continue to be necessary.