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

• Proceedings of the Optical Society of Korea Conference
• /
• 1999.08a
• /
• pp.160-161
• /
• 1999

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.06a
• /
• pp.663-664
• /
• 2009

• Journal of the Korean Society of Radiology
• /
• v.15 no.2
• /
• pp.153-158
• /
• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.117-125
• /
• 2022

Although stationary inverse-geometry digital tomosynthesis (s-IGDT) is able to reduce motion artifacts, image acquisition time and radiation dose, the image quality of the s-IGDT is degraded due to the truncations arisen in projections. Therefore, the effects of geometric and image acquisition conditions in the s-IGDT should be analyzed for improving the image quality and clinical applicability of the s-IGDT system. In this study, the s-IGDT images were obtained with the various X-ray source arrangement types and the various number of projections. The resolution and noise characteristics of the obtained s-IGDT images were evaluated, and the characteristics were compared with those of the conventional DT images. The s-IGDT system using linear X-ray source arrangement and 40 projections maximized the image characteristics of resolution and noise, and the corresponding system was superior to the conventional DT system in terms of image resolution. In conclusion, we expect that the s-IGDT system can be used for providing medical images in diagnosis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2002.05b
• /
• pp.31-34
• /
• 2002

In this paper, the electric properties of amophous selenium multilayer samples has been investigated. In order to develop the hybrid flat-panel digital· X-ray image detector, we measured and analyzed their performance parameters such as the X -ray sensitivity and dark-current for a amophous selenium multilayers X-ray detector with a phosphor layer, The hybrid digital X-ray image detector can be constructed by integrating a phosphor layer (or a scintillative layer) that convert X-ray to a light on a-Se photoconduction mulilayers that convert a light to electrical signal. As results, the dielectric materials such as parylene between the phosphor layer and the top electrode may reduce the dark-current of the samples. Amorphous selenium multilayers having dielectric layer(parylene) has characteristics of low dark-current, high X-ray sensitivity. So we can acquired a enhanced signal to noise ratio. In this paper offer the method can reduce the dark-current in the hybrid X-ray detector.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.29 no.11 s.242
• /
• pp.1182-1188
• /
• 2005

An x-ray PIV (Particle Image Velocimetry) technique was developed to measure quantitative information on flows inside opaque conduits and on opaque-fluid flows. At first, the developed x-ray PIV technique was applied to flow in an opaque Teflon tube. To acquire x-ray images suitable for PIV velocity field measurements, refraction-based edge enhancement mechanism was employed using detectable tracer particles. The optimal distance between with the sample and detector was experimentally determined. The resulting amassed velocity field data were in reasonable agreement with the theoretical prediction. The x-ray PIV technique was also applied to blood flow in a microchannel. The flow pattern of blood was visualifed by enhancing the diffraction/interference -bas ed characteristic s of blood cells on synchrotron x-rays without any contrast agent or tracer particles. That is, the flow-pattern image of blood was achieved by optimizing the sample (blood) to detector distance and the sample thickness. Quantitative velocity field information was obtained by applying PIV algorithm to the enhanced x-ray flow images. The measured velocity field data show a typical flow structure of flow in a macro-scale channel.

• Journal of Korea Multimedia Society
• /
• v.18 no.6
• /
• pp.701-709
• /
• 2015

Image stitching method is a technique for obtaining an high-resolution image by combining two or more images. In X-ray image for clinical diagnosis, the size of the imaging region taken by one shot is limited due to the field-of-view of the equipment. Therefore, in order to obtain a high-resolution image including large regions such as a whole body, the synthesis of multiple X-ray images is required. In this paper, we propose a rapid stitching method of digital X-ray images using template-based registration. The proposed algorithm use principal component analysis(PCA) and k-nearest neighborhood(k-NN) to determine the location of input images before performing a template-based matching. After detecting the overlapping position using template-based matching, we synthesize input images by alpha blending. To improve the computational efficiency, reduced images are used for PCA and k-NN analysis. Experimental results showed that our method was more accurate comparing with the previous method with the improvement of the registration speed. Our stitching method could be usefully applied into the stitching of 2D or 3D multiple images.

• Journal of radiological science and technology
• /
• v.42 no.5
• /
• pp.365-371
• /
• 2019

We have developed and applied a diagnostic Multi-Leaf Collimator (MLC) to optimized the X-ray field in medical imaging and the usefulness evaluated through the fusion of infrared image and X-ray image acquired by infrared camera. The hand and skull radiography with multi-leaf collimator(MLC) showed significant area dose reductions of 22.9% and 31.3% compared to ARC and leakage dose was compliant with KS A 4732. Also scattering doses of 50 cm and 100 cm showed a significant decrease to confirm the usefulness of MLC. It was confirmed that the fusion of infrared images with an adjustable degree of transparency was possible in the X-ray images. Therefore, fusion of anatomical information with physiological convergence is expected to contribute and improvement of diagnostic ability. In addition, the feasibility of convergence X-ray imaging and DITI devices and the possibility of driving MLC with infrared images were confirmed.

• Electronics and Telecommunications Trends
• /
• v.35 no.6
• /
• pp.37-47
• /
• 2020

Today, the volume of international trade by airplanes and ships is rapidly increasing, and the volume of trade over land is expected to increase as inter-Korean relations change. In customs processes, humans inspect using the naked eye; therefore, computer vision technology can be used to assist customs inspectors responsible for X-ray security screening. In particular, because of recent advances in deep learning technology, algorithms for image understanding and object detection performance are improving, and studies on their application to X-ray screening have been published. This manuscript describes trends in artificial intelligence X-ray image-reading technology to detect prohibited items. X-ray inspection AI technology is similar to the Sphinx, which was the guardian of the pyramids in ancient Egyptian mythology.