• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.2
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• pp.12-21
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• 2001

For the purpose of determining characteristics of widely used commercial medical x-ray films, which are used for obtaining the Linac-grams for radiational treatment of cancers, we placed several commercial x-ray films at a fixed distance form the linear accelerator. After varying the exposed amount of radiation step by step, we could obtain a continually increasing density image for each film whose densities were determined by microdensitometer readings. The characteristic curves of the films were obtained by plotting the densities vs. the exposed radiation amounts, and their ${\gamma}$ values were determined. These values can be used to suggest a minimum necessary amount of exposed radiation to get a useful Linac-gram. The measured ${\gamma}$ values of the characteristic curves of the Kodak-DVP/RA-1 film were 1.73 when used 6MV x-ray, 1.70 when used 15MV of intensity. For the Konica-AX film, ${\gamma}$ values were 1.29 and 1.18 respectively. The result suggests that the effective conditions for high resolution of a L-gram be 6 MV of x-ray intensity and about 3 rad of exposed dose on a Kodak-DVP/RA-1 film.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.97.1-102
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• 1999

This paper porposes and efficient coding scheme for remote medical communication systems, or“telemedicine systems”. These systems require a technique which is able to transfer large volume of data such as X-ray images effectively. We have already developed a hierarchical image coding and transmission scheme (HITS), which achieves an efficient transmission of medical images simply[1]. In this paper, a new coding scheme for HITS is proposed, which used hierarchical context modeling for the purpose of improving the coding efficiency. The hierarchical context modeling divides wavelet coefficients into several sets by the value of a correspondent coefficient in their higher class, or“a parent”, optimizes a Golomb-Rice (GR) code parameter in each set, and then encodes the coefficients with the parameter. Computer simulation shows that the proposed scheme is effective with simple implementation. This is due to fact that a wavelet coefficient has dependence on its parent. As a result, high speed data transmission is achieved even if the telemedicine system consists of simple personal computers.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.601-615
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• 2015

Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.713-718
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• 1998

The paper is part of an investigation by the authors on development of a knowledge based frame work for multimodal medical image in collaboration with the All India Institute of Medical Science, new Delhi. After presenting the key aspects of the Dempster-Shafer Evidence theory we have presented implementation of registration and fusion of T₁and T₂ weighted MR images and CT images of the brain of an Alzheimer's patient for minimising the uncertainty and increasing the reliability for dianostics and therapeutic planning.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.196-201
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• 2023

In recent years, virtual reality (VR) technology has been widely used in many industrial fields, especially in the fields of medical treatment, games, film and television, to improve the interaction between medical teaching and practical treatment. On the gaming side, the production of virtual reality gaming screens and scenes became increasingly popular, greatly expanding the form of the visual experience. But VR is no longer confined to the health care, education and entertainment industries. During this time, the news media industry has also begun to integrate virtual reality into interviews and user interactions. This study aims to analyze the development of VR technology from the perspectives of immersive VR news experience, real reporting, and prospects, and analyze and think about the interactive participation of media users, the transformation of traditional media, and the upgrading of practitioners' roles.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.124-127
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• 1995

Due to the self-diffusion of nuclear spins, the edge of phantoms is enhanced in the magnetic resonance imaging (MRI), especially in the case of microscopy [1]. According to several published works, theory has been established that the edge enhancement is caused by the motion narrowing around bounded regions due to diffusions of nuclear spins during data acquisition. It is found, however, that the signal decreases due to the diffusion attenuation and image is distorted as edge of the image is sharpened. In this paper, we wilt investigate this signal loss during data acquisition and its effects on image, i.e., image edge enhancement due to the diffusion phenomenon. This result is new and different from the previously discussed edge enhancement due to the diffusion, namely, by motion narrowing effect or spin bouncing effect at the boundary.

• Journal of the Korean Graphic Arts Communication Society
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• v.20 no.2
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• pp.153-160
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• 2002

Two different commercial brand X-ray films are used for examining possible differences caused by different target distances when the patients are examined with highenergy X-rays. 6MV- and 15MV X-rays are tested at four different target distances. The films on which the radiation amounts are gradually increased using H$_2$O phantoms are developed by an automatic developing machine to be analysed in the image densities. Characteristic curves have similar shapes for different conditions but for the 130cm target distance. ${\gamma}$ values and average image densities per the illuminated radiation are used to analyse the differences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.960-966
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• 2017

In this paper, we proposed an algorithm that can extract lesion by inputting a medical image. Feature points are extracted using SIFT algorithm to extract genetic training of medical image. To increase the intensity of the feature points, the input image and that raining image are matched using vector similarity and the lesion is extracted. The vector similarity match can quickly lead to lesions. Since the direction vector is generated from the local feature point pair, the direction itself only shows the local feature, but it has the advantage of comparing the similarity between the other vectors existing between the two images and expanding to the global feature. The experimental results show that the lesion matching error rate is 1.02% and the processing speed is improved by about 40% compared to the case of not using the feature point intensity information.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.1-6
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• 2014

In many medical image devices, dc noise often prevents normal diagnosis. In wireless capsule endoscopy systems, multipath fading through indoor wireless links induces inter-symbol interference (ISI) and indoor electric devices generate impulsive noise in the received signal. Moreover, dc noise, ISI, and impulsive noise are also found in optical fiber communication that can be used in remote medical diagnosis. In this paper, a blind signal processing method based on the biased probability density functions of constant modulus error that is robust to those problems that can cause error propagation in decision feedback (DF) methods is presented. Based on this property of robustness to error propagation, a DF version of the method is proposed. In the simulation for the impulse response of optical fiber channels having slowly varying dc noise and impulsive noise, the proposed DF method yields a performance enhancement of approximately 10 dB in mean squared error over its linear counterpart.

• 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.