• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.91-97
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• 2015

In this paper, we designed a emergency medical image communication system EMICS added concept of emergency medical image to the existing emergency medical information system based on DICOM. Also we suggested a emergency medical image object EMISPS of EMICS. Using EMICS, the emergency medical technician can work together with emergency doctor. Therefore the patient can take more stable care than existing emergency medical information system. Using EMISPS, the emergency medical technician can get exact situation information of the patient.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6177-6200
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• 2018

In order to achieve the goal of protecting medical images, some existing watermark techniques for medical image protection mainly focus on improving the invisibility and robustness properties of the method, in order to prevent unnecessary medical disputes. This paper proposes a novel copyright method for medical image protection based on visual secret sharing (VSS) and cellular automata transform (CAT). This method uses the protected medical image feature as well as VSS and a watermark to produce the ownership share image (OSI). The OSI is used for medical image verification and must be registered to a certified authority. In the watermark extraction process, the suspected medical image is used to generate a master share image (MSI). The watermark can be extracted by combining the MSI and the OSI. Different from other traditional methods, the proposed method does not need to modify the medical image in order to protect the copyright of the image. Moreover, the registered OSI used to verify the ownership and its appearance display meaningful information, facilitating image management. Finally, the results of the final experiment can prove the effectiveness of our method.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.37-47
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• 2019

The purpose of this paper is to construct a system that matches the patient's image disease information with the medical image viewer in providing the medical image information to the medical staff. Currently, medical image information systems that are commercialized mostly provide only one image viewer with various image information of diseases or use incompatible exclusive viewers. For this reason, we designed and implemented a medical image information viewer matching system that integrates and provides specialized viewers that can be selected by diseases' image information. That is, it is a system to match and view medical image viewers based on disease information extracted from tag information stored as the metadata in DICOM file, which is medical image information standard, for disease-specific viewer matching. We analyzed the execution performances through our retrieval service of medical image information from our implementation system, and showed compatibility and control with various viewers.

• Health Policy and Management
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• v.24 no.4
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• pp.367-379
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• 2014

Background: Over the last decade, medical tourism industry has grown in Korea. Especially the number of Mongolian medical tourists has increased rapidly. Therefore, the Mongolia is one of the targets for Korea medical tourism. The purpose of this study is to investigate the effects of destination image and expected attributes of medical services on Mongolian's intention to use Korean medical tourism service. Methods: This study empirically collected survey data from Mongolian lived in Mongolia. The study analyzed the data using a PLS model. Results: Our results are as follows. First, the country image didn't significantly have causal effects on expected medical service quality and perceived risk. Second, tourism image (e.g., entertainment, economic feasibility, and local convenience) has significantly causal effects on expected medical service quality and perceived risk. However, tourist site as tourism image didn't significantly have causal effects on expected medical service quality and perceived risk. Third, medical image made a statistically significant effect on expected medical service quality and perceived risk. Fourth, the expected medical service quality showed a significant effect on intention to use Korean medical tourism service. Fifth, the perceived risk of medical tourism showed a significant effect on the reliability of medical tourism, but didn't show a significant effect on the intention to use Korean medical tourism service. Finally, the reliability has a significant effect on the intention to use Korean medical tourism service. Conclusion: From our empirical results, this study concluded that as a strategy attracting Mongolian patients, it is more effective to strengthen Korean hospital image and tourism image than Korean country image.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.121-128
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• 1994

In this paper, MIlS (Medical Image Information System) has been designed and implemented using INGRES RDBMS, which is based on a client/server architecture. The implemnted system allows users to register and retrieve patient information, medical images and diagnostic reports. It also provides the function to display these information on workstation windows simultaneously by using the designed menu-driven graphic user interface. The medical image compression! decompression techniques are implemented and integrated into the medical image database system for the efficient data storage and the fast access through the network.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.225-228
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• 2003

The purpose of this paper is to develop a semi -automated system for medical image processing with which tissues or organs from medical images can be segmented and classified by people who have basic knowledge of image processing. In addition, the proposed medical image processing system is independent on types of human tissues or images. In this paper, a new semi-automated image processing system with essential image processing functions for medical images is introduced

• 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.

• ETRI Journal
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• v.39 no.2
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• pp.173-180
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• 2017

Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.