• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.151-154
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• 1996

A medical record and radiographic image transmission system has been developed using high speed communication network. The databases are designed to store and transmit the data acquired from the scanner. To maximally utilize the communication bandwidth, the medical records and radiographic images are compressed using the G3 facsimile and JPEG coding standard method respectively. TCP/IP, OOP and window based system software enables the modular design, future expandability, open system interconnectivity, and graphical user interface. In addition, the fast and easy data base access capability and diverse image manipulation functions are also implemented.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.33-40
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• 2002

In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11A
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• pp.870-881
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• 2009

The rapid development of healthcare information management for 3D medical digital library, 3D PACS and 3D medical diagnosis has addressed security issues with medical IT technology. This paper presents multiple 3D medical image data for protection, authentication, indexing and diagnosis information hiding applied to healthcare information management. The proposed scheme based on POCS watermarking embeds the robust watermark for doctor's digital signature and information retrieval indexing key to the distribution of vertex curvedness and embeds the fragile watermark for diagnosis information and authentication reference message to the distance difference of vertex. The multiple embedding process designs three convex sets for robustness, fragileness and invisibility and projects 3D medical image data onto three convex sets alternatively and iteratively. Experimental results confirmed that the proposed scheme has the robustness and fragileness to various 3D geometric and mesh modifiers at once.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.382-384
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• 2002

Filtered-Back-Projection technique is used in X-ray CT image reconstruction. This requires X-ray transmission data from all directions. As the transverse cross-section of the body is approximately 50 cm, transmitted X-rays in this direction are strongly attenuated. If X-ray transmission data in this direction is avoided, exposure to the patients seems to be reduced one 20th of usual value. Some alternative method has to be found for clinically sufficient image quality. New methods are under development and tentative results are reported that utilizes the principle of superposition.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.167-171
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• 2004

The evolution of modern imaging modalities, followed by the rapid development of computer technology has introduced many new features in the communication networks used in medical facilities. Since it is very important to keep patient's record accurately, the ability to exchange medical data securely over the communication network is essential for any medical information. In this paper, therefore, we introduce some problems which occur from digitizing medical images such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), CR(Computed Radiography), etc., and then we propose a authentication mechanism for medical image verification using secret watermark images.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.173-178
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• 1989

A vector quantization technique using a variable block size was applied to image compression of digitized X -ray films. Whether the size of VQ block should be subdivided or not is determined experimentally by the threshold value. The simulation result shows that the performance of the proposed vector quantizer is suitable for the medical image coding, which is applicable to PACS( Picture Archiving and Communication System).

• The Pure and Applied Mathematics
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• v.11 no.1
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• pp.1-14
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• 2004

Due to the development of computer network and mobile communications, the security in image data and other related source are very important as in saving or transferring the commercial documents, medical data, and every private picture. Nonetheless, the conventional encryption algorithms are usually focusing on the word message. These methods are too complicated or complex in the respect of image data because they have much more amounts of information to represent. In this sense, we proposed an efficient secret symmetric stream type encryption algorithm which is based on Boolean matrix operation and the characteristic of image data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.373-385
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• 2022

Due to the lack of information, medical tourists are regarded to be at high risk. Prior medical tourism research has found that various types of perceived risks have a significant impact on medical tourists' purchase behavior. Even though medical tourism is predicted to increase, there is a lack of behavioral research to explain how perceived risks affect medical tourists' purchase behavior. In the context of Korean medical tourism, this study attempts to evaluate the effects of multi-level (macro, organizational, and personal) factors on medical tourists' perceived risks and purchase intentions. A conceptual model and hypotheses were built and empirically validated to investigate links between multi-level characteristics, perceived risks, and purchasing intentions. The data for this study was collected from Chinese tourists using a questionnaire. The impact of cognitive country image, affective country image, and medical service quality on fundamental risk is confirmed by statistical testing. Surprisingly, expectancy discrepancy risk is influenced only by cognitive country image and information search capabilities. Both fundamental and expectation discrepancy risks lower medical tourists' purchase intentions. The findings of this study show that a multi-level strategy is required to investigate the links between perceived risks and medical tourism purchasing intentions based on macro, organizational, and personal factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.500-508
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• 2008

For efficient compatible system between medical clinics, the medical information has to be built on a standardized protocol such as a HL7 for text data and a DICOM for image data. But it is difficult to exchange information between medical clinics because the systems and softwares are different and also a structure of data and a type of code. Therefore we analyze a structure of DICOM file and design an integrated database for effective information sharing and exchange. The WISD system suggested in this paper separate the DICOM file transmitted by medical clinics to text data and image data and store it in the integrated DB(database) by standardized protocol respectively. It is very efficient that each medical clinic can search and exchange information by web browser using the suggested system. The WISD system can not only search and control of image data and patient information through integrated database and internet, but share medical information without extra charge like construction of new system.

• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.