• 한국정보통신학회논문지
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• 제15권8호
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• pp.1782-1790
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• 2011

영상처리의 목적은 관측자를 위해서 영상의 시각적인 일면을 증진하는 것이다. 영상증진의 목적은 특정 응용분야에 따라서 달라지며, 또한 특정 목적을 위해서 사용되는 영상 증진 기법들은 다른 응용분야에는 적용되지 못하는 실정이다. 본 논문에서는 먼저 히스토그램 쉬링크 및 평활화, 보수적인 적응적 컨트라스트 증진 필터등에 대해서 살펴 보고져 한다. 그리고 적응적인 컨트라스트 증진 필터 기법을 의료영상에 맞게 구성하는 변수들의 적용값을 최적화했으며, 후 처리로 히스토그램 평활화 기법을 사용했다. 결과적으로 입력치인 의료영상들을 사용하여 적용한 결과 제안한 필터를 적용한 결과치 영상들의 에지가 강조됨을 보였고, 또한 후처리로 인해서 영상외형의 컨트라스트를 향상시켰다.

• 대한디지털의료영상학회논문지
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• 제7권1호
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• pp.33-38
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• 2005

Modern hospitals have been greatly facilitated with information technology (IT) such as hospital information system (HIS). One of the most prominent achievements is medical imaging and image data management so-called Picture Archiving and Communication Systems (PACS). Due to inevitable use of diagnostic images (such as X-ray, CT, MRI), PACS made tremendous impact not only on radiology department but also nearly all clinical departments for exchange and sharing image related clinical information. There is no doubt that better use of PACS leads to highly efficient clinical administration and hospital management. However, due to rapid and widespread acceptance of PACS storage and management of digitized image data in hospital introduces overhead and bottleneck when transferring images among clinical departments within and/or across hospitals. Despite numerous technical difficulties, financing for installing PACS is a major hindrance to overcome. In addition, a mirroring or a clustering backup can be used to maximize security and efficiency, which may not be considered as cost-effective approach because of extra hardware expenses. In this study therefore we have developed a new based on grid of distributed PACS in order to balance between the cost and network performance among multiple hospitals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• Journal of Information Science Theory and Practice
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• 제11권1호
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• pp.14-30
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• 2023

The purpose of this study is to compare research trends in the information and communication technology (ICT) field between North and South Korea and analyze the differences by using data mining. Frequency analysis, clustering, and network analysis were performed using keywords from seven South Korean and two North Korean ICT academic journals published for five years (2015-2019). In the case of South Korea (S. Korea), the frequency of research on image processing and wireless communication was high at 16.7% and 16.3%, respectively. North Korea (N. Korea) had a high frequency of research, in the order of 18.2% for image processing, 16.9% for computer/Internet applications/security, and 16.4% for industrial technology. N. Korea's natural language processing (NLP) sector was 11.9%, far higher than S. Korea's 0.7 percent. Student education is a unique subject that is not clustered in S. Korea. In order to promote exchanges between the two Koreas in the ICT field, the following specific policies are proposed. Joint research will be easily possible in the image processing sector, with the highest research rate in both Koreas. Technical cooperation of medical images is required. If S. Korea's high-quality image source is provided free of charge to N. Korea, research materials can be enriched. In the field of NLP, it calls for proposing exchanges such as holding a Korean language information conference, developing a Korean computer operating system. The field of student education encourages support for remote education contents and management know-how, as well as joint research on student remote evaluation.

• 대한디지털의료영상학회논문지
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• 제6권1호
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• pp.51-64
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• 2003

This thesis describes the effective interfacing methods of PACS Modality based on the system installation andoperating experiences. PACS(Picture Archiving and Communication Systems) is a system for medical image archiving and communication using large storage device and high-speed network. The standard communication protocol of PACS is DICOM(Digital Imaging and Communication in Medicine) based on TCP/IP and point-to-point protocol. However, there are many Non-DICOM Modalities and DICOM Modalities having problems. First, we had interfaced almost modalities. Fuji CR, GE CT, MRI, Angio, Fluoro, Phillips Angio, Shimadzu Fluoro. Ultrasound PACS, with the main PACS in the Seoul S Hospital as large scale hospital. And we manipulated the intelligent image distribution and the CT. MRI Interfaces never experienced beforein the Anyang J Hospital and the Chungju C Hospital as mid or small scale hospital. Technically, we developed both the DICOM Interface and the Non-DICOM Interface. At the last, the DICOM Worklist and the DICOM Print Interface were implemented in the Seoul B Hospital, the Bucheon SJ Hospital and the Seoul K Hospital independently with PACS. The Oracle, Sybase and MS-SQL are used as database, and UNIX, Macintosh, MS Windows as operating systems. And the Visual C++ and UNIX C are the main programming tools. We have used UTP, coaxial and fiber optic cable under 10/100 mbps LAN for networking.

• 대한디지털의료영상학회논문지
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• 제5권1호
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• pp.46-63
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• 2002

This thesis describes the effective interfacing methods of PACS Modality based on the system installation and operating experiences. PACS(Picture Archiving and Communication Systems) is a system for medical image archiving and communication using large storage device and high-speed network. The standard communication protocol of PACS is DICOM(Digital Imaging and Communication in Medicine) based on TCP/IP and point-to-point protocol. However, there are many Non-DICOM Modalities and DICOM Modalities having problems. First, we had interfaced almost modalities, Fuji CR, GE CT, MRI, Angio, Fluoro, Phillips Angio, Shimadzu Fluoro, Ultrasound PACS, with the main PACS in the Seoul S Hospital as large scale hospital. And we manipulated the intelligent image distribution and the CT, MRI Interfaces never experienced before in the Anyang J Hospital and the Chungju C Hospital as mid or small scale hospital. Technically, we developed both the DICOM Interface and the Non-DICOM Interface. At the last, the DICOM Worklist and the DICOM Print Interface were implemented in the Seoul B Hospital, the Bucheon SJ Hospital and the Seoul K Hospital independently with PACS. The Oracle, Sybase and MS-SQL are used as database, and UNIX, Macintosh, MS Windows as operating systems. And the Visual C++ and UNIX C are the main programming tools. We have used UTP, coaxial and fiber optic Gable under 10/100 mbps LAN for networking.

• 한국전자통신학회논문지
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• 제10권12호
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• pp.1403-1410
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• 2015

본 연구에서는 병원정보시스템에서 분야별로 발생하는 의료 빅데이터 자료를 활용하여 가치있는 의료정보를 생성하고 활용할 수 있는 방안을 마련하고자 한다. 본 연구의 결과는 첫 번째, 의료정보시스템의 진료정보와 각종 검사장비 및 의료영상장비와 연동된 PACS의 발생자료를 통합하고 의료 빅데이터를 분석하여 새로운 의료정보를 생성한다. 이렇게 생성된 의료정보는 감염병 및 질병 예방과 질병의 치료를 위한 다양한 건강정보를 생성하게 된다. 두 번째, 환자의 접수내역과 수납내역 그리고 청구내역들을 통합하여 축적해온 의료 빅데이터를 분석하여 다양한 수익통계정보를 생성한다. 이렇게 생성된 수익통계정보는 의료기관의 운영과 수익분석에 활용하기 위한 다양한 경영정보를 생성하게 된다. 이와 같이 병원정보시스템에서 발생하는 의료정보와 공공기관의 의료정보 그리고 개인건강기록의 자료들이 통합이 되면 의료자료를 활용한 가치있는 보건의료정보를 창출하게 된다.

• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2007년도 춘계학술대회 논문집
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• pp.71-72
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• 2007

In this study and design, I explore the visual communication design process related to wayfinding in Wonkwang University Hospital in Iksan City. The purpose for designing an integrated is to graphically communicate the complex information. I have designed a graphic information system combining graphic elements such as typography, color, image and text layout, and graphic image to present interpretive and communicative processes. As a result of designing, the wayfinding is s term used to describe the program of applying environmental graphics and or architectural sign systems. In other words, wayfinding is the art of helping people find their way by utilizing visual communications such as signs, directories, landmarks, edges, paths and so on. Wayfinding can also be explained as the ability to know where one is in space and how to find one's way through space. Wayfinding design then becomes a systematic arrangement of the variables that allows patients and visitors to successfully find their desired destinations, especially in a hospital.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• 의학교육논단
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• 제20권3호
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• pp.141-149
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• 2018

This study examined doctors' images represented in the Korean press to identify overall public perceptions of doctors and to suggest areas for improvement to enhance their image. All news reports published in the two major Korean daily newspapers between years 2011 and 2015 related to doctors and the practice of medicine were searched and collected. The news reports were categorized into the five competency domains in the Korean doctor's role (i.e., patient care, communication and cooperation, social accountability, professionalism, and education and research). Each news item was coded as being either positive or negative and was given a score regarding the extent to which positive or negative image that it represents of doctors using the Doctor Image Scale (DIS) score. A total of 314 news reports were collected, a majority of which were on patient care (36%), professionalism (33%), and social accountability (23%). Positive stories slightly outnumbered negative ones (56% vs. 44%). The largest number of positive news reports was in patient care (n=82); negative news reports most frequently appeared in professionalism (n=99) and patient care (n=32). The total DIS score was also positive (+28): the highest positive DIS score was in social accountability (+164); the highest negative DIS score was obtained in professionalism (-226). This study revealed overall positive portrayals of doctors in the Korean press, yet doctors need to better comply with regulations and ethical guidelines and enhance their medical knowledge and clinical skills and to improve their image.