• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• 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.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.76-77
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• 2003

Digital Radiograpy ＆ Fluoroscopy(DRF 또는 DR 또는 DF)는 cone beam을 이용하여 인체를 투과한 X선을 영상증배관(Image Intensifying Tube： IIT)을 통하여 가시광선으로 변환시킨 후 영상을 카메라로 보내고, 이곳에서 발생한 영상정보를 디지털로 처리하여 모니터를 통해 눈에 보이는 영상으로 만드는 방법으로 IIT에 기초한 디지털 방사선 촬영술이라고도 한다. DF 방법은 즉시 영상 표시와 진단이 가능하기 때문에 즉시성이 요구되는 심장이나 두복부 등의 순환기 분야에서 DSA(Digital Subtraction Angiography) 장비로 이용되고 있고, 순환기뿐만 아니라 위를 중심으로 한 소화관(식도, 위, 소장, 대장, 직장)의 분야에서 적용 가능하다. (중략)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1407-1413
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• 2006

The field of medical images has been digitalized as the development of computer and the digitalization of the medical instruments. As a result it causes a lot of problems such as an illegal copy related to medical images and property right of the medical images. Therefore, digital watermarking is used for discrimination whether the data are modified or not. It is also used to protect both the property right of medical images and the private life of many patients. The proposed theories, the Non-blind and the Blind method, have two problems. One is needed an original image and the other is using a gaussian watermarking. This paper proposes the new Blind Watermarking using binary images in order to easily recognize the results of watermark. This algorism is described that an watermark of a binary image is wavelet-transformed, and then a transformed watermark is inserted in medium-band of frequency domains of original image by the Circular Input method. This method don't have any loss when image didn't have any attack. As a result Watermark can be perfectly extracted by using this algorithm. And Maximam PSNR value is improved 3.35dB. This algorithm will be improved by using gray level image and color image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.40-43
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• 2012

3차원 영상 기술은 방송, 영화, 게임, 의료, 국방 등 다양한 기존 산업들과 융합하며 새로운 패러다임을 형성하고 있으며, 고품질 및 고해상도의 3차원 영상 획득에 대한 필요성이 강조되고 있다. 이에 따라, 최근에는 3차원 입체 영상을 제작 하는 방법 중 하나인 2D-plus-Depth 구조에 대한 연구가 활발히 진행되고 있다. 2D-plus-Depth 구조는 Charge-Coupled Device(CCD) 센서 등을 이용한 일반 카메라와 깊이 카메라를 결합한 형태로써 이 구조로부터 얻은 깊이 영상의 해상도를 상향 변환하기 위해서 Joint Bilateral Upsampling(JBU)[1], 컬러 영상의 정보를 활용한 보간법[2] 등의 방법들이 사용된다. 하지만 이 방법들은 깊이 영상을 높은 배율로 상향 변환할 경우 텍스처가 복사되거나 흐림 및 블록화 현상이 발생하는 문제점이 있다. 본 논문에서는 2D-plus-Depth 구조에서 얻은 고해상도 컬러 영상에서 보간 정보를 구하고 이 정보를 저해상도의 깊이 영상에 적용하여 상향 변환된 가이드 깊이 영상을 제작한다. 이 가이드 깊이 영상을 Bilateral Filtering[8]을 이용함으로써 고품질의 고해상도 깊이 영상을 획득한다. 실험 결과 제안하는 방법으로 해상도를 상향 변환을 할 경우에 기존의 보간법들에 비해 깊이 영상의 특성을 잘 보존함을 확인할 수 있고, 가이드 깊이 영상에 필터링을 처리한 결과가 JBU의 결과보다 향상됨을 확인할 수 있다.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.275-286
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• 2011

As the 21st century paradigm of healthcare service changes from post-therapeutic treatment to disease prevention and management in advance, the M2M-based u-healthcare application service is increasingly important. M2M-based u-healthcare application service uses mobile handsets equipped with sensors to measure vital information, and the medical staff in remote locations can manage the health of the patient or the public in real time. In this paper, IEEE/HL7 translation gateway, utilizing the gateway based on M2M u-healthcare application service structure, which is based on international standards, has been designed and implemented. Specifically, the gateway between ISO/IEEE 11073 standards and ANSI HL7 has been developed. The former defines the protocol for the exchange of information between the agent device and the manger devices for measuring and handling biological signal, and the latter defines the application layer protocol for the exchange of various healthcare information systems. Finally, in order to demonstrate the functionality of the proposed architecture, the M2M-based U-healthcare application service based on IEEE/HL7 translation gateway, utilizing the gateway, has been developed which can measure, collect and process a variety of vital signs such as ECG or SpO2.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.704-709
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• 2006

In this study, we suggest the effective storage structure and management method for XML-based electrocardiography(ECG) data to support the interoperability between medical information systems, and implement the metadata system of ECG data providing the web-based information service. ECG matadata management system include functions for storing and managing as well as reporting PDF service of ECG data. We analyzed a characteristics of the data and access patterns for XML-based ECG and then used the non-partitioning storing method and indexing the extracted metadata from the HL7 aECC for supporting the quick search. We, using the template mechanism, converts the XML-based results data into various formats in order to provide services of the ECG reporting.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.731-736
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• 2023

Recently, the medical field has been applying mandatory Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) systems that computerize and manage medical records, and distributing them throughout the entire medical industry to utilize patients' past medical records for additional medical procedures. However, the conversations between medical professionals and patients that occur during general medical consultations and counseling sessions are not separately recorded or stored, so additional important patient information cannot be efficiently utilized. Therefore, we propose an electronic medical record system that uses speech recognition and natural language processing deep learning to store conversations between medical professionals and patients in text form, automatically extracts and summarizes important medical consultation information, and generates electronic medical records. The system acquires text information through the recognition process of medical professionals and patients' medical consultation content. The acquired text is then divided into multiple sentences, and the importance of multiple keywords included in the generated sentences is calculated. Based on the calculated importance, the system ranks multiple sentences and summarizes them to create the final electronic medical record data. The proposed system's performance is verified to be excellent through quantitative analysis.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.309-316
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• 2020

We predict the mortality of the elderly patients visiting the emergency department who are over 65 years old using Feed Forward Neural Network (FFNN) and Convolutional Neural Network (CNN) respectively. Medical data consist of 99 features including basic information such as sex, age, temperature, and heart rate as well as past history, various blood tests and culture tests, and etc. Among these, we used random forest to select features by measuring the importance of features in the prediction of mortality. As a result, using the top 80 features with high importance is best in the mortality prediction. The performance of the FFNN and CNN is compared by using the selected features for training each neural network. To train CNN with images, we convert medical data to fixed size images. We acquire better results with CNN than with FFNN. With CNN for mortality prediction, F1 score and the AUC for test data are 56.9 and 92.1 respectively.