• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.87-94
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• 2008

Iris diagnosis is an alternative medicine technique whose proponents believe that patterns, colors, and other characteristics of the iris can be examined to determine information about a patient's systemic health. However, because most of previous studies only find a sign pattern in a gray iris image, they are not enough to be used for a iris diagnosis system. In this paper, we developed an iris diagnosis system based on a color image processing and iris medical information stored in database. The system includes four modules : input module with an iris camera, iris signs extraction module, medical database, output module with printing. Based on a color image processing approach, this paper presents the extraction algorithms of 7 major iris signs and correction manually for improving the accuracy of analysis. We can use the iridology and patient's health DB in the stage of signs analysis. Compared with the existing system, the developed system can be applied to an iris diagnosis system since it provides various additional functions.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.147-159
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• 2013

In medical visualization, volume visualization is widely used. Applying 3D images to diagnose requires high resolution and accurately implement visualization techniques are being researched accordingly. However, when a three-dimensional image volume visualization is implemented using volume data, aliasing will occur since using discrete data. Supersampling method, getting lots of samples, is used to reduce artifacts. One of the supersampling methods is Catmull-rom spline. This method calculates accurate interpolation value because it is easy to compute and pass through control points. But, Catmull-rom spline method occurs overshoot or undershoot in large gradient of pixel values. So, interpolated values are different from original signal. In this paper, we propose an adaptive adjusting weights interpolation method using Gaussian function. Proposed method shows that overshoot is reduced on the point has a large gradient and PSNR is higher than other interpolated image results.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.289-308
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• 2024

This study investigates the collaboration networks in the field of AI-driven diagnostic medical imaging, focusing on the influence of two social capital concepts-network closure and structural holes-on research performance. The analysis reveals a highly fragmented network structure with one dominant component, while individual clusters exhibit strong internal cohesion. Both network closure, measured by density, and structural holes, assessed through efficiency, positively impact research performance, as demonstrated by QAP regression analysis. The findings highlight that, in the integration of AI into diagnostic medical imaging, robust connections among researchers are vital, and the presence of structural holes, which enable the assimilation of diverse knowledge, also significantly enhances research outcomes. This underscores the importance of fostering a well-balanced network to optimize collaboration and knowledge production in this emerging interdisciplinary field.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.231-236
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• 2014

The transit time of contrast agents and the parameters of time-intensity curves in ultrasonography are important factors to diagnose various diseases of a digestive organ. We have implemented an automatic parametric imaging method to overcome the difficulty of the diagnosis by naked eyes. However, the micro-bubble noise and the respiratory motions may degrade the reliability of the parameter images. In this paper, we introduce an optimization technique based on MRF(Markov Random Field) model to enhance the quality of the parameter images, and present an image tracking algorithm to compensate the image distortion by respiratory motions. A method to extract the respiration periods from the ultrasound image sequence has been developed. We have implemented the ROI(Region of Interest) tracking algorithm using the dynamic weights and a momentum factor based on these periods. An energy function is defined for the Gibbs sampler of the image enhancement method. Through the experiments using the data to diagnose liver lesions, we have shown that the proposed method improves the quality of the parametric images.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.443-445
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• 2021

갑상선 결절(thyroid nodule)은 검진 인구에서 빈번하게 진단되는 질환이지만 현재까지 진단방법은 경험적이며 정성적 판단에 의존하고 있는 실정이다. 본 연구는 갑상선 결절을 평가하기 위하여 시행한 초음파 의료영상을 이용하여 정량 분석할 수 있는 소프트웨어를 개발하였으며 갑상선 양성 결절환자에서의 임상활용 가능성을 평가하고자 한다. 임상 연구는 총 13명의 갑상선 양성 결절 환자를 대상으로 하였다. 환자별 갑상선 초음파영상을 이용하여 정상부위와 병변부위에서 정량 지표인 변동계수를 각각 측정하였다. 환자별 정상부위와 병변부위의 변동계수 차이는 대응표본 T 검정을 사용하여 비교하였으며 유의한 차이를 확인할 수 있었다. 본 연구를 통하여 개발한 정량분석 소프트웨어를 실제 갑상선 양성 결절 환자에서 갑상선 결절을 분석·평가하는데 활용할 수 있을 것으로 판단된다.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.45-51
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• 2005

This paper emphasizes on the accomplishment of compensated proton density image and T2 weighted image taken from the shrinkage surface of the Brain. From the images, the Brain's surface shrinkage in the normal image and the surface shrinkage in the abnormal image can be observed. After the separation of white matter, gray matter, and CSF, this algorithm calculates the volume of each of them automatically. Results are subdivided into particular ages and saved in the database to be analyzed and to be processed statistically. Therefore, by using this algorithm the normal and abnormal stages can be detected in the early stages to diagnose. This result easily discernment Alzheimer patient and is useful for Alzheimer diagnostic and early detection.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.1-5
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• 2003

의료영상의 분할은 의료영상을 컴퓨터 진단 및 가시화에 필요한 같은 성질을 가진 여러 조직으로 나누어주는 방법이다. 즉 입력되어진 영상을 처리하여 유사한 화소들의 집합인 영역들로 화소들을 구분하는 작업이며 영상분할의 결과는 영상인식의 정확성에 큰 영향을 미친다. MRI(Magnetic Resonance Imaging)으로부터 정상적인 세포조직 또는 뇌종양과 같은 비정상적인 세포조직의 가시화와 분석을 위해서는 대상 세포조직의 적절한 분류를 필요로 한다. 하지만 기존의 영역 검출 방법으로는 잡음이 섞여 있는 영상에서 여러 가지의 처리과정(주로 잡음 제거)이 필수적이고 그런 과정으로 인해 정확한 영역 검출이 힘들게 된다. 이에 잡음이 있더라도 이를 제거하기 위한 처리가 필요 없이 영역기반으로 필요한 파라미터의 추정을 통한 MRF(Markov Random Field)를 이용하여 보다 효율적이고 정확하게 MRI에서 질환 영역을 검출할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.703-705
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• 2004

의학적 진단이나 치료를 하는 과정에선 실험이나 촬영을 통한 사진이나 영상이 많이 생성된다 의학용 이미지들은 각각 생성된 환경에 따른 특성을 가지고 있기 때문에. 효율적으로 이미지를 관리하기 위해서는 이미지의 특징이나 증상과 같은 정보들을 함께 표현해야한다. 본 논문에서는 의료 영상이 나타내는 정보를 표기하고, 카메라로부터 입력받는 영상을 출력 및 분석할 수 있는 컴포넌트웨어에 대해서 살펴본다. 본 논문에서 개발한 컴포넌트웨어는 다른 프로그램에서도 쉽게 영상관련 처리를 할 수 있도록 COM 기반의 Activex 컨트롤로 개발하였으며 이미지의 주석기능과 더불어 각각의 이미지나 카메라로부터 입력받는 동영상의 색 공간변환 및 왜곡보정 등의 기능을 가지고 있어, 보다 정교한 작업을 요하는 의료 영상처리 프로그램에 손쉽게 추가하여 사용할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.296-299
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• 2011

임상 진단에서 환자의 의료 영상을 시각적으로 보고 해석하거나 또는 수작업으로 영상을 해석하여 진단에 이용한다. 이러한 수작업의 불편함을 해소하기 위하여 의료 영상처리 알고리즘들이 많이 연구되어오고 있다. 그 중 영상처리의 정확도 부분이 많은 문제가 되고 있는데, 특히 세포영상에서는 인접한 영역의 분할이 가장 중요시되고 있다. 본 논문은 이러한 인접영역의 분할을 위해 객체의 기하 정보인 곡률(Curvature) 정보와 컨벡스 헐(Convex Hull)을 통한 분할 방법을 제안하고자 한다. 실험 결과 87.5%의 정확도가 검출되었으며 향후 인접 객체의 내부정보까지 고려한 효과적인 분할 방법을 연구하고자 한다.