• Journal of Multimedia Information System
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• 제3권3호
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• pp.47-52
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• 2016

This research paper examines the usage of graphic imaging in Holographic Projections to further advance the medical field. It highlights the importance and necessity of this technology as well as avant-garde techniques applied in the process of displaying images in digital holography. This paper also discusses the different types of applications for holograms in society today. Different tools were utilized to transfer a set of a cancer patient's brain tumor data into data used to produce a 3D holographic image. This image was produced through the transfer of data from one program to another. Through the use of semi-automatic segmentation through the seed region method, we were able to create a 3D visualization from Computed Tomography (CT) data.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.209-213
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• 2023

In recent years, the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer tumors such as the liver cancer. Liver cancer has been attracting the attention of medical and sciatic communities in the latest years because of its high prevalence allied with the difficult treatment. Statistics indicate that liver cancer, throughout world, is the one that attacks the greatest number of people. Over the time, study of MR images related to cancer detection in the liver or abdominal area has been difficult. Early detection of liver cancer is very important for successful treatment. There are few methods available to detect cancerous cells. In this paper, an automatic approach that integrates the intensity-based segmentation and k-means clustering approach for detection of cancer region in MRI scan images of liver.

• 한국멀티미디어학회논문지
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• 제17권5호
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• pp.566-572
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• 2014

The hippocampus has been known as one of the most important structure related to many neurological disorders, such as Alzheimer's disease. This paper presents the snake model to segment hippocampus from brain MRI. The snake model or active contour model is widely used in medical image processing fields, especially image segmentation they look onto nearby edge, localizing them accurately. We applied a snake model on brain MRI. Then we compared our results with an active shape approach. The results show that hippocampus was successfully segmented by the snake model.

• 한국멀티미디어학회논문지
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• 제15권12호
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• pp.1409-1416
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• 2012

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• 한국컴퓨터그래픽스학회논문지
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• 제25권3호
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• pp.123-131
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• 2019

본 연구에서는 CT 영상의 대퇴골 부위를 해부학적으로 의미 있게 변형하여 CT 영상의 대퇴골 영역을 분할하기 위한 컨벌루션 신경망(CNN)의 훈련 데이터를 증강하는 방법을 제안한다. 먼저 CT 영상으로부터 삼차원 삼각형 대퇴골 메쉬를 얻는다. 그 후 메쉬의 국소부위에 대한 기하학적 특성을 계산하고, 군집화하여 메쉬를 의미 있는 부분들로 분할한다. 마지막으로, 분할한 부분들을 적절한 알고리즘으로 변형한 뒤, 이를 바탕으로 CT 영상을 와핑하여 새로운 CT영상을 생성하였다. 본 연구의 데이터 증강 방법을 이용하여 학습시킨 딥러닝 모델은 기하학적 변환이나 색상 변환 같이 일반적으로 사용되는 데이터 증강법과 비교하여 더 나은 영상분할 성능을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4336-4354
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• 2018

Fuzzy C-means (FCM) algorithm is a most usually technique for medical image segmentation. But conventional FCM fails to perform well enough on magnetic resonance imaging (MRI) data with the noise and intensity inhomogeneity (IIH). In the paper, we propose a Gamma correction conditional FCM algorithm with spatial information (GcsFCM) to solve this problem. Firstly, the pre-processing, Gamma correction, is introduced to enhance the details of images. Secondly, the spatial information is introduced to reduce the effect of noise. Then we introduce the effective neighborhood mechanism into the local space information to improve the robustness for the noise and inhomogeneity. And the mechanism describes the degree of participation in generating local membership values and building clusters. Finally, the adjustment mechanism and the spatial information are combined into the weighted membership function. Experimental results on four image volumes with noise and IIH indicate that the proposed GcsFCM algorithm is more effective and robust to noise and IIH than the FCM, sFCM and csFCM algorithms.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.554-556
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• 2011

각종 폐혈관 질환의 발생에 따른 정확하고 빠른 진단의 필요성이 강조되었다. 몇 가지 폐혈관 조영술의 제약사항의 존재로 흉부 CT에 대한 영상 처리의 필요성을 인지하였고 의료 영상처리의 다양성을 위해 ITK를 이용한 폐혈관 분할을 제안하였다. 본 논문은 명암 값을 기반한 방법으로 두 단계의 폐 영역 분할과 혈관 분할의 과정을 수행한다. 각 단계로 폐 영역 분할은 영상 향상, 문턱치 값, 관심영역 잘라내기로 결과 영상을 획득하고 폐 혈관 분할은 획득된 폐 영역에 영역 채우기를 적용하여 얻는다. 분할된 폐혈관 영상을 바탕으로 3차원 시각화 영상을 획득하여 폐혈관에 대한 다양한 관점의 분석 및 진단이 가능할 것으로 판단된다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.1036-1038
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• 2020

This paper proposes a novel automated liver segmentation using Multi-Dilated U-Nets. The proposed multidilation segmentation model has the advantage of considering both local and global shapes of the liver image. We use the CT images subject-wise, every 2D image is concatenated to 3D to calculate the IOU score and DICE score. The experimental results on Jeonbuk National University hospital dataset achieves better performance than the conventional U-Net.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2003년도 제27회 추계학술대회
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• pp.77-77
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• 2003

CT와 MRI의 단면 영상을 대상으로 영상분할 (Image segmentation)과 Image registration방법을 이용하여 인체 모델을 개발 하고자 한다. 우선 인체의 Head와 Neck부분의 CT와 MR 영상을 얻어 뼈, 근육, 인대, 그리고 그 밖의 장기의 해부학적 영상 특징을 분석하였다. 인체의 Head와 Neck 부분에 대한 CT와 MR 영상에 대해 각 부위별로 ROI(region-of-interrest)를 설정하였고, 각 volxel 마다 3차원 좌표를 계산할 수 있는 소프트웨어를 개발하였다. 특히 각 해부학적 영상에서 부위별로 CT 번호를 분석하고, pulse sequence에 따른 MRI 영상의 부위별 특정을 분석하였다. 이 분석한 자료를 바탕으로 영상 분할을 하였다. 영상 분할전에 각종 잡음(noise) 제거 및 영상 분할을 효과적으로 처리하기 위해 기본적인 영상처리 (filtering)를 구현하였고, 대조도(contrast) 및 밝기(brightness)를 조절할 수 있게 프로그램을 구현하였다. 영상 분할 방법 중 선(line) 및 에지(edge) 의 검출 방법, 문턱치화(threshold) 방법, 영역확대(region growing) 방법으로 영상 분할을 해봄으로써 우리의 인체 모델링 개발에 가장 적합한 영상 분할 알고리듬 방법을 찾도록 시도하였다. 결과적으로 말하면, 한가지 방법의 알고리듬을 쓰는 것보다는 인체의 부위에 따라 두 가지 이상의 알고리듬 방법을 쓰는 것이 원하고자 하는 부위를 영상 분할하는데 더 효과적이다는 것을 알게 되었다. 우리의 연구 과제에서는 영역확대(region growing) 방법과 문턱치화 방법, 모드법(피크니스, 밸리)의 알고리듬을 이용하여 영상 분할을 한 결과 우리가 얻고자 하는 인체 부위별 중 근육과 뼈를 구별하는데는 별 무리가 없었으나, 인대 및 기타 장기를 구별하는데는 어려움을 겪게 되었다. 이후에 좀더 알고리듬을 연구하여 이번 연구에서 구별하기 어려운 장기 부분도 구별 할 수 있도록 노력하겠다.

• 대한의용생체공학회:의공학회지
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• 제40권5호
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• pp.179-188
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• 2019

In ultrasound telemedicine, it is important to reduce the size of the data by compressing the ultrasound image when sending it. Ultrasound images can be divided into image context and other information consisting of patient ID, date, and several letters. Between them, ultrasound context is very important information for diagnosis and should be securely preserved as much as possible. In several previous papers, ultrasound compression methods were proposed to compress ultrasound context and other information into different compression parameters. This ultrasound compression method minimized the loss of ultrasound context while greatly compressing other information. This paper proposed the method of automatic segmentation of ultrasound context to overcome the limitation of the previously described ultrasound compression method. This algorithm was designed to robust for various ultrasound device and to enable real-time operation to maintain the benefits of ultrasound imaging machine. The operation time of extracting ultrasound context through the proposed segmentation method was measured, and it took 311.11 ms. In order to optimize the algorithm, the ultrasound context was segmented with down sampled input image. When the resolution of the input image was reduced by half, the computational time was 126.84 ms. When the resolution was reduced by one-third, it took 45.83 ms to segment the ultrasound context. As a result, we verified through experiments that the proposed method works in real time.