• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.79-81
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• 2018

혈관내 초음파(Intravascular Ultrasound, IVUS)는 혈관 내벽의 단면을 보여주는 검사 방법으로 관상 동맥 내의 내강, 죽상 경화반, 그리고 혈관벽의 변화에 관한 직접적이고 구체적인 정보를 제공한다. 본 논문에서는 IVUS 영상에서 내막과 외막을 추출하고 각 막의 지름을 자동적으로 추출하는 방법을 제안한다. 제안된 방법은 IVUS 영상에 Histogram Equalization 기법을 적용하여 명암 대비를 강조한 후에 퍼지 이진화 기법과 평균 이진화 기법을 각각 적용하여 내막과 외막을 추출하기 위해 이진화한다. 이진화된 내막과 외막의 각 영역 중에서 혈관내 초음파 영상 중심에서 가장 큰 영역의 정보를 이용하여 라벨링 기법을 적용하여 내막과 외막 영역을 추출하고 각 막의 지름을 계산한다. 제안된 방법을 IVUS 영상을 대상으로 실험한 결과, 내막과 외막의 지름이 비교적 정확히 추출되는 것을 실험을 통하여 확인하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1635-1636
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1475-1476
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• 2011

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1514-1519
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• 2019

IVUS is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. IVUS is regularly used to locate the atherosclerosis lesions in the coronary arteries. Auto-segmentation of the vessel structure is important to detect the disorder of coronary artery. In this paper, we propose a simple strategy to extract Intima/Adventitia area effectively using fuzzy binarization from intravascular images. The proposed method apply fuzzy binarization to find the adventitia but apply average binarization to locate the intima since they have different homogeneity of pixel intensity comparing with the environment. In this paper, we demonstrate an effective auto-segmentation method for detecting the interior/exterior of the vessel walls by differentiating the fuzzy binarization result and average binarization result from IVUS image. Important statistics such as Intima-Media Thickness (IMT) or volume of a target area can be easily computed from result.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.940-942
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• 2005

혈관내부의 초음파는 혈관 벽(vessel wail) 전체를 관찰할 수 있는 단면적 영상(cross-sectional image)으로부터 혈관 벽의 서로 다른 층을 평가할 수 있다. IVUS(Intravascular Ultrasound)영상은 잡음에 매우 민감하고 해상도가 낮기 때문에 혈관 벽의 서로 다른 층을 구분된다. IVUS영상이 내강, 혈관 벽, 외막을 둘러싸는 영역으로 구성되어있다고 가정하면 내부와 외부의 두 경계선으로 구분할 수 있다. 따라서 본 논문에서는 IVUS영상을 웨이블릿 변환하여 주파수 공간에서 관상동맥 벽의 두 경계선을 추출한다. 실험결과를 통하여 관상동맥 벽의 두 경계선이 잘 추출되는 것을 확인할 수 있다.

• Journal of Chest Surgery
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• v.41 no.6
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• pp.768-771
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• 2008

The use of IVUS provides information about the lumen and arterial wall, the location and extent of atherosclerotic plaque and the state of the arterial wall after PCi. The use of IVUS after insertion of an intracoronary stent has currently increased, while conflicting evidence exists concerning the long-term benefits of IVUS. We report here on a case of surgical removal of an entrapped and fractured IVUS catheter during insertion of intracoronary stents, and we include a review of the relevant literature.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.349-356
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• 2012

In the diagnosis of coronary artery atheromatous plaque, Cardiac computed tomography (Cardiac Computed Tomographic Angiography: CCTA) compared with IVUS(Intravascular Ultrasound: IVUS) investigate the diagnostic accuracy, Interested in CCTA atheromatous plaque in computed tomography values (Hounsfield Unit: HU) try to find out. From April 2006 to August 2008 among coronary artery disease(Coronary Artery Disease: CAD) patients with confirmed or suspicious of CAD by CCTA performed atherosclerotic plaques and found 200 patients who underwent IVUS were enrolled. 200 patients who underwent CCTA and IVUS results from the 476 plaque was found, IVUS results of the soft plaque(n; 84), fibrous plaque(n; 63), mixed plaque (n; 97), calcific plaque(n; 232). The results are classified according to the IVUS plaque in HU in the soft plaque : $53.8{\pm}10.5$, fibrous plaque : $108.1{\pm}20.0$, mixed plaque : $371.2{\pm}113.1$, and calcific plaque : $731.0{\pm}160.4$. CCTA had sensitivity of 97% and confidence interval of 95.0-98.3. This study that is the diagnosis of coronary atheromatous plaque for using CCTA, we confirm the high sensitivity and the confidence interval Based on IVUS results CCTA atheromatous plaque with HU in the analysis could be classified to characterize in the treatment of patients with CAD is expected to help.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.329-336
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• 2003

Intra-Vascular Ultra-Sound (IVUS) transducers were developed for the application to diagnose coronary diseases. The transducer consists of 32 piezoelectric elements with a front insulation layer and a polymeric acoustic backing layer on a hollow alumina tube. The optimal geometrical structure of the transducer was designed through theoretical analysis of radiation patterns of the transducer. Samples of the IVUS transducers of the diameter of 3㎜ were fabricated to illustrate the design scheme. For the piezoelectric elements, 2-2 mode piezocomposite materials were employed. Experimental performance of the transducers showed good agreement with the design results, which verified feasibility of the transducer for IVUS applications.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.29-31
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• 2020

제안된 방법은 외막의 경계선을 추출하기 위해 Max-Min 이진화를 적용하여 외막을 추출 한 후에 삼각 함수의 각도 값을 이용하여 외막의 경계점을 추출한다. 추출한 경계점들을 Bezier Curve 기법을 적용하여 외막의 경계점들을 연결하여 외막의 경계선을 추출한다. 그리고 내막 영역을 추출하기 위해 외막 영역을 ROI 영역으로 추출한다. 추출된 ROI 영역을 오목 파라볼라 기법을 적용하여 내막의 영역을 강조한다. 내막영역이 강조된 ROI 영역에 평균 이진화를 적용하여 내막의 영역을 추출한다. 추출된 영역에서 잡음을 제거하기 위해 내막 영역만 Labeling 기법을 적용한다. 제안된 방법을 IVUS 영상을 대상으로 실험한 결과, 내막과 외막간의 포함관계의 정도에 따라 환자의 수술 여부 결정에 대한 외막과 내막의 각 넓이 정보를 개관적으로 제공할 수 있는 가능성을 확인하였다.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.201-208
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• 2012

Accurately segmenting lumen border in intravascular ultrasound images (IVUS) is very important to study vascular wall architecture for diagnosis of the cardiovascular diseases. After each of IVUS image is transformed to a polar coordinated image, initial points are detected using wavelet transform. Then, lumen border is initialized as the set of important points using non parametric probability density function and smoothing function by removing outlier initial points occurred by noises and artifacts. Finally, polynomial curve fitting is applied to obtain real lumen border using filtered important points. The evaluation of proposed method was performed with related method and the proposed method produced accurate lumen contour detection when compared to another method in most types of IVUS images.