• Journal of Biomedical Engineering Research
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• v.20 no.5
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• pp.545-557
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• 1999

본 논문에서는 호흡 연동 장치와 EBT로부터 획득한 폐실질 영상에 대하여 동적 윤곽선 모델 방법과 영역 성장법을 이용하여 폐실질 영역을 검출하였다. 그런 다음 , 검출된 폐실질 영역내에서의 각종 정량적 요소들을 도출하여 농도 분포 곡선에대한 분석을 하였다. 동적 윤곽선 모델방법에서 페실질 영역의 낮은 휘도 준위와 폐의 윤곽선 벡터 방향을 고려한 에너지 함수를 제안하였다. 그리고 폐실질 영역 성장법에서는 폐실질 영역내의 분포한 공기 성분에 대한 화소를 확장시켜 효과적으로 폐실질 영역을 검출하였다. 추출된 폐실질 영역내의 빈도 분포 곡선을 분석하여 정상군과 비교한 결과 만성 폐쇄성 폐질환자에서는 정상인에 비하여 평균 농도,최대 빈도 농도, 최대 상승 기울기 농도가 낮았으며, 농도 분포곡선은 더 낮은 쪽으로 이동하였음을 알 수 있었다. 또한, 특발성 폐섬유증 환자에서는 평균 농도, 최대 빈도 농도, 최대 상승 기울기 농도가 모두 증가되었고 농도 분포 곡선은 더 높은쪽으로 이동하였다. 폐실질 영역을 추출하여 히스토그램 분포에 대한 정량적 분석을 함으로써 정상인으로부터 만성 폐쇄성 질환자의 폐섬유증 환자를 구분할 수 있었다.

• Annual Conference of KIPS
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• 2000.10b
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• pp.895-898
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• 2000

의료 영상에서 폐 영역의 정확한 추출과 폐엽의 분할은 폐 기능의 측정 및 폐 질환의 진단을 위하여 매우 중요하다. 본 논문에서는 EBT 흉부 영상에서 자동으로 폐 영역을 추출하고 폐 영역을 폐엽 단위로 분할하는 방법을 제안한다. 본 논문에서는 히스토그램 분석과 형태학적 연산자를 이용하여 폐 영역을 추출하고 adaptive filter를 이용한 에지 연산과 폐엽 경계(pulmonary fissure)에 대한 의학적 지식을 바탕으로 폐엽을 분할하였다. 본 방법을 여러 종류의 EBT 폐 영상에 적용하여 실험한 결과 95%이상의 정확도를 보였다.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.276-292
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• 2004

In this paper. we present methods that extract lung regions from chest EBT(electron beam tomography) images then segment the extracted lung region into lung lobes. We use histogram based thresholding and mathematical morphology for extracting lung regions. For detecting pulmonary fissures, we use edge detector and knowledge-based search method. We suggest this edge detector, which uses adaptive filter scale, to work very well for real edge and insensitive for edge by noise. Our experiments showed about 95% accuracy or higher in extracting lung regions and about 5 pixel distance error in detecting pulmonary fissures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.554-556
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• 2011

The occurrence of various vascular diseases due to the need for accurate and rapid diagnosis was emphasized. Several limitations to the presence of pulmonary vascular angiography for chest CT imaging was aware of the need for diversity in medical image processing with Insight Toolkit(ITK) suggested pulmonary vascular division. In this paper, by contrast, based on the value of a two-step partitioning of the lungs and blood vessels to perform the process of splitting. Lung area segmentation of each stage image enhancement, threshold value, resulting in areas of interest cut image acquisition and acquired pulmonary vascular division in lung area obtained by applying the fill area. Partitioned on the basis of pulmonary vascular imaging to obtain three-dimensional visualization image of the pulmonary vascular analysis and diagnosis of a variety of perspectives are considered possible.

• Annual Conference of KIPS
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• 2004.05a
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• pp.699-702
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• 2004

단층촬영에 의해 획득된 흉부영상의 폐 영역은 기관지, 폐동맥, 폐정맥으로 구성된 복잡한 형태를 가지고 있다. 또한 이들 조직과 폐 영역 내에 존재하는 악성 종양과 같은 질병들 사이의 공간정보의 유사성으로 인해 방사선 전문의조차도 질병을 간단히 구분 해내는데 많은 어려움이 따른다. 따라서 본 논문에서는 이러한 유사한 공간정보를 갖는 폐 영역을 수리형태학 필터인 모폴로지(morphology)와 국부적인 워터쉐드(watershed) 알고리즘을 이용하여 분할하고, 분할된 폐 영역으로부터 색전 또는 종양 등의 결절(nodule)의 정보를 가지고 있는 혈관들을 추출하는 효과적인 알고리즘을 제안한다.

• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.641-650
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• 2005

In this parer, curve stopping function based on the CT number of lung parenchyma from CT lung images is proposed to detect lung region in replacement of conventional edge indication function in geodesic active contour model. We showed that the proposed method was able to detect lung region more effectively than conventional method by applying three kinds of measurement numerically. And, we verified the effectiveness of proposed method visually by observing the detection Procedure on actual CT images. Because lung parenchyma region could be precisely detected from actual EBCT (electron beam computer tomography) lung images, we were sure that the Proposed method could aid to early diagnosis of lung disease and local abnormality of function.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.763-765
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• 2004

현재 의료 영상을 이용한 신속하고 정확한 진단과 치료를 위하여 각 기관별로 영상을 분할하는 방식이 기본적으로 사용되고 있다. 본 논문에서는 워터쉐드(Watershed) 알고리즘을 이용하여 해부학적 기관 중 폐 영역을 분할하는 방식을 제안한다. 초기에 소벨 에지 마스크(Sobel Edge Mask)를 이용하여 윤곽선을 강조하여 워터쉐드 알고리즘을 적용하였을 경우 과다 분할되는 문제점이 발생한다. 이를 해결하기 위하여 제거(Opening) 연산과 채움(Closing) 연산을 이용하여 마커(Marker) 정보를 추출하여 워터쉐드 알고리즘을 재적용하여 폐 영역 이미지를 분할하였다. 본 논문에서 제안한 마커 정보를 이용한 워터쉐드 재적용 방식은 폐 영역 효율적이고 정확하게 추출한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.336-344
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• 2005

In this paper, we extracted the contour of lung parenchyma on EBT images with the improved active contour model. The objects boundary in conventional active contour model can be extracted by controlling internal energy and external energy as energy minimizing form. However, there are a number of problems such as initialization and the poor convergence about concave part. Expecially, contour can not enter the concave region by discouraging characteristic about stretching and bending in internal energy. We controlled internal energy by moving local perpendicular bisector point of each control point in the contour and implemented the object boundary by minimizing energy with external energy The convergence of concave part could be efficiently implemented toward lung parenchyma region by this internal energy and both lung images for initial contour could also be detected by multi-detection method. We were sure this method could be applied detection of lung parenchyma region in medical image.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.