• Journal of the Korea Convergence Society
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• v.3 no.4
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• pp.15-21
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• 2012

Image segmentation for object to extract data from ultrasound acquired is an essential preprocessing step for the effective diagnosis. Various image segmentation methods have been studied. In this study, interactive image segmentation method by graph cut algorithm is proposed to develop a variety of applications of vascular ultrasound imaging and diagnostics. General imaging and vascular ultrasound imaging segmentation by entering constrain condition such as foreground and background. In the future it will be able to develop new ultrasound diagnostics.

• 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.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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• pp.75-79
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• 2014

Purpose The region growing has a critical problem that it often extract vessels with unexpected objects such as a bone which has a similar intensity characteristics to the vessel. We propose the new method to extract arterial vascular anatomy around the stomach from the CTA volume without the post-processing. Materials and Methods Our method, which is also based on the region growing, requires the two seed points from the use. I automatically extracts perigastric arteries using the adaptive region growing method and it does not need any post-processing. Results The three region growing based methods are used to extract perigastric arteries - the conventional region growings with restrict and loose thresholds each and the proposed method. The 3D visualization from the result of our method shows our method extracted the all required arteries for gastric surgery. Conclusion By extracting perigastric arteries using the proposed method, over-segmentation problem that unexpected anatomical objects such as a rib or backbone are also segmented does not occurs anymore. The proposed method does not need to sensitively determine the thresholds of the similarity function. By visualizing the result, the preoperative simulation of arterial vascular anatomy around the stomach can be possible.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.592-601
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• 2019

For coronary procedures, X-ray angiogram images are useful for diagnosing and assisting procedures. It is challenging to accurately segment a coronary artery using only a single segmentation model in 2D X-ray images due to a complex structure of three-dimensional coronary artery, especially from phenomenon of vessels being broken in the middle or end of coronary artery. In order to solve these problems, the initial segmentation is performed using an existing single model, and the candidate regions for the sophisticate correction is estimated based on the initial segment, and the local patch-based correction is performed in the candidate regions. Through this research, not only the broken coronary arteries are re-connected, but also the distal part of coronary artery that is very thin is additionally correctly found. Further, the performance can be much improved by combining the proposed correction method with any existing coronary artery segmentation method. In this paper, the U-net, a fully convolutional network was chosen as a segmentation method and the proposed correction method was combined with U-net to demonstrate a significant improvement in performance through X-ray images from several patients.

• Journal of IKEEE
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• v.23 no.2
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• pp.557-570
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• 2019

In this study, a novel hierarchical approach is investigated to extract coronary vessel from X-ray angiogram. First, we propose to combine Decimation-free Directional Filter Bank (DDFB) and Homographic Filtering (HF) in order to enhance X-ray coronary angiographic image for segmentation purposes. Because the blood vessel ensures that blood flows in only one direction on vessel branch, the DDFB filter is suitable to be used to enhance the vessels at different orientations and radius. In the combination with HF filter, our method can simultaneously normalize the brightness across the image and increases contrast. Next, a coarse-to-fine strategy for iterative segmentation based on Otsu algorithm is applied to extract the main coronary vessels in different sizes. Furthermore, we also propose a new approach to segment very small vessels. Specifically, based on information of the main extracted vessels, we introduce a new method to extract junctions on the vascular tree and level of nodes on the tree. Then, the window based segmentation is applied to locate and extract the small vessels. Experimental results on our coronary X-ray angiography dataset demonstrate that the proposed approach can outperform standard method and attain the accuracy of 71.34%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.591-597
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• 2012

Medical device performance has been advanced while images are expected to be acquired with further higher quality and pertinent applicability as images have been increasing in importance in analyzing major organs. Recent high frequency of image processing by MATLAB in image analysis area accounts for the intent of this study to segment pulmonary vessels by means of MATLAB. This study is to consist of 3 phases including pulmonary region segmentation, pulmonary vessel segmentation and three dimensional connectivity assessment, in which vessel was segmented, using threshold level, from the pulmonary region segmented, vessel thickness was measured as two dimensional refining process and three dimensional connectivity was assessed as three dimensional refining process. It is expected that MATLAB-based image processing should contribute to diversity and reliability of medical image processing and that the study results may lay a foundation for chest CT images-related researches.

• Korean Circulation Journal
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• v.54 no.1
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• pp.30-39
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• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

• Health Policy and Management
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• v.12 no.1
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• pp.125-143
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• 2002

The objectives of this study were analysis of patient\`s characteristics and market segmentation in oriental medical care and western medical care. This study focused on medical utilization using Anderson's health utilization model. The source of data was 1998 National Health and Nutrition Survey which Korean Institute For Health and Social Affairs carried out. A stratified multistage probability sampling design was used in this survey. The analysis was conducted using the statistical software package SPSS version 10.0 and Answer Tree 2.1 which is one of data mining methodology. The results were as follows ; 1) 44.9% of respondents reported visiting oriental medical center within recent two weeks. 3.4% of them used oriental medical care. The group of age, kind of disease and medical expenditure are associated with the difference western and oriental medical utilization rate. 2) There were several factors related to utilization of oriental medical care according to decision tree. Especially, important factors that patient chose his medical center were kinds of disease, kinds of common medical use, and expenditure. 3) in the results of CART analysis, market of oriental medical care were classified by seven categories. The major groups who have a preference for oriental medicine were those musculo-skeletal, cerebra-vascular disease, or chronic headache patients, and they had a preference fur oriental medical care in common use. These results show that oriental and western medical market were divided into various areas by market segmentation.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.300-302
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• 2022

Palm vein identification has attracted attention due to its distinct characteristics and excellent recognition accuracy. However, many contactless palm vein identification systems suffer from the issue of having low-quality palm images, resulting in degradation of recognition accuracy. This paper proposes the use of U-Net architecture to correctly segment the vascular blood vessel from palm images. Attention gate mechanism and residual block are also utilized to effectively learn the crucial features of a specific segmentation task. The experiments were conducted on CASIA dataset. Hessian-based Jerman filtering method is applied to label the palm vein patterns from the original images, then the network is trained to segment the palm vein features from the background noise. The proposed method has obtained 96.24 IoU coefficient and 98.09 dice coefficient.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.101-106
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• 2004

In this paper, a blood vessel in an angiographic image, which plays an importance role in the diagnose diseases including in the eyes, brain and heart, is enhanced by using a directed diffusion technique. A fundamental component of the angiographic analysis is vessel segmentation that the proposed method provides a preprocessing of the image into a form suitable for human analysis, or more importantly, for machine analysis such the segmentation. Vessel enhancement is a challenging problem due to the complex nature of vascular trees and to imaging imperfections. Some parts of the inherent imperfections in angiography are the intensity inhomogeneity between the larger and smaller vessels, and another imperfection is the leakage of contrast agent into the background tissue that provides to low contrast between vessels and tissue. In the proposed scheme, the directed diffusion solves the problem by formulating a local geometric structure, which consists of direction and scale of the blood vessels. The diffusion process uses the local structure to enhance by a diffusivity tensor. The proposed algorithm can be applied to maintain sharpness and coherence-smooth the intra-regions into homogeneity better than traditional diffusion methods, which are Gaussian regulation and coherence enhancing diffusion.