Mr. Choong-Il Ahn Profile

Choong-Il Ahn

at INFINITT Healthcare Co Ltd

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Author

Publications (3)

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Automatic polyp detection using SmartEndo-Net based on fusion feature pyramid network with mix-up edges

Young Jae Kim, Sohyun Byun, Chung il Ahn, Sangwook Cho, Kwang Gi Kim

Proceedings Volume 12033, 120332S (2022) https://doi.org/10.1117/12.2612651

KEYWORDS: Colorectal cancer, Video, Cancer, Edge detection, Sensors, Medicine, Image resolution, Image fusion, Image filtering, Convolutional neural networks

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Proceedings Article | 15 March 2006 Paper

Automated segmentation of three-dimensional MR brain images

Jonggeun Park, Byungjun Baek, Choong-Il Ahn, Kyo Bum Ku, Dong Kyun Jeong, Chulhee Lee

Proceedings Volume 6144, 61444R (2006) https://doi.org/10.1117/12.655532

KEYWORDS: Brain, Image segmentation, Neuroimaging, Magnetic resonance imaging, 3D image processing, Surface plasmons, Spinal cord, Surgery, Tissues, Image processing

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Proceedings Article | 14 April 2005 Paper

The cerebral imaging using vessel-around method in the perfusion CT of the human brain

Choong-Il Ahn, Seung-Wook Choi, Seung-Chul Park, Yeong-Gil Shin, Jae-Hyoung Kim, Gi-Bong Chong

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.594460

KEYWORDS: Computed tomography, Brain, Neuroimaging, Tissues, Ischemia, Image processing, Brain mapping, Ischemic stroke, Image enhancement, Functional imaging

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Perfusion CT has been successfully used as a functional imaging technique for diagnosis of patients with hyperacute stroke. However, the commonly used methods based on curve-fitting are time consuming. Numerous researchers have investigated to what extent Perfusion CT can be used for the quantitative assessment of cerebral ischemia and to rapidly obtain comprehensive information regarding the extent of ischemic damage in acute stroke patients. The aim of this study is to propose an alternative approach to rapidly obtain the brain perfusion mapping and to show the proposed cerebral flow imaging of the vessel and tissue in human brain be reliable and useful. Our main design concern was algorithmic speed, robustness and automation in order to allow its potential use in the emergency situation of acute stroke. To obtain a more effective mapping, we analyzed the signal characteristics of Perfusion CT and defined the vessel-around model which includes the vessel and tissue. We proposed a nonparametric vessel-around approach which automatically discriminates the vessel and tissue around vessel from non-interested brain matter stratifying the level of maximum enhancement of pixel-based TAC. The stratification of pixel-based TAC was executed using the mean and standard deviation of the signal intensity of each pixel and mapped to the cerebral flow imaging. The defined vessel-around model was used to show the cerebral flow imaging and to specify the area of markedly reduced perfusion with loss of function of still viable neurons. Perfusion CT is a fast and practical technique for routine clinical application. It provides substantial and important additional information for the selection of the optimal treatment strategy for patients with hyperacute stroke. The vessel-around approach reduces the computation time significantly when compared with the perfusion imaging using the GVF. The proposed cerebral imaging shows reliable results which are validated by physicians and medical staff. Moreover the vessel-around approach was found to be comprehensive and easy-to-interpret by physicians and medical staff, hence we conclude that our proposed vessel-around technique can be used for brain perfusion mapping.

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