Dr. Kwang-Gi Kim Profile

Dr. Kwang-Gi Kim

Researcher at National Cancer Ctr

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Publications (4)

Proceedings Article | 18 March 2008 Paper

Accurate measurement of respiratory airway wall thickness in CT images using a signal restoration technique

Sang Joon Park, Tae Jung Kim, Kwang Gi Kim, Sang Ho Lee, Jin Mo Goo, Jong Hyo Kim

Proceedings Volume 6913, 691336 (2008) https://doi.org/10.1117/12.771038

KEYWORDS: Deconvolution, Computed tomography, Error analysis, Point spread functions, 3D metrology, Image segmentation, Radiology, Medicine, Image analysis, Chronic obstructive pulmonary disease

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Proceedings Article | 30 March 2007 Paper

A novel algorithm for polyp detection using Eigen decomposition of Hessian-matrix for CT colonography CAD: validation with physical phantom study

June-Goo Lee, Se Hyung Kim, Jong Hyo Kim, Namkug Kim, Jin Young Choi, KwangGi Kim, Jong-Mo Seo

Proceedings Volume 6514, 65142K (2007) https://doi.org/10.1117/12.709175

KEYWORDS: Detection and tracking algorithms, Computer aided diagnosis and therapy, Computer aided design, Computed tomography, Algorithm development, Virtual colonoscopy, Medicine, Object recognition, Machine vision, Computer vision technology

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Proceedings Article | 17 March 2007 Paper

Application of time sampling in brain CT perfusion imaging for dose reduction

S. H. Lee, J. H. Kim, K. G. Kim, S. J. Park, Jung Gi Im

Proceedings Volume 6510, 65102P (2007) https://doi.org/10.1117/12.713529

KEYWORDS: Tissues, Computed tomography, Brain, Neuroimaging, Deconvolution, Statistical analysis, Convolution, Signal processing, Hemodynamics, Imaging systems

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Proceedings Article | 12 April 2005 Paper

Segmentation of breast ultrasound lesion boundary using texture-based multiresolution method

Kwang Kim, Jong Hyo Kim, Byoung Gu Min, Kyongtae Bae

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.595936

KEYWORDS: Image segmentation, Breast, Ultrasonography, Image processing, Image resolution, Binary data, Mammography, Mathematical modeling, Image analysis, Solids

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Computer-aided characterization of a breast ultrasound lesion involves two steps: first, extracting features from the lesion whose boundary is pre-defined on the images, and then converting the features into mathematical models. Most methods assume that the boundaries of the lesions are pre-selected or outlined by sonographers or operators, because automated delineation of lesion boundary is not trivial and is a challenging task. The purpose of this study was to develop and evaluate an automated lesion boundary segmentation method that is based on texture-based, multi-resolution image analysis. One hundred ninety-seven breast ultrasound images containing solid breast lesions from 172 women (age 24-89 years, mean 38 years) were studied. Fifty-five of the 197 images were from 40 women with malignant lesions, and the remaining 142 were from 132 patients with benign lesions. Each breast lesion was identified by an operator who placed a rectangular region of interest (ROI) to widely encompass the lesion. The resolution of the image was compressed, at variable ratios depending on the ROI size, to reduce noise. Texture momentum was computed. A binary image was generated from the texture and pixel intensity parameters. Initial seed boundary was segmented from the binary image and then expanded to the original resolution using the boundary pixel intensity gradient information. The boundary of each breast lesion was delineated by a breast-imaging radiologist who was blinded to the computer-detected lesion boundary. The 'area match ratio' between the manually drawn boundaries and the automatically detected boundaries was computed. This ratio is equal to or less than unity (unity indicates that the areas match exactly). Overall, good agreement was seen between the multi-resolution segmentation method and the radiologist’s manual delineation. The mean area match ratio was 0.87 ±0.02. We have developed a multi-resolution, texture-based method to segment the boundary of breast lesions. This method will facilitate full automation for the characterization of breast ultrasound lesions.

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