Prof. Namkug Kim Profile

Prof. Namkug Kim

at Asan Medical Ctr

SPIE Involvement:

Author

Publications (13)

PROCEEDINGS ARTICLE | March 9, 2018

Breast imaging using micro-resolution field emission x-ray system with carbon nanotube emitter

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: Breast, X-ray optics, Tissues, Surgery, Imaging systems, X-rays, Mammography, X-ray imaging, Carbon nanotubes, Breast imaging

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PROCEEDINGS ARTICLE | March 17, 2015

Size-based emphysema cluster analysis on low attenuation area in 3D volumetric CT: comparison with pulmonary functional test

Proc. SPIE. 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Chest, Signal attenuation, Image segmentation, Scanners, Lung, Computed tomography, Analytical research, Pulmonary function tests, Emphysema, Chronic obstructive pulmonary disease

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PROCEEDINGS ARTICLE | March 27, 2009

Novel level-set based segmentation method of the lung at HRCT images of diffuse interstitial lung disease (DILD)

Proc. SPIE. 7259, Medical Imaging 2009: Image Processing

KEYWORDS: Image segmentation, 3D modeling, Lung, Shape analysis, Machine learning, Colon, Radiology, Algorithm development, Radio propagation, 3D image processing

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PROCEEDINGS ARTICLE | February 28, 2009

A computer-aided differential diagnosis between UIP and NSIP using automated assessment of the extent and distribution of regional disease patterns at HRCT: comparison with the radiologist's decision

Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis

KEYWORDS: Atrial fibrillation, Opacity, Diagnostics, Lung, Artificial intelligence, Image classification, Feature selection, Computer aided design, Radiology, Emphysema

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To evaluate the accuracy of computer aided differential diagnosis (CADD) between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) at HRCT in comparison with that of a radiologist's decision. A computerized classification for six local disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EM; and consolidation, CON) using texture/shape analyses and a SVM classifier at HRCT was used for pixel-by-pixel labeling on the whole lung area. The mode filter was applied on the results to reduce noise. Area fraction (AF) of each pattern, directional probabilistic density function (pdf) (dPDF: mean, SD, skewness of pdf /3 directions: superior-inferior, anterior-posterior, central-peripheral), regional cluster distribution pattern (RCDP: number, mean, SD of clusters, mean, SD of centroid of clusters) were automatically evaluated. Spatially normalized left and right lungs were evaluated separately. Disease division index (DDI) on every combination of AFs and asymmetric index (AI) between left and right lung ((left-right)/left) were also evaluated. To assess the accuracy of the system, fifty-four HRCT data sets in patients with pathologically diagnosed UIP (n=26) and NSIP (n=28) were used. For a classification procedure, a CADD-SVM classifier with internal parameter optimization, and sequential forward floating feature selection (SFFS) were employed. The accuracy was assessed by a 5-folding cross validation with 20- times repetition. For comparison, two thoracic radiologists reviewed the whole HRCT images without clinical information and diagnose each case either as UIP or NSIP. The accuracies of radiologists' decision were 0.75 and 0.87, respectively. The accuracies of the CADD system using the features of AF, dPDF, AI of dPDF, RDP, AI of RDP, DDI were 0.70, 0.79, 0.77, 0.80, 0.78, 0.81, respectively. The accuracy of optimized CADD using all features after SFFS was 0.91. We developed the CADD system to differentiate between UIP and NSIP using automated assessment of the extent and distribution of regional disease patterns at HRCT.

PROCEEDINGS ARTICLE | February 28, 2009

Improvement of computational efficiency using a cascade classification scheme for the classification of diffuse infiltrative lung disease on HRCT

Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis

KEYWORDS: Medicine, Opacity, Feature extraction, Lung, Image classification, Feature selection, Radiology, Binary data, Emphysema, Classification systems

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PROCEEDINGS ARTICLE | March 19, 2008

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

Proc. SPIE. 6914, Medical Imaging 2008: Image Processing

KEYWORDS: Statistical analysis, Opacity, Feature extraction, Lung, Medical imaging, Shape analysis, Machine learning, Radiology, Emphysema, Classification systems

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To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

PROCEEDINGS ARTICLE | March 30, 2007

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

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

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

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PROCEEDINGS ARTICLE | March 26, 2007

Analysis of point-to-point lung motion with full inspiration and expiration CT data using non-linear optimization method: optimal geometric assumption model for the effective registration algorithm