Prof. Jong Hyo Kim Profile

Prof. Jong Hyo Kim

Professor at Seoul National Univ Hospital

SPIE Involvement:

Conference Program Committee | Author

Publications (41)

Proceedings Article | 2 April 2024 Poster + Paper

Age-dependent generalizability of lumbar spine detection and segmentation models: a comparative study in pediatric populations (Image Processing Poster Award)

Jemyoung Lee, Changmin Park, Minkyoung Cho, Young Hun Choi, Jong Hyo Kim

Proceedings Volume 12926, 1292626 (2024) https://doi.org/10.1117/12.3006168

KEYWORDS: Data modeling, Spine, Education and training, Performance modeling, Image segmentation, Diagnostics, Bone, Computed tomography, Radiology

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Proceedings Article | 3 April 2023 Poster + Paper

How to overcome the data limited segmentation in abdominal CT: multi-planar UNet coupled with augmented contrast-boosting

Sihwan Kim, Chulkyun Ahn, Jong Hyo Kim

Proceedings Volume 12464, 124642S (2023) https://doi.org/10.1117/12.2654171

KEYWORDS: Image segmentation, Computed tomography, Data modeling, Education and training, Artificial intelligence, Spleen, Evolutionary algorithms, Performance modeling, Image restoration, Contrast agents

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Proceedings Article | 1 May 2022 Paper

Improved lung cancer detection in ultra low dose CT with combined AI-based nodule detection and denoising techniques

Jemyoung Lee, Jae-Hyun Park, Minsu Kim, Changyoung Heo, Kyong Joon Lee, Hanyoung Kim, Jihang Kim, Jong Hyo Kim

Proceedings Volume 12177, 1217721 (2022) https://doi.org/10.1117/12.2625966

KEYWORDS: Denoising, Lung, Computed tomography, Lung cancer, Artificial intelligence, Manufacturing, Spatial resolution, Computer simulations, Radiology, Image segmentation

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Proceedings Article | 16 March 2020 Paper

Synthesis of ghost-free panoramic radiographs from dental CBCT images

Do Il Lee, Sihwan Kim, Chulkyun Ahn, Changyong Heo, Jong Hyo Kim

Proceedings Volume 11312, 113122U (2020) https://doi.org/10.1117/12.2549929

KEYWORDS: Panoramic photography, Radiography, Teeth, Spine, 3D image processing, Skull, Computed tomography, 3D acquisition, Image resolution, X-rays

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Proceedings Article | 27 March 2019 Paper

A 3D printing-based realistic anthropomorphic dental phantom and its imaging evaluation

Do Il Lee, Zepa Yang, Jong Hyo Kim

Proceedings Volume 11050, 110501K (2019) https://doi.org/10.1117/12.2521503

KEYWORDS: Skull, X-ray computed tomography, 3D image processing, Teeth, Bone, 3D printing, 3D modeling, Metals, Tissues, Computed tomography

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Proceedings Article | 27 March 2019 Paper

Combined low-dose simulation and deep learning for CT denoising: application in ultra-low-dose chest CT

Chulkyun Ahn, Changyong Heo, Jong Hyo Kim

Proceedings Volume 11050, 110500E (2019) https://doi.org/10.1117/12.2521539

KEYWORDS: Computed tomography, Chest, Denoising, Image quality, Reconstruction algorithms, Lung, Convolutional neural networks, Tissues, Visualization, Infrared imaging

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Proceedings Article | 27 March 2019 Paper

How dependent are CT radiomic features on CT scan parameters?

Hyeongmin Jin, Jong Hyo Kim

Proceedings Volume 11050, 110500D (2019) https://doi.org/10.1117/12.2521488

KEYWORDS: Computed tomography, Feature extraction, CT reconstruction, Image segmentation, Lung cancer, Image quality

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Proceedings Article | 15 March 2019 Paper

A 3D printing-based realistic anthropomorphic dental phantom and its imaging evaluation

Do Il Lee, Zepa Yang, Jong Hyo Kim

Proceedings Volume 10954, 1095415 (2019) https://doi.org/10.1117/12.2513155

KEYWORDS: Panoramic photography, Skull, Teeth, Metals, Bone, 3D printing, Tissues, 3D image processing, Computed tomography, 3D modeling

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Proceedings Article | 1 March 2019 Paper

Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA

Chul Kyun Ahn, Hyeongmin Jin, Changyong Heo, Jong Hyo Kim

Proceedings Volume 10948, 1094846 (2019) https://doi.org/10.1117/12.2513144

KEYWORDS: Denoising, Image quality, Computed tomography, Angiography, Convolutional neural networks, Aorta, Electrocardiography, Radiation oncology, Medicine, Radiology

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Proceedings Article | 9 March 2018 Paper

A deep learning-enabled iterative reconstruction of ultra-low-dose CT: use of synthetic sinogram-based noise simulation technique

Chul Kyun Ahn, Zepa Yang, Changyong Heo, Hyeongmin Jin, Byungjun Park, Jong Hyo Kim

Proceedings Volume 10573, 1057335 (2018) https://doi.org/10.1117/12.2294013

KEYWORDS: Computed tomography, Denoising, Image quality, Wavelets, CT reconstruction, Image denoising, Convolutional neural networks, Image analysis

Read Abstract +

Effective elimination of unique CT noise pattern while preserving adequate image quality is crucial in reducing radiation dose to ultra-low-dose level in CT imaging practice. In this study, we present a novel Deep Learning-enable Iterative Reconstruction (Deep IR) approach for CT denoising which incorporate a synthetic sinogram-based noise simulation technique for training of Convolutional Neural Network (CNN). Regular dose CT images from 25 patients were used from Seoul National University Hospital. The CT scans were performed at 140 kVp, 100 mAs, and reconstructed with standard FBP technique using B60f kernel. Among them, 20 patients were randomly selected as a training set and the rest 5 patients were used for a test set. We applied a re-projection technique to create a synthetic sinogram from the DICOM CT image, and then a simulated noise sinogram was generated to match the noise level of 10mAs according to Poisson statistic and the system noise model of the given scanner (Somatom Sensation 16, Siemens). We added the simulated noise sinogram to the re-projected synthetic sinogram to generate a simulated sinogram of ultra-low dose scan. We also created the simulated ultra-low-dose CT image by applying FBP reconstruction of the simulated noise sinogram with B60f kernel. A CNN model was created using a TensorFlow framework to have 10 consecutive convolution layer and activation layer. The CNN was trained to learn the noise in sinogram domain: the simulated noisy sinogram of ultra-low dose scan was fed into its input nodes with the output node being fed by the simulated noise sinogram. At test phase, the noise sinogram from the CNN output was reconstructed with using B60f kernel to create a noise CT image, which in turn was subtracted from the simulated ultra-low-dose CT image to produce a Deep IR CT image. The performance was evaluated quantitatively in terms of structural similarity (SSIM) index, peak signal-to-noise ratio (PSNR) and noise level measurement and qualitatively in CT image by comparing the noise pattern and image quality. Compared to low-dose image, denoising image of the SSIM and the PSNR were improved from 0.75 to 0.80, 28.61db to 32.16 respectively. The noise level of denoising image was reduced to an average of 56 % of that of low-dose image. The noise pattern in reconstructed noise CT was indistinguishable from that of real CT images, and the image quality of Deep IR CT image was overall much higher than that of simulated ultra-low-dose CT.

Proceedings Article | 27 February 2018 Paper

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Hyeongmin Jin, Changyong Heo, Jong Hyo Kim

Proceedings Volume 10575, 105753L (2018) https://doi.org/10.1117/12.2295010

KEYWORDS: Medical image processing, Machine learning, Computed tomography, Emphysema, Lung, CT reconstruction, Data conversion, Image segmentation, Modulation transfer functions, Databases, Chronic obstructive pulmonary disease

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Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 ± 7.28% for B50f data set, 10.82 ± 6.71% for the B30f data set, and 8.87 ± 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

Proceedings Article | 3 March 2017 Paper

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

Chul Kyun Ahn, Changyong Heo, Heongmin Jin, Jong Hyo Kim

Proceedings Volume 10134, 101342O (2017) https://doi.org/10.1117/12.2254264

KEYWORDS: Convolutional neural networks, Breast, Mammography, Neural networks, Digital mammography, Feature extraction, X-ray imaging, X-rays, Radiology, Image segmentation

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Proceedings Article | 21 March 2014 Paper

Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

Myungeun Lee, Jong Hyo Kim, Moon Ho Park, Ye-Hoon Kim, Yeong Kyeong Seong, Baek Hwan Cho, Kyoung-Gu Woo

Proceedings Volume 9034, 90341N (2014) https://doi.org/10.1117/12.2043427

KEYWORDS: Tumors, Image segmentation, 3D modeling, Ultrasonography, Liver, Tumor growth modeling, 3D image processing, Solid modeling, Image classification, Feature extraction

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Proceedings Article | 18 March 2014 Paper

Comparison of biophysical factors influencing on emphysema quantification with low-dose CT

Chang Yong Heo, Jong Hyo Kim

Proceedings Volume 9035, 90352E (2014) https://doi.org/10.1117/12.2043894

KEYWORDS: Emphysema, Lung, Computed tomography, Image segmentation, Lung cancer, Signal attenuation, Biological research, Factor analysis, Computer aided diagnosis and therapy, Collimation

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Proceedings Article | 6 March 2013 Paper

Evaluation of nonlinear pre-sampled modulation transfer function in iterative reconstruction CT

Hyeong Min Jin, Jong Hyo Kim

Proceedings Volume 8668, 86684P (2013) https://doi.org/10.1117/12.2007823

KEYWORDS: Modulation transfer functions, Computed tomography, Infrared imaging, Image quality, Reconstruction algorithms, Spatial frequencies, Iodine, CT reconstruction, Imaging systems, Quantitative analysis

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Proceedings Article | 22 March 2012 Paper

An image-based approach to low-dose CT simulation

Chang Won Kim, Jong Hyo Kim

Proceedings Volume 8313, 83134D (2012) https://doi.org/10.1117/12.912393

KEYWORDS: X-ray computed tomography, Computed tomography, Modulation transfer functions, Signal attenuation, Image filtering, Apodization, Optical filters, Sensors, Chest, X-rays

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Proceedings Article | 14 February 2012 Paper

Segmentation of the pectoral muscle in breast MR images using structure tensor and deformable model

Myungeun Lee, Jong Hyo Kim

Proceedings Volume 8314, 83142P (2012) https://doi.org/10.1117/12.911181

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Gaussian filters, Image filtering, Tissues, Skin, Medicine, Medical imaging, Breast cancer

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Proceedings Article | 17 March 2011 Paper

Contrast adaptive total p-norm variation minimization approach to CT reconstruction for artifact reduction in reduced-view brain perfusion CT

Chang-Won Kim, Jong-Hyo Kim

Proceedings Volume 7961, 79612W (2011) https://doi.org/10.1117/12.877614

KEYWORDS: Brain, X-ray computed tomography, Reconstruction algorithms, CT reconstruction, X-rays, Image segmentation, Tissues, Head, Neuroimaging, Image filtering

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Perfusion CT (PCT) examinations are getting more frequently used for diagnosis of acute brain diseases such as hemorrhage and infarction, because the functional map images it produces such as regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), and mean transit time (MTT) may provide critical information in the emergency work-up of patient care. However, a typical PCT scans the same slices several tens of times after injection of contrast agent, which leads to much increased radiation dose and is inevitability of growing concern for radiation-induced cancer risk. Reducing the number of views in projection in combination of TV minimization reconstruction technique is being regarded as an option for radiation reduction. However, reconstruction artifacts due to insufficient number of X-ray projections become problematic especially when high contrast enhancement signals are present or patient's motion occurred. In this study, we present a novel reconstruction technique using contrast-adaptive TpV minimization that can reduce reconstruction artifacts effectively by using different p-norms in high contrast and low contrast objects. In the proposed method, high contrast components are first reconstructed using thresholded projection data and low p-norm total variation to reflect sparseness in both projection and reconstruction spaces. Next, projection data are modified to contain only low contrast objects by creating projection data of reconstructed high contrast components and subtracting them from original projection data. Then, the low contrast projection data are reconstructed by using relatively high p-norm TV minimization technique, and are combined with the reconstructed high contrast component images to produce final reconstructed images. The proposed algorithm was applied to numerical phantom and a clinical data set of brain PCT exam, and the resultant images were compared with those using filtered back projection (FBP) and conventional TV reconstruction algorithm. Our results show the potential of the proposed algorithm for image quality improvement, which in turn may lead to dose reduction.

Proceedings Article | 9 March 2010 Paper

Automated volumetric segmentation method for computerizeddiagnosis of pure nodular ground-glass opacity in high-resolution CT

Wooram Son, Sang Joon Park, Chang Min Park, Jin Mo Goo, Jong Hyo Kim

Proceedings Volume 7624, 76241P (2010) https://doi.org/10.1117/12.844108

KEYWORDS: Image segmentation, Opacity, Lung, Computed tomography, Tissues, Computer aided diagnosis and therapy, Imaging systems, Solid modeling, Databases, Reconstruction algorithms

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Proceedings Article | 9 March 2010 Paper

Automated estimation of breast density on mammogram using combined information of histogram statistics and boundary gradients

Youngwoo Kim, Changwon Kim, Jong-Hyo Kim

Proceedings Volume 7624, 76242F (2010) https://doi.org/10.1117/12.844083

KEYWORDS: Breast, Mammography, Tissues, Breast cancer, Statistical analysis, Image segmentation, Digital filtering, Feature extraction, Medicine, Digital mammography

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Proceedings Article | 28 February 2009 Paper

Automatic patient-adaptive bleeding detection in a capsule endoscopy

Yun Sub Jung, Yong Ho Kim, Dong Ha Lee, Sang Ho Lee, Jeong Joo Song, Jong Hyo Kim

Proceedings Volume 7260, 72603T (2009) https://doi.org/10.1117/12.813793

KEYWORDS: Endoscopy, Tissues, Image resolution, Image filtering, Receivers, Computer aided diagnosis and therapy, Computing systems, Video, Holmium, Feature extraction

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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 | 17 March 2008 Paper

Tumor classification using perfusion volume fractions in breast DCE-MRI

Sang Ho Lee, Jong Hyo Kim, Jeong Seon Park, Sang Joon Park, Yun Sub Jung, Jung Joo Song, Woo Kyung Moon

Proceedings Volume 6915, 69152D (2008) https://doi.org/10.1117/12.774370

KEYWORDS: Surface plasmons, Tumors, Image segmentation, Breast, Image classification, Computer aided diagnosis and therapy, Magnetic resonance imaging, 3D acquisition, 3D image enhancement, Statistical analysis

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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 | 29 April 2005 Paper

Automatic registration of ICG images using mutual information and perfusion analysis

Namkug Kim, Jong-Mo Seo, June-goo Lee, Jong Hyo Kim, Kwangsuk Park, Hyeong-Gon Yu, Young Suk Yu, Hum Chung

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595107

KEYWORDS: Image registration, Angiography, Quantitative analysis, Blood circulation, Eye, Data modeling, Tissues, Image acquisition, Blood, Medicine

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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.

Proceedings Article | 16 May 2002 Paper

Technical aspects of PACS in Korea

JongHyo Kim

Proceedings Volume 4685, (2002) https://doi.org/10.1117/12.467041

KEYWORDS: Picture Archiving and Communication System, Radiology, Software development, Standards development, Imaging systems, Switches, Data storage, Biomedical engineering, Medical imaging, Internet

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Proceedings Article | 16 May 2002 Paper

DICOM structured report: implementation notes for basic structured reporting system

DongOok Kim, DongHyuk Lee, JinHyung Lee, HeeJung Park, HyunWoo Lim, JinYoung Ahn, JongHyo Kim

Proceedings Volume 4685, (2002) https://doi.org/10.1117/12.467034

KEYWORDS: Human-machine interfaces, Internet, Microsoft Foundation Class Library, Radiology, Digital imaging, Strontium, Databases, Medical imaging, Picture Archiving and Communication System, C++

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Proceedings Article | 16 May 2002 Paper

Full PACS installation in Seoul National University Hospital, Korea

HyunWoo Lim, DongOok Kim, JinYoung Ahn, DongHyuk Lee, JinHyung Lee, HeeJung Park, JongHyo Kim, Jungu Han

Proceedings Volume 4685, (2002) https://doi.org/10.1117/12.466991

KEYWORDS: Picture Archiving and Communication System, Endoscopes, Image compression, Magnetic resonance imaging, Buildings, System integration, Databases, Chromium, Rutherfordium, Surgery

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Proceedings Article | 30 May 2000 Paper

Lossless compression of 3D medical images using reversible integer wavelet transforms

HyoJoon Kim, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 4067, (2000) https://doi.org/10.1117/12.386584

KEYWORDS: 3D image processing, Wavelets, Image compression, Wavelet transforms, Medical imaging, 3D acquisition, Computed tomography, Image quality, Laser induced breakdown spectroscopy, Image processing

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Proceedings Article | 18 July 1999 Paper

Virtual integration of multiple-facility PAC systems with ISDN intranet and Web-based DICOM white board

JongHyo Kim, Heung Sik Kang, Duk-Woo Ro, Myung Shin, Dongsung Kim, Young Kim, Seok Kang

Proceedings Volume 3662, (1999) https://doi.org/10.1117/12.352773

KEYWORDS: Picture Archiving and Communication System, Radiology, Diagnostics, System integration, Software development, Clinical trials, Electronics, Windows NT, Sun, Internet

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Proceedings Article | 3 July 1998 Paper

Dynamic three-dimensional phase-contrast technique in MRI: application to complex flow analysis around the artificial heart valve

Soo Jeong Kim, Dong Hyuk Lee, Inchang Song, Nam Gook Kim, Jae-Hyeung Park, JongHyo Kim, Man Chung Han, Byong Goo Min

Proceedings Volume 3337, (1998) https://doi.org/10.1117/12.312580

KEYWORDS: Magnetic resonance imaging, Heart, Velocity measurements, Data acquisition, 3D image processing, Magnetism, Phase shifts, Computer programming, Visualization, Phase contrast

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Phase-contrast (PC) method of magnetic resonance imaging (MRI) has bee used for quantitative measurements of flow velocity and volume flow rate. It is a noninvasive technique which provides an accurate two-dimensional velocity image. Moreover, Phase Contrast Cine magnetic resonance imaging combines the flow dependent contrast of PC-MRI with the ability of cardiac cine imaging to produce images throughout the cardiac cycle. However, the accuracy of the data acquired from the single through-plane velocity encoding can be reduced by the effect of flow direction, because in many practical cases flow directions are not uniform throughout the whole region of interest. In this study, we present dynamic three-dimensional velocity vector mapping method using PC-MRI which can visualize the complex flow pattern through 3D volume rendered images displayed dynamically. The direction of velocity mapping can be selected along any three orthogonal axes. By vector summation, the three maps can be combined to form a velocity vector map that determines the velocity regardless of the flow direction. At the same time, Cine method is used to observe the dynamic change of flow. We performed a phantom study to evaluate the accuracy of the suggested PC-MRI in continuous and pulsatile flow measurement. Pulsatile flow wave form is generated by the ventricular assistant device (VAD), HEMO-PULSA (Biomedlab, Seoul, Korea). We varied flow velocity, pulsatile flow wave form, and pulsing rate. The PC-MRI-derived velocities were compared with Doppler-derived results. The velocities of the two measurements showed a significant linear correlation. Dynamic three-dimensional velocity vector mapping was carried out for two cases. First, we applied to the flow analysis around the artificial heart valve in a flat phantom. We could observe the flow pattern around the valve through the 3-dimensional cine image. Next, it is applied to the complex flow inside the polymer sac that is used as ventricle in totally implantable artificial heart (TAH). As a result we could observe the flow pattern around the valves of the sac, though complex flow can not be detected correctly in the conventional phase contrast method. In addition, we could calculate the cardiac output from TAH sac by quantitative measurement of the volume of flow across the outlet valve.

Proceedings Article | 26 June 1998 Paper

Region-adaptive JPEG encoding scheme for medical images

HyoJoon Kim, Jun Song, Seung Kim, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312537

KEYWORDS: Distortion, Image segmentation, Computer programming, Medical imaging, Image compression, Mammography, Tin, Diagnostics, Image quality, Astatine

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Proceedings Article | 26 June 1998 Paper

Block-based wavelet transform coding of mammograms with region-adaptive quantization

Nam Su Moon, Jun Song, Musik Kwon, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312533

KEYWORDS: Quantization, Image segmentation, Wavelet transforms, Image compression, Mammography, Breast, Wavelets, Medical imaging, Breast cancer, Image processing

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Proceedings Article | 22 May 1997 Paper

HIS/RIS/PACS integration toward total hospital information system

JongHyo Kim, Man Chung Han, Kyoung Yeon, Dong Hyuk Lee, Han Cho

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274583

KEYWORDS: Picture Archiving and Communication System, Telecommunications, System integration, Databases, Local area networks, Information fusion, Asynchronous transfer mode, Computing systems, Radiology, Data communications

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Proceedings Article | 22 May 1997 Paper

Development of hospital-integrated large-scale PACS in Seoul National University Hospital

JongHyo Kim, Kyoung Yeon, Man Chung Han, Dong Hyuk Lee, Han Cho

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274578

KEYWORDS: Picture Archiving and Communication System, Image storage, Windows NT, Radiology, Surgery, Image resolution, Diagnostics, Optical resolution, Computer architecture, Telecommunications

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Proceedings Article | 7 May 1997 Paper

Wavelet compression of medical image using tree-structured vector quantization and high-order entropy coding

Jun Song, Seung Jun Lee, HyoJoon Kim, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273954

KEYWORDS: Computer programming, Wavelets, Quantization, Image compression, Medical imaging, Computed tomography, Abdomen, Neon, Image quality, Quantum efficiency

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Proceedings Article | 7 May 1997 Paper

Lossless progressive compression of medical images

HyoJoon Kim, Jun Song, Seung Jun Lee, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273959

KEYWORDS: Image compression, Quantization, Digital filtering, Computer programming, Medical imaging, Linear filtering, Switching, Magnetic resonance imaging, Computed tomography, Error analysis

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Proceedings Article | 7 May 1997 Paper

Enhanced visualization of MR angiogram with modified MIP and 3D image fusion

JongHyo Kim, Kyoung Yeon, Man Chung Han, Dong Hyuk Lee, Han Cho

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273918

KEYWORDS: Image fusion, 3D image processing, Brain, Visualization, Image visualization, Image segmentation, Magnetic resonance imaging, Neuroimaging, 3D image enhancement, Volume rendering

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Proceedings Article | 25 April 1997 Paper

Neural net classification of liver ultrasonogram for quantitative evaluation of diffuse liver disease

Dong Hyuk Lee, JongHyo Kim, Hee Kim, Yong Lee, Byong Goo Min

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274078

KEYWORDS: Liver, Algorithm development, Neural networks, Radiology, Spleen, Evolutionary algorithms, Information operations, Medicine, Image classification, Clinical research

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Showing 5 of 41 publications

Conference Committee Involvement (17)

Computer-Aided Diagnosis

16 February 2025 | San Diego, California, United States

Computer-Aided Diagnosis

19 February 2024 | San Diego, California, United States

Computer-Aided Diagnosis

20 February 2023 | San Diego, California, United States

Computer-Aided Diagnosis

21 February 2022 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2021 | Online Only, California, United States

Computer-Aided Diagnosis

16 February 2020 | Houston, Texas, United States

Computer-Aided Diagnosis

17 February 2019 | San Diego, California, United States

International Forum on Medical Imaging in Asia

7 January 2019 | Singapore, Singapore

Computer-Aided Diagnosis

12 February 2018 | Houston, Texas, United States

Computer-Aided Diagnosis

13 February 2017 | Orlando, Florida, United States

Computer-Aided Diagnosis Posters

13 February 2017 | Orlando, FL, United States

Computer-Aided Diagnosis

28 February 2016 | San Diego, California, United States

Computer-Aided Diagnosis

22 February 2015 | Orlando, Florida, United States

Computer-Aided Diagnosis

18 February 2014 | San Diego, California, United States

Computer-Aided Diagnosis

12 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

Computer-Aided Diagnosis

7 February 2012 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2011 | Lake Buena Vista (Orlando), Florida, United States

Showing 5 of 17 Conference Committees

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