• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.157-164
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• 2003

Volume graphics have received a lot of attention as a medical image analysis tool nowadays. In the visualization based on volume graphics, there is a process called voxelization which transforms the geometrically defined objects into the volumetric objects. It enables us to volume render the geometrically defined data with sampling data. This paper suggests a voxeliration method using the cutting surfaces of cubes, implements the method on a PC, and evaluates it with simple geometric modeling data to explore propriety of the method. This method features the ability of calculating the exact normal vector from a voxel, having no hole among voxels, having multi-resolution representation.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.521-526
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• 2003

In this paper, we propose a geometric active contour model based on intensity information and curve evolution for detecting region boundaries. We put boundary extraction problem as the minimization of the difference between the average intensity of the region and the intensity of the expanding closed curves. We used level set theory to implement the curve evolution for optimal solution. It offered much more freedom in the initial curve position than a general active contour model. Our methods could detect regions whose boundaries are not necessarily defiened by gradient compared to general edge based methods and detect multiple boundaries at the same time. We could improve the result by using anisotropic diffusion filter in image preprocessing. The performance of our model was demonstrated on several data sets like CT and MRI medical images.

• Journal of Magnetics
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• v.16 no.3
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• pp.246-252
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• 2011

A high-voltage power supply has been built for activation of the brain via stimulation using a Full Wave Cockroft-Walton Circuit (FWCW). A resonant half-bridge inverter was applied (with half plus/half minus DC voltage) through a bidirectional power transistor to a magnetic stimulation device with the capability of producing a variety of pulse forms. The energy obtained from the previous stage runs the transformer and FW-CW, and the current pulse coming from the pulse-forming circuit is transmitted to a stimulation coil device. In addition, the residual energy in each circuit will again generate stimulation pulses through the transformer. In particular, the bidirectional device modifies the control mode of the stimulation coil to which the current that exceeds the rated current is applied, consequently controlling the output voltage as a constant current mode. Since a serial resonant half-bridge has less switching loss and is able to reduce parasitic capacitance, a device, which can simultaneously change the charging voltage of the energy-storage condenser and the pulse repetition rate, could be implemented. Image processing of the brain activity was implemented using a graphical user interface (GUI) through a data mining technique (data mining) after measuring the vital signs separated from the frequencies of EEG and ECG spectra obtained from the pulse stimulation using a 90S8535 chip (AMTEL Corporation).

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1383-1385
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• 1996

A network based interactive magnetic resonance imaging (MRI) system has been developed using the World Wide Web. For this purpose, an HTTP server is developed on the host computer of the MRI system. Capabilities of video and audio conferencing are included for monitoring experiment. Using the developed system. MRI imaging has been successfully carried out at the Signal Processing Lab in the Kwangwoon University with the remote MRI system located at the Medical Image Research Center at the KAIST in Daejon.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.104-106
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• 1993

Some cardio-vascular assist systems need more inexpensive artificial heart valves for short-term use. To meet with this need, we have developed a new polymer valve that is very simple to manufacture and of which its dimensions are easy to change, depending on its application. We have tested the hydrodynamic performance of the new polymer valve using a mock circulatory testing system and studied the flow through the valve using high-speed camera combined with image processing techniques. The results show that this valve is superior in its performances to the other valves (Bjork-Shiley mechanical valve and trileaflet polymer valve) and have no stagnation points. We also have tested the hemolytic potential of the valve. The valve is less hemolytic than the Bjork-shiley mechanical valve finally, we have applied this valve to a left ventricular assist device that we are developing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.684-686
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• 2017

Currently, image processing is used in various fields including military, medical and industrial fields. Noise added to images undermine the quality of images. As such, the removal of noise is an essential step to process images such as through recognition of images, detection of edge and segmentation of images. Studies on removing noise from images are actively being undertaken. One of the leading noises that are added to images is the AWGN(additive white Gaussian noise). This paper suggests an algorithm that synthesizes a filter that uses edge detection and standard deviation to ease AWGN.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.151-158
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• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.