• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.101-107
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• 2016

Due to the recent advancement in digital imaging technology, development of intervention equipment has become generalize. Video arbitration procedure is a process to insert a tiny catheter and a guide wire in the body, so in order to enhance the effectiveness and safety of this treatment, the high-quality of x-ray of image should be used. However, the increasing of radiation has become the problem. Therefore, the studies to improve the performance of x-ray detectors are being actively processed. Moreover, this intervention is based on the reference of the angiographic imaging and 3D medical image processing. In this paper, we propose a guidance system to support this intervention. Through this intervention, it can solve the problem of the existing 2D medical images based vessel that has a formation of cerebrovascular disease, and guide the real-time tracking and optimal route to the target lesion by intervention catheter and guide wire tool. As a result, the system was completely composed for medical image acquisition unit and image processing unit as well as a display device. The experimental environment, guide services which are provided by the proposed system Brain Phantom (complete intracranial model with aneurysms, ref H+N-S-A-010) was taken with x-ray and testing. To generate a reference image based on the Laplacian algorithm for the image processing which derived from the cerebral blood vessel model was applied to DICOM by Volume ray casting technique. $A^*$ algorithm was used to provide the catheter with a guide wire tracking path. Finally, the result does show the location of the catheter and guide wire providing in the proposed system especially, it is expected to provide a useful guide for future intervention service.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.53-55
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• 2004

In emergency cases, such as the severe trauma involving the fracture of skull, spine, or cervical bone, from auto accident or a fall, and/or pneumothorax which can not be diagnosed exactly by the eye examination, it is necessary the radiological examination during transferring to the hospital for emergency care. The aim of this study was to design and evaluate the prototype of mobile medical image communication system based on CDMA 1X EVDO. The system consists of a laptop computer used as a transmit DICOM client, linked with cellular phone which support to the CDMA 1X EVDO communication service, and a receiving DICOM server installed in the hospital. The DR images were stored with DICOM format in the storage of transmit client. Those images were compressed into JPEG2000 format and transmitted from transmit client to the receiving server. All of those images were progressively transmitted to the receiving server and displayed on the server monitor. To evaluate the image quality, PSNR of compressed image was measured. Also, several field tests had been performed using commercial CDMA2000 1X-EVDO reverse link with the TCP/IP data segments. The test had been taken under several velocity of vehicle in seoul areas.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.3
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• pp.197-203
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• 2014

In this paper, we develop an attachable head of bed(HOB) monitoring system, which can prevent ventilator associated pneumonia(VAP), and analyze the performance of the developed HOB monitoring system. The main purpose of the HOB monitoring system is to support visible HOB display for keeping patients' position effectively and collect data for analysis of the relation between HOB elevation and patients' symptom. The HOB monitoring system is developed in attached-type and uses an FIR filter with heuristic logic to remove the unwanted noise. The optical encoder is used for the performance analysis of the developed HOB monitoring system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.385-386
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• 2018

최근 실감형 미디어에 대한 관심이 증폭되고 있으며 제조, 교육, 의료, 국방 등에 분야에서 기존 산업과 융합하여 많은 연구가 진행되고 있으며 MPEG에서도 이러한 실감형 미디어 기술에 대한 자체적인 표준화가 진행 중에 있다. 하지만 실감형 미디어에 대한 제작기술과 디스플레이기술에 대한 이슈는 있으나 콘텐츠 보호에 대한 기술 연구는 활발하게 진행되지 않고 있다. 더구나 실감형 미디어가 최근 웹하드, 토렌트 등에서 불법 유출 되고 있어 이에 대응한 저작권기술연구가 필요하다. 본 논문은 MPEG 산하에서 표준화가 진행되는 실감형 미디어 지원 포맷인 OMAF 구조를 설명하고 이에 대한 기술적 특징을 활용하여 특징점으로 활용될 수 있는 이미지 영역에 대한 선택 방안을 제안한다.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.165-181
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• 2009

This paper presents an effective representation scheme for the shape analysis of the hippocampal structure and a stereoscopic-haptic environment to enhance sense of realism. The parametric model and the 3D skeleton represent various types of hippocampal shapes and they are stored in the Octree data structure. So they can be used for the interactive shape analysis. And the 3D skeleton-based pose normalization allows us to align a position and an orientation of the 3D hippocampal models constructed from multimodal medical imaging data. We also have trained Support Vector Machine (SVM) for classifying between the normal controls and epileptic patients. Results suggest that the presented representation scheme provides various level of shape representation and the SVM can be a useful classifier in analyzing the shape differences between two groups. A stereoscopic-haptic virtual environment combining an auto-stereoscopic display with a force-feedback (or haptic) device takes an advantage of 3D applications for medicine because it improves space and depth perception.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Korean Journal of Optics and Photonics
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• v.34 no.4
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• pp.157-169
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• 2023

Augmented reality (AR) using a head mounted display (HMD) is used in various fields such as military, medicine, manufacturing, gaming, and education. In this paper, we discuss the design and fabrication of the AR optical system, which is most essential for HMD. The AR optical system for HMD requires a wide transparent area in which the augmented image of the display and the real world can be viewed at the same time. To this end, an AR optical system was designed and manufactured by dividing it into three parts according to each characteristic. Also, the refractive index of the ultra-violet (UV) adhesive layer required to make the three optical systems into one complete AR optical system was considered from the design stage to minimize the optical path shift phenomenon when the input light source passes through the UV adhesive layer. In addition, when designing the AR optical system, two aspheric surfaces were used to compensate for off-axis aberration and to be suitable for mass production. Finally, for HMD mass production, an aspheric AR optical system with a thickness of 11 mm, a diagonal field of view of 40°, and a weight of 11.3 g was designed and manufactured.

• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.17-21
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• 2009

The Recently, large area matrix-addressed image detectors are investigated for X-ray imaging with medical diagnostic and other applications. In this paper, a new flat panel gas detector for diagnostic X-ray imaging is proposed, and its characteristics are investigated. The research of flat panel gas detector is not exist at all. Because of difficulty to inject gas against to atmospheric pressure. So almost gas detector made by chamber shape. We made flat panel sample by display technique. (ex: PDP, Fed, etc.) The experimental measurements, the transparent electrodes, dielectric layer, and the MgO protection layer were formed in front glass. And, the X-ray phosphor layer and address electrodes are formed in the rare glass. The dark current, the x-ray sensitivity and linearity as a function of electric field were measured to investigate the electrical properties. From the results, the stabilized dark current density and the significant x-ray sensitivity were obtained. And the good linearity as a function of exposure dose was showed in wide diagnostic energy range. These results means that the passive matrix-addressed flat panel gas detector can be used for digital x-ray imaging.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.