• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.176-176
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• 2001

목적： 기존의 역수신코일(inside-out receiver coil)로 관벽의 MR 영상을 얻을 때 영상영역이 좁고 수신감도가 불균일한 단점을 보완하면서 신호대 잡음비(S/N ratio)도 높일 수 있는 회전자계역수 신코일(quadratic inside-out receiver coil)의 개발을 목적으로 한다. 대상 및 방법： 8극형코일, 선형자계수신코일, 회전자계수신코일에 대하여 컴퓨터 모의실험으로 영상영역 및 감도의 균일성을 비교하였다. 회전자계수신코일은 안장 모양을 한 두 개의 선형자계코일이 서로 간섭이 일어나지 않도록 수직 방향으로 배열된 구조를 갖도록 하였다. 지름 3 cm 크기로 각 코일을 제작하였으며 지름 20 cm의 원통 중앙에 내경 4 cm의 관이 있는 팬텀을 만들어 MnC1$_2$를 섞은 물을 채운 다음 1.5T 초전도 MRI 장치와 0.3T 영구자석 MRI 장치에서 팬텀의 관벽 영상을 얻었다. 본 실험을 시행한 1.5T 장치의 구조 때문에 회전자계코일의 두 안장코일을 결합하는 회로를 제작하여 사용하였고 0.3T에서는 장치에 내장된 결합회로를 사용하였다. 또한 포르말린 용액에 보관된 소의 대장 조직 단면 영상을 FOV 10-12 cm로 얻어 회전자 계안장코일의 성능을 평가하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.948-958
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• 2010

Human body signals collected by the MRI system are very weak, such that they may be easily affected by either external noise or system instability while being imaged. Therefore, this paper analyzes the nonuniformity caused by a design of the RF receiving coil in a low-magnetic-field MRI system, and proposes an efficient method to improve the image uniformity. In this paper, a method for acquiring 3D bias volume data by using phantom data among various methods for correcting such nonuniformity in MRI image is proposed, such that it is possible to correct various-sized images. It is shown by simulations that images obtained by various imaging methods can be effectively corrected using single bias data.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.10a
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• pp.266-271
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• 1998

본 논문에서는 칼라지도영상으로부터 GIS의 벡터링 과정에 사용할 벡터링 영역(도로, 해안선, 등고선 등)을 추출하는 방법에 대해 연구하였다. 입력영상으로는 트루칼라영상을 사용할 경우 추출 영역의 칼라가 비교적 균일하게 분포되지만 데이터량이 방대하여 처리에 어려움이 있어 현실적이지 못하므로 이를 양자화하여 256칼라 영상으로 변환한 후 사용할 수 있도록 하였다. 추출 단계에서는 Lab칼라공간에서 mahalanobis 거리 및 방향성 마스크를 사용하여 다양한 칼라 분포를 흡수할 수 있도록 하여 배경 영역을 배제하면서 연결성이 있는 추출결과를 얻을수 있도록 하였다. 그리고 추출된 결과를 원영상과 중첩해 보면서 기호, 문자 등의 요소로 인해 끊어진 영역이나 추출시 발생되는 피할 수 없는 잡영을 편집하여 제거할 수 있는 기능을 제공하였다. 추출된 결과는 벡터링 작업에 직접 사용 가능한 형태로 추출되도록 하였는데 실제 벡터링 작업에 다양한 추출영역을 사용해 봄으로써 이를 검증하였다.

• Journal of the Korean Society of Radiology
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• v.12 no.5
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• pp.631-636
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• 2018

The problem that the existing the magnetic resonance image (MRI) was prone to have not only long readout duration and low bandwidth in the phase-encode direction, but also geometric distortion was pointed out. The purpose of this study is to identify the usefulness of FOCUS-DWI through comparing FOCUS-DWI with the Conventional-DWI on a degree of uniformity and artifacts caused by the distance change in the central axis within the magnetic field. In terms of artifacts, there happened irregular striped artifacts in the Conventional-DWI technique, which in particular, more often arose in the central axis. Also, the overlap of imaging drastically increased. By contrast, there were no irregular striped artifacts in the FOCUS-DWI technique. In conclusion, it was found that the FOCUS-DWI technique was superior to the Conventional-DWI technique in terms of artifacts, the overlap of imaging, and a degree of uniformity. In addition, there was no difference of the change in distance from the central axis between the FOCUS-DWI technique and the Conventional-DWI technique. Thus, it is considered the FOCUS-DWI technique having less imaging distortion and high image quality will be highly clinically used.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.169-172
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• 2005

크기조절 트랜스코딩에서 움직임 벡터 재 예측 과정은 많은 연산량을 필요로 하기 때문에, 실시간 처리를 위해서는 이 과정의 연산량을 줄이는 것이 필요하다. 본 논문에서는 여러 영상에 대해 예측 움직임 벡터를 산출하는 방법을 적응적으로 수행함으로써, 기존 방법에 비해 화질열화 없이 연산량을 줄이는 방법을 제안한다. 전체 움직임의 크기와 움직임 벡터들의 균일성(homogeneity)을 이용하여 움직임이 작을 때는 움직임 벡터 재산출 과정 없이 예측 움직임 벡터 성분을 0으로, 움직임이 크면 움직임 벡터들의 균일성의 정도에 따라 평균값 또는 중간값을 예측 움직임 벡터 성분으로 적응적으로 선택하였다. 그리고 좀 더 효율적인 움직임 벡터 수행을 위해 제안된 과정을 수평, 수직 성분에 각각 따로 적용하였다. 가중치를 부여하여 평균값을 취하는 가중평균 방법과 비효 실험한 결과, 같은 PSNR을 유지하는 조건에서 움직임 벡터 재산출 과정의 덧셈과 곱셈 연산의 수가 평균적으로 각각 96%, 42% 정도 감소하였다.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.592-600
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• 2016

AAPM CT performance for special medical equipment quality control checks using a standard phantom for evaluation, using the evaluator's subjective assessment as to minimize errors due computerized assessment program to evaluate their usefulness. Phantom for evaluation AAPM CT Performance Phantom: was used, the default shooting conditions are the same as quality control checks. And, we use IMAGE J to evaluate the program. Quantitative evaluation with CT attenuation coefficient and the noise measurement, the uniformity measurement, the slice thickness measurement, contrast resolution of the measurement, a phantom image of the spatial resolution determined by the evaluation program is evaluated as self-extracting the result after processing the image, CT uniformity measurement for the evaluation that was smaller and the standard deviation of a video image processing more uniform slice thickness measurements it is difficult to evaluate due to the difference of the ratio of the measured value of the phantom image. Contrast resolution was measured cylindrical diameter 6th evaluate the shape of a circle obtained a mean value and a standard deviation of diameters, the spatial resolution of the group of source, including acceptance criteria automatically extracted result as a result of both the number of the extracted circularIt appeared. Evaluate the source image and video processing, and video to qualitative evaluation by gross were processed video image is shown excellent results. If the evaluators in order to minimize the errors of subjective judgment based on the results of the above should be done with a quantitative evaluation and qualitative evaluation utilizes a computerized assessment program is considered that further evaluation be made more efficient.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.141-144
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• 2002

영상에서의 칼라분포 정보는 영상간의 유사성을 표현하는데 매우 유용하여 내용기반 영상검색분야에서 기본적으로 사용하고 있다. 이때, 영상 데이터베이스에서의 각 영상에 대하여 동일한 방식으로 (비)균일하게 양자화하여 표현한 칼라 히스토그램이 주로 사용되고 있다. 그러나, 전체영상에 대하여 동일한 개수의 고정된 양자화를 통해 칼라분포 정보를 표현하는데, 여러 가지 문제점과 성능 차이가 있어 다양한 해결 방안이 연구되고 있다. 본 논문에서는, 적응적 양자화 방법으로 각 영상의 칼라분포 정보를 표현하되, 상이한 양자화 칼라간의 유사도를 정의하여 칼라히스토그램 인터섹션 방법과 유사하게 영상간의 칼라분포 유사도를 계산하는 방법을 제안한다. 양자화 칼라간의 유사도는 거리에 반비례하면서 두 양자화 칼라의 작은 빈도값에 비례하도록 정의하였다. 영상간의 칼라분포 유사도는 칼라 히스토그램 인터섹션 방법을 생산자-소비자 모델로 해석하여 구하는 방법을 제안한다. 제안한 방법에 의해 기존의 칼라 히스토그램 인터섹션 방법보다 향상된 결과를 얻을 수 있음을 실험을 통해 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.109-116
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• 2008

Even though many studies on parallel rendering based on PC clusters have been done. most of those did not cope with non-uniform scenes, where locations of 3D models are biased. In this work. we have built a PC cluster system with POV-Ray, a free rendering software on the public domain, and developed an adaptive load balancing scheme to optimize the parallel efficiency Especially, we noticed that a frame of 3D animation are closely coherent with adjacent frames. and thus we could estimate distribution of computation amount, based on the computation time of previous frame. The experimental results with 2 real animation data show that the proposed scheme reduces by 40% of execution time compared to the simple static partitioning scheme.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.465-473
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• 2002

Images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in radiography is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in reading. We have proposed adaptive weighted median(AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by homogeneous factor(HF). Homogeneous factor(HF) from the quantum noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the detection systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by visual C++ language on a IBM-PC Pentium 550 for testing purposes, the effects and results of the noise filtering were proposed by comparing with images of the other existing filtering methods.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.