• Journal of Biomedical Engineering Research
• /
• v.12 no.1
• /
• pp.43-48
• /
• 1991

As the computerized methods and equipments In nuclear medicine imaging increases, quantitative information is needed on the single photon emission computed tomographic Images as well as on the conventional nuclear medicine images. In this paper, the authors investigated the effect of several clinician - friendly reconstrution filters on the resultant transverse slices of backprojected Profiles of radioisotope distribution from the Quantitative point of view, and reduced the filter parameters such as cutoff frequency and order of filter which are neces mary to minimize the quantification error using computer-generated phantoms.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.578-582
• /
• 1994

In this paper reconstructing the internal resistivity and relative permittivity distribution is discussed. The iterative reconstruction method based on Finite Element method and Newton method were used to reconstruct both of resistivity ind permittivity distribution. The Finite Element model of impedance distribution is built in complex field of resistivity and capacitive medium. The reconstruction results based on computer simulated data and experimental data are presented.

• International journal of advanced smart convergence
• /
• v.7 no.2
• /
• pp.1-6
• /
• 2018

In this paper, we propose an automatic left ventricle segmentation method in computed tomography angiography (CTA) using separating energy function. First, we smooth the images by applying anisotropic diffusion filter to remove noise. Secondly, the volume of interest (VOI) is detected by using k-means clustering. Thirdly, we divide the left and right heart with split energy function. Finally, we extract only left ventricle from left and right heart with optimizing cost function including orientation term.

• Journal of Biomedical Engineering Research
• /
• v.10 no.2
• /
• pp.102-105
• /
• 1989

In electrical impedance tomography(EIT), we use boundary current and voltage measurements toprovide the information about the cross-sectional distribution of electrical impedance or resistivity. One of the major problems in EIT has been the inaccessibility of internal voltage or current data in finding the internal impedance values. We propose a new image reconstruction method using internal current density data measured by NMR. We obtained a two-dimensional current density distribution within a phantom by processing the real and imaginary MR images from a 4.77 NMR machine. We implemented a resistivity mage reconstruction algorithm using the finite element method and sensitivity matrix. We presented computer simulation results of the mage reconstruction algorithm and furture direction of the research.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.5
• /
• pp.335-342
• /
• 1991

A real-tme nonlinear parameter tomography is pumping wave method. This tomorgraphy has a merit which requires no 180$^{\circ}$ projection datum, while the ray-bending effect is remrkably remained on the reconstructed image. In this paper we intend to compensate this ray-bending effect using the perturbation method. Impulsive pumping wave makes derived compensative term simple form, nad the compensative image is easily obtained. We perform computer simulation to confirm the improvement of corrected imate.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.105-106
• /
• 2007

We present a fast reconstruction technique for elliptic objects, which can be applied to real-time computer tomography (CT) for simple geometric objects. It will be also shown that only 3 projections are needed to reconstruct an ellipse. A piecewise quadratic model is also proposed for more efficient Kalman filter based support estimation, which is used for the fast reconstruction technique. The performance of the piecewise quadratic model is compared with that of the existing piecewise linear model. Simulation results for the fast reconstruction are also presented.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.21 no.2
• /
• pp.1-7
• /
• 1984

An algorithm, which can obtain a reconstructed image from incomplete projections in computed tomography, is proposed. The algorithm is accomplished with a simple operations of iterative correction in reconstruction - reprojection process using the measured incomplete projections the object's crossection boundary, and so on, To demonstrate effectiveness of the algotithm the results of a computer simulation is presented.

• Journal of information and communication convergence engineering
• /
• v.2 no.3
• /
• pp.167-171
• /
• 2004

The evolution of modern imaging modalities, followed by the rapid development of computer technology has introduced many new features in the communication networks used in medical facilities. Since it is very important to keep patient's record accurately, the ability to exchange medical data securely over the communication network is essential for any medical information. In this paper, therefore, we introduce some problems which occur from digitizing medical images such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), CR(Computed Radiography), etc., and then we propose a authentication mechanism for medical image verification using secret watermark images.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2004.05a
• /
• pp.228-231
• /
• 2004

의학 분야에서 의료 영상 데이터에 해당하는 컴퓨터 단층 찰영(CT. Computer Tomography), 자기 공명 영상법 (MRI : Magnetic Resonance Imaging)둥의 데이터 등이 정확하고 신속한 진단ㆍ관리를 위하여 의료 영상 데이터 중에서 관심의 대상이 되는 영역은 무손실 압축 기법을 수행하고, 그외의 지역은 움직임 보상 방식을 사용하여 압축하는 방식을 제안하고 실험하였다. 그 결과 기존의 손실 압축 기법에 비하여 더 낮은 비트율로 효율적인 압축을 수행하였다.

• Journal of Korea Multimedia Society
• /
• v.23 no.6
• /
• pp.729-737
• /
• 2020

Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.