• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.183-186
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• 2007

Realistic deformation of computer simulated anatomical structures is computationally intensive. As a result, simple methodologies not based in continuum mechanics have been employed for achieving real time deformation of virtual reality. Since the graphical interpolations and simple spring models commonly used in these simulations are not based on the biomechanical properties of tissue structures, these "quick and dirty"methods typically do not accurately represent the complex deformations and force-feedback interactions that can take place during surgery. Finite Element(FE) analysis is widely regarded as the most appropriate alternative to these methods. However, because of the highly computational nature of the FE method, its direct application to real time force feedback and visualization of tissue deformation has not been practical for most simulations. If the mathematics are optimized through pre-processing to yield only the information essential to the simulation task run-time computation requirements can be drastically reduced. To apply the FEM, We examined a various in verse matrix method and a deformed material model is produced and then the graphic deformation with this model is able to force. As our simulation program is reduced by the real-time calculation and simplification because the purpose of this system is to transact in the real time.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.9-14
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• 2002

In cases of densely occluded urban scenes, it is effective to determine the visibility of scenes, since only small parts of the scene are visible from a given cell. In this paper, we introduce a new visibility preprocessing method that efficiently computes potentially visible objects for volumetric cells. The proposed method deals with general 3D polygonal models and invisible objects jointly blocked by multiple occluders. The proposed approach decomposes volume visibility into a set of point visibilities, and then computes point visibility using hardware visibility queries, in particular HP_occlusion_test and NV_occlusion_query. We carry out experiments on various large-scale scenes, and show the performance of our algorithm.

• Journal of the Korean Society of Visualization
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• v.2 no.2
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• pp.8-15
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• 2004

With all the recent progresses in computer hardware and software technology, the animation of fluids in real-time is still among the most challenging issues of computer graphics. The fluid animation is carried out in two steps - the physical simulation of fluids immediately followed by the visual rendering. The physical simulation is usually accomplished by numerical methods utilizing the particle dynamics equations as well as the fluid mechanics based on the Navier-Stokes equations. Particle dynamics method is usually fast in calculation, but the resulting fluid motion is conditionally unrealistic. The methods using Navier-Stokes equation, on the contrary, yield lifelike fluid motion when properly conditioned, yet the complexity of calculation restrains this method from being used in real-time applications. This article presents a rapid fluid animation method by using the continuum-based fluid mechanics and the enhanced particle dynamics equations. For real-time rendering, pre-integrated volume rendering technique was employed. The proposed method can create realistic fluid effects that can interact with the viewer in action, to be used in computer games, performances, installation arts, virtual reality and many similar multimedia applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.737-739
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• 2023

몰입형 가상현실 시스템은 더 나은 3차원 시각정보를 제공할 수 있어, 의료계에서 해부학에 대한 이해를 높이는 데 사용되고 있다. 우리는 몰입형 가상현실에서 다중 사용자가 함께 MRI 영상으로부터 생성된 볼륨 렌더링 된 객체를 관찰하고 수술을 계획할 수 있는 시스템을 개발하여 소개하고자 한다.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.967-976
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• 2009

In this paper, we propose an automatic lower extremity vessel extraction based on rigid registration and bone elimination techniques in CT and CT angiography images. First, automatic partitioning of the lower extremity based on the anatomy is proposed to consider the local movement of the bone. Second, rigid registration based on distance map is performed to estimate the movement of the bone between CT and CT angiography images. Third, bone elimination and vessel masking techniques are proposed to remove bones in CT angiography image and to prevent the vessel near to bone from eroding. Fourth, post-processing based on vessel tracking is proposed to reduce the effect of misalignment and noises like a cartilage. For the evaluation of our method, we performed the visual inspection, accuracy measures and processing time. For visual inspection, the results of applying general subtraction, registered subtraction and proposed method are compared using volume rendering and maximum intensity projection. For accuracy evaluation, intensity distributions of CT angiography image, subtraction based method and proposed method are analyzed. Experimental result shows that bones are accurately eliminated and vessels are robustly extracted without the loss of other structure. The total processing time of thirteen patient datasets was 40 seconds on average.

• Journal of the Korea Computer Graphics Society
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• v.26 no.1
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• pp.17-25
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• 2020

We present a novel multi-core CPU based parallel algorithm for the cell-connectivity information extraction algorithm, which is one of the preprocessing steps for volume rendering of unstructured grid data. We first check the synchronization issues when parallelizing the prior serial algorithm naively. Then, we propose a 3-step parallel algorithm that achieves high parallelization efficiency by removing synchronization in each step. Also, our 3-step algorithm improves the cache utilization efficiency by increasing the spatial locality for the duplicated triangle test process, which is the core operation of building cell-connectivity information. We further improve the efficiency of our parallel algorithm by employing a memory pool for each thread. To check the benefit of our approach, we implemented our method on a system consisting of two octa-core CPUs and measured the performance. As a result, our method shows continuous performance improvement as we add threads. Also, it achieves up to 82.9 times higher performance compared with the prior serial algorithm when we use thirty-two threads (sixteen physical cores). These results demonstrate the high parallelization efficiency and high cache utilization efficiency of our method. Also, it validates the suitability of our algorithm for large-scale unstructured data.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.191-197
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• 2013

Conventional CT and MRI scans produce cross-section slices of body that are viewed sequentially by radiologists who must imagine or extrapolate from these views what the 3 dimensional anatomy should be. By using sophisticated algorithm and high performance computing, these cross-sections may be rendered as direct 3D representations of human anatomy. The 2D medical image analysis forced to use time-consuming, subjective, error-prone manual techniques, such as slice tracing and region painting, for extracting regions of interest. To overcome the drawbacks of 2D medical image analysis, combining with medical image processing, 3D visualization is essential for extracting anatomical structures and making measurements. We used the gray-level thresholding, region growing, contour following, deformable model to segment human organ and used the feature vectors from texture analysis to detect harmful cancer. We used the perspective projection and marching cube algorithm to render the surface from volumetric MR and CT image data. The 3D visualization of human anatomy and segmented human organ provides valuable benefits for radiation treatment planning, surgical planning, surgery simulation, image guided surgery and interventional imaging applications.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.36-45
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• 2015

It is important subject of research in engineering and natural science field that creating continuing high-definition image from very large volume data. The necessity of software that helps analyze useful information in data has improved by effectively showing visual image information of high resolution data with visualization technique. In this paper, we designed multi-platform visualization system based on client-server to analyze and express earth environment data effectively constructed with observation and prediction. The visualization server comprised of cluster transfers data to clients through parallel/distributed computing, and the client is developed to be operated in various platform and visualize data. In addition, we aim user-friendly program through multi-touch, sensor and have made realistic simulation image with image-based lighting technique.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.445-449
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• 2009

In this paper, we propose a computationally efficient multi GPU accelerated image registration technique to correct the motion difference between the pre-contrast CT image and post-contrast CTA image. Our method consists of two steps: multi GPU based image registration and a cerebrovascular visualization. At first, it computes a similarity measure considering the parallelism between both GPUs as well as the parallelism inside GPU for performing the voxel-based registration. Then, it subtracts a CT image transformed by optimal transformation matrix from CTA image, and visualizes the subtracted volume using GPU based volume rendering technique. In this paper, we compare our proposed method with existing methods using 5 pairs of pre-contrast brain CT image and post-contrast brain CTA image in order to prove the superiority of our method in regard to visual quality and computational time. Experimental results show that our method well visualizes a brain vessel, so it well diagnose a vessel disease. Our multi GPU based approach is 11.6 times faster than CPU based approach and 1.4 times faster than single GPU based approach for total processing.