• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.849-854
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• 2001

GTM(Generative Topographic Mapping) model is a probabilistic version of the SOM(Self Organizing Maps) which was proposed by T. Kohonen. The GTM is modelled by latent or hidden variables of probability distribution of data. It is a unique characteristic not implemented in SOM model, and, therefore, it is possible with GTM to analyze data accurately, thereby overcoming the limits of SOM. In the present investigation we proposed a BGTM(Bayesian GTM) combined with Bayesian learning and GTM model that has a small mis-classification ratio. By combining fast calculation ability and probabilistic distribution of data of GTM with correct reasoning based on Bayesian model, the BGTM model provided improved results, compared with existing models.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.29-34
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• 2002

In this paper, we present a new geometric active contour model based on level set methods introduced by Osher and Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image. Using anisotropic diffusion filtering for each slice, we have the result with reduced noise and extracted exact shape. Volume rendering operates on three-dimensional data, processes it, and transforms it into a simple two-dimensional image.

• The Journal of Internal Korean Medicine
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• v.40 no.1
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• pp.58-69
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• 2019

Objective: This study investigated the effects of Hansu-Daebowon (HDW) on bone resorption in vitro and bone loss in vivo. Methods: Osteoclast differentiation was measured by counting TRAP (+) MNC formed from RAW 264.7 in the presence of RANKL. Bone pit formation was determined in an artificial bone slice loaded with RANKL-stimulated osteoclasts. To elucidate the mechanisms of the inhibitory effects of HDW on bone resorption and osteoclast differentiation, osteoclastogenic genes (i.e. TRAP, MMP-9, NFATc1, c-Fos, and Cathepsin K) were measured using real time PCR. Furthermore, bone loss was observed using micro-CT in an LPS-treated mammal model. Results: HDW inhibited the bone pit formation in vitro and inhibited bone loss in vivo. Moreover, HDW decreased the number of TRAP (+) MNCs in the presence of RANKL, and HDW inhibited the expressions of cathepsin K, MMP-9, TRAP, NFATc1, and c-Fos in the osteoclasts. Conclusion: HDW exerts inhibitory effects on bone loss and bone resorption resulting from the inhibitions of osteoclast differentiation and osteoclastogenic gene expression.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.163-174
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• 2004

The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.

• Journal of Korea Multimedia Society
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• v.9 no.7
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• pp.818-826
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• 2006

In this paper, we investigate the relationship of bitrate and quantization parameter needed for the trans-coding method that makes the H.264 bitstream of a particular bitrate to the other titrate. Also we propose the new method in order to transcode the titrate between H.264 video coded bitstreams. The proposed transcoding method updates the model parameters from previous picture or slice by using the approximated relationship of bitrate and quantization step-size and finds the target quantization step-size, and then generates the target titrate by simple coding processing just after requantization. Therefore, the proposed method does not need the complex bitrate control and converts to the target titrate by simple implementation. From simulation, we can see that the proposed method transcodes exactly to an assigned target bitrate for the four test sequences with different their characteristics.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.1-11
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• 2007

H.264/AVC uses variable block sizes to achieve significant coding gain. It has 7 different coding modes having different motion compensation block sizes in Inter slice, and 2 different intra prediction modes in Intra slice. This fine-tuned new coding feature has achieved far more significant coding gain compared with previous video coding standards. However, extremely high computational complexity is required when rate-distortion optimization (RDO) algorithm is used. This computational complexity is a major problem in implementing real-time H.264/AVC encoder on computationally constrained devices. Therefore, there is a clear need for complexity reduction algorithm of H.264/AVC such as fast mode decision. In this paper, we propose a fast mode decision with early $P8\times8$ mode rejection based on block size activity using large block history map (LBHM). Simulation results show that without any meaningful degradation, the proposed method reduces whole encoding time on average by 53%. Also the hybrid usage of the proposed method and the early SKIP mode decision in H.264/AVC reference model reduces whole encoding time by 63% on average.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.96-106
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• 2014

Previous academic research on FPGA tools has relied on simple imaginary models for the targeting architecture. As the first step to overcome such restriction, the issues on analytic placement and legalization which are applied to commercial FPGAs have been brought up, and several techniques to remedy them are presented, and evaluated. First of all, the center of gravity of the placed cells may be far displaced from the center of the chip during analytic placement. A function is proposed to be added to the objective function for minimizing this displacement. And then, the density map is expanded into multiple layers to accurately calculate the density distribution for each of the cell types. Early fixation is also proposed for the memory blocks which can be placed at limited sites in small numbers. Since two flip-flops share control pins in a slice, a compatibility constraint is introduced during legalization. Pre-packing compatible flip-flops is proposed as a proactive step. The proposed techniques are implemented on the K-FPGA fabric evaluation framework in which commercial architectures can be precisely modeled, and modified for enhancement, and validated on twelve industrial strength examples. The placement results show that the proposed techniques have reduced the wire length by 22%, and the slice usage by 5% on average. This research is expected to be a development basis of the optimization CAD tools for new as well as the state-of-the-art FPGA architectures.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.51-61
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• 2004

In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

• Journal of the Korea Computer Graphics Society
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• v.22 no.5
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• pp.17-26
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• 2016

This paper presents an adaptive slicing method based on merging vertical layer polylines. Firstly, we slice the input 3D polygon model uniformly with the minimum printable thickness, which results in bounding polylines of the cross section at each layer. Next, we group a set of layer polylines according to vertical connectivity. We then remove polylines in overdense area of each group. The number of layers to merge is determined by the layer thickness computed using the cusp height of the layer. A set of layer polylines are merged into a single polyline by removing the polylines within the layer thickness. The proposed method maintains the shape features as well as reduces the printing time. For evaluation, we sliced ten 3D polygon models using our method and a global adaptive slicing method and measured the total length of polylines which determines the printing time. The result showed that the total length from our method was shorter than the other method for all ten models, which meant that our method achieved less printing time.

• Journal of radiological science and technology
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• v.46 no.6
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• pp.535-542
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• 2023

This study aimed to implement a deep learning model that can perform quantitative quality control through ACTS software used for quantitative evaluation of ACR phantom in CT quality control and evaluate its usefulness. By changing the scanning conditions, images of three modules of the ACR phantom's slice thickness (ST), low contrast resolution (LC), and high contrast resolution (HC) were obtained and classified as ACTS software. The deep learning model used ResNet18, implementing three models in which ST, HC, and LC were learned with epoch 50 and an integrated model in which three modules were learned with Epoch 10, 30, and 50 at once. The performance of each model was evaluated through Accuracy and Loss. When comparing and evaluating the accuracy and loss function values of the deep learning models by ST, LC, and HC modules, the Accuracy and Loss of the HC model were the best with 100% and 0.0081, and in the integrated model according to the Epoch value, Accuracy and Loss with epoch 50 were the best with 96.29% and 0.1856. This paper showed that quantitative quality control is possible through a deep learning model, and it can be used as a basis and evidence for applying deep learning to the CT quality control.