• PNF and Movement
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• v.14 no.3
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• pp.231-236
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• 2016

Purpose: The purpose of this study was to compare cerebral motor area activation between the diagonal and straight movements of the lower extremity. Methods: The subjects of this study consisted of two right-handed adults. Functional magnetic resonance imaging was conducted to measure brain activation following the diagonal and straight movements of the lower extremity. The primary motor area, premotor area, and supplementary motor area, which are closely related to exercise, were set as the regions of interest. Results: The brain activation by diagonal movement was an average of $1036{\pm}75$ voxel, and brain activation by straight exercise was an average of $773{\pm}55$ voxel. Conclusion: Based on these results, we conclude that the activation of the cerebral motor area is more effective for diagonal movements than for straight movements.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.22 no.1
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• pp.117-127
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• 1992

This investigation was performed to test the readability of the video based digital radiography, that can be applied clinically, compared with the periapical radiograph. The experiments were performed with IBM-PC/AT compatible, video camera and ADC (analog-digital converter). And spatial resolution was 512 X 480 with 256 (8 bit) gray levels. The radiographs obtained by using variable steps of exposure time were digitized. and then the digital images were analyzed. The obtained results were as follows: 1. There was no remarkable difference in readability between the radiographs and their digital images. However, under over exposure the digital images were superior to the radiographs in readability and vice versa. 2. As the exposure time was increased, the gray level of the digital image was decreased proportionally. 3. The correlation beween the regions of interest and the aluminum step wedges were relatively close; R=0.9965 (p <0.001).

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.425-429
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• 2008

We investigated algorithm to detect and characterize interstitial lung abnormalities seen at chest radiographs. This method includes a process of 4 directional Laplaction-Gaussian filtering, and a process of linear opacity judgment. Two regions of interest (ROIs) were selected in each right lung of patients, and these ROIs were processed by our computer-analyzing system. For quantitative analysis of interstitial opacities, the radiographic index, which is the percentage of opacity areas in a ROI, was obtained and evaluated in the images. From or result, abnormal lungs were well differentiated from normal lungs. In our algorithm, the processing results were not only given as the numeric data named "radiographic index" but also confirmed with radiologists observation on CRT. The approach, by which the interstitial abnormalities themselves are extracted, is good enough because the results can be confirmed by the observations of radiologists. In conclusion, our system is useful for the detection and characterization of interstitial lung abnormalities.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1802-1806
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• 2007

Nowadays, many configurations and applications of small atmospheric plasma source have been investigated with growing interest, as it provides the bacteria inactivation, the surface modification and removal of unwanted small regions, and so on. In this paper, the non-thermal micro plasma source under atmospheric pressure by means of submicrosecond pulse voltage waveforms is suggested. Plasma operates in helium is appears as a small (sub-mm) glow at the tip of a plasma gun. Electrical measurements show that the plasma source operates at low voltage (about 500V) and the power consumption is about 1W at 25kHz. Moreover, the emission spectrum shows the relatively higher emission intensity of oxygen particles than those of helium and nitrogen.

• International Journal of Contents
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• v.11 no.4
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• pp.77-85
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• 2015

Table detection is a challenging problem and plays an important role in document layout analysis. In this paper, we propose an effective method to identify the table region from document images. First, the regions of interest (ROIs) are recognized as the table candidates. In each ROI, we locate text components and extract text blocks. After that, we check all text blocks to determine if they are arranged horizontally or vertically and compare the height of each text block with the average height. If the text blocks satisfy a series of rules, the ROI is regarded as a table. Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging. This proves the effectiveness and superiority of our proposed method.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.35-42
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• 2018

Direct volume rendering (DVR) is an important 3D visualization method for medical images as it depicts the full volumetric data. However, because DVR renders the whole volume, regions of interests (ROIs) such as a tumor that are embedded within the volume maybe occluded from view. Thus, conventional 2D cross-sectional views are still widely used, while the advantages of the DVR are often neglected. In this study, we propose a new visualization algorithm where we augment the 2D slice of interest (SOI) from an image volume with volumetric information derived from the DVR of the same volume. Our occlusion-based DVR augmentation for SOI (ODAS) uses the occlusion information derived from the voxels in front of the SOI to calculate a depth parameter that controls the amount of DVR visibility which is used to provide 3D spatial cues while not impairing the visibility of the SOI. We outline the capabilities of our ODAS and through a variety of computer tomography (CT) medical image examples, compare it to a conventional fusion of the SOI and the clipped DVR.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.153-156
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• 2003

In this paper, we propose a new scheme for automatic segmentation of the liver in CT images. The proposed scheme is carried out on region of interest(ROI) blocks that include regions of the liver with high probabilities. The ROI approach saves unnecessary computational loss in finding the accurate boundary of the liver. The proposed method utilizes the composition of multi-size morphological filters with a prior knowledge, such as the general location or the approximate intensity of the liver to detect the initial boundary of the liver. Then, we make the gradient image with the weight of the initial liver boundary and segment the liver legion by using an immersion-based waters hed algorithm in the gradient image. finally, the refining process is carried out to acquire a more accurate liver region.

• Smart Media Journal
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• v.9 no.3
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• pp.9-17
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• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

• Journal of the Korean Mathematical Society
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• v.37 no.1
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• pp.31-44
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• 2000

We study a free boundary problem satisfying Bernoulli type boundary condition along which the gradient of a piecewise harmonic solution jumps zero to a given constant value. In such problem, the free boundary splits the domain into two regions, the zero set and the harmonic region. Our main interest is to identify the global shape and the location of the zero set. In this paper, we find the lower and the upper bound of the zero set. In a convex domain, easier estimation of the upper bound and faster disk test technique are given to find a rough shape of the zero set. Also a simple proof on the convexity of zero set is given for a connected zero set in a convex domain.