• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.315-330
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• 2012

Recently, methodologies for developing brain-computer interface (BCI) games using the BCI have been actively researched. The existing general framework for processing brain waves does not provide the functions required to develop BCI games. Thus, developing BCI games is difficult and requires a large amount of time. Effective BCI game development requires a BCI game framework. Therefore the BCI game framework should provide the functions to generate discrete values, events, and converted waves considering the difference between the brain waves of users and the BCIs of those. In this paper, BCI game frameworks for processing brain waves for BCI games are proposed. A variety of processes for converting brain waves to apply the measured brain waves to the games are also proposed. In an experiment the frameworks proposed were applied to a BCI game for visual perception training. Furthermore, it was verified that the time required for BCI game development was reduced when the framework proposed in the experiment was applied.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.455-458
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• 2010

Extracting the brain from magnetic resonance imaging head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool produces a brain mask which may be too smooth for practical use to reduce the accuracy. This paper presents a novel and indirect brain extraction method based on non-brain tissue segmentation. Based on ITK, the proposed method allows a non-brain contour by using region growing to match with the original image naturally and extract the brain tissue. Experiments on two set of MRI data and 2D brain image in horizontal plane and 3D brain model indicate successful extraction of brain tissue from a head.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.343-344
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• 2009

The aim in this paper is to show how to extract scalp of a series of brain MR images by using region growing segmentation algorithm. Most researches are all forces on the segmentation of skull, gray matter, white matter and CSF. Prior to the segmentation of these inner objects in brain, we segmented the scalp and the brain from the MR images. The scalp mask makes us to quickly exclude background pixels with intensities similar those of the skull, while the brain mask obtained from our brain surface. We make use of connected threshold method (CTM) and confidence connected method (CCM). Both of them are two implementations of region growing in Insight Toolkit (ITK). By using these two methods, the results are displayed contrast in the form of 2D and 3D scalp images.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.336-339
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• 2012

VTK is a free but professional development platform for images three-dimensional (3D) reconstruction and processing. It is powerful, open-source, and users can customize their own needs by self-development of great flexibility. To give the doctors more and detailed information by simulate dissection to the 3-D brain MRI image after reconstruction. A Visualization System (VS) is proposed to achieve 3D brain reconstruction and virtual dissection functions. Based on the free VTK visualization development platform and Visual Studio 2010 IDE development tools, through C++ language, using real people's MRI brain dataset, we realized the images 3D reconstruction and also its applications and extensions correspondingly. The display effect of the reconstructed 3D image is well and intuitive. With the related operations such as measurement, virtual dissection and so on, the good results we desired could be achieved.

• Science of Emotion and Sensibility
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• v.9 no.4
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• pp.307-319
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• 2006

Problematic behaviors have been among adolescent population in school and home. Problematic behavior manifested in childhood and adolescence is reported to be a good predictor for adult criminal behavior although no clear factor to cause was identified. Based on literature review on this subject, our hypotheses that delinquency and aggressive behaviors are associated with brain information processing and emotionality in adolescents was developed and this study aimed to test these hypotheses. 1,479 male and female middle school students were selected and given the Trait Meta-Mood Scale, Korea Youth Self Report-Child Behavior Check List and Brain Preference Indicator Test. The main results are as follows: 1) Subjects with problematic behavior compared to average students showed a significant difference in sub-variables of emotionality ant the characteristics of brain information processing. 2) Young adolescent's emotionality and brain information processing characteristics have effects on problematic behaviors. 3) However, the effect on aggression and delinquency was different by gender.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.

• Journal of Gifted/Talented Education
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• v.21 no.1
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• pp.175-191
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• 2011

This study is to investigate the creative thinking style and it's leaning that normal students and scientifically gifted students use mainly at processing information. Right Brain vs Left Brain Creativity Test(R/LCT) and Brain Preference Indicator(BPI) is taken to investigate the creative thinking style of normal students(N=144) and scientifically gifted students(N=97). In the R/LCT, the normal students responded that they prefer to use right-brain thinking rather than left-brain thinking. But the scientifically gifted students prefer to left-brain thinking. The normal students showed most preference for Holistic Processing of right side brain and they did most avoiding for Verbal Processing of left side brain. The scientifically gifted students showed most preference for Logical Processing of left side brain. And they did most avoiding for Random Processing of right side brain. There was a meaningful difference between left side brain preference group and right side brain preference group on Sequential, Symbolic, Logical, Verbal, Random, Intuitive, Fantasy-oriented Processing of normal Students. But the scientifically gifted students showed a meaningful difference in right side brain processing mainly. In other word, all the scientifically gifted students took an lean processing in Logical, Symbolic, Linear Processing, etc. In sum, the scientifically gifted students are unequal in at processing information against the normal students. So it is required more appropriate teaching-learning method based on the creative thinking style and it's leaning for effective gifted education.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.977-991
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• 2000

In this paper, a medical image processing system was designed and implemented for morphometric and functional analysis of a human brain. The system is composed of image registration, ROI(region of interest) analysis, functional analysis, image visualization, 3D medical image database management system(DBMS), and database. The software processes an anatomical and functional image as input data, and provides visual and quantitative results. Input data and intermediate or final output data are stored to the database as several data types by the DBMS for other further image processing. In the experiment, the ROI analysis, for a normal, a tumor, a Parkinson's decease, and a depression case, showed that the system is useful for morphometric and functional analysis of a human brain.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.542-551
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• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.