• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.409-417
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• 1999

Talairach atlas consists of three orthogonal sets of coronal, sagittal, and axial slices. This atlas has recently an important role as a standard brain atlas in diagnosing disease related with brain function and analyzing cause of brain disease. The 3D digital volume data set reconstructed from the atlas is widely applied to visualization and quantitative analysis of results processed in the digital computer. This paper represented application method of bi-linear interpolation technique, proposed tri-planar interpolation algorithm for 3D volume data reconstruction of Talairach atlas. And we implemented Talairach atlas editor and discussed problems in volume reconstruction of Talairach atlas. The bi-linear method was applied to only one set of the slices and considered the on intensity value in the interpolation process. The tri-planar technique concurrently uses three orthogonal sets of slices with the same information of brain structures. Talairach atlas editor visualized three sets. of atlas slices on the same coordinate and had editing function. Using the atlas editor, we represented problems in volume reconstruction by showing inconsistency of brain structures among three sets of atlas slices.

• Journal of Biomedical Engineering Research
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• v.26 no.1
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• pp.9-16
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• 2005

One of the most important roles of a brain atlas is providing a spatial reference system in which multiple images can be interpreted in a consistent way. The brain atlase based on Western populations such as the International Consortium for Brain Mapping's 452 T-1 Weighted Average Atlas was widely used; however, they may not be the optimal choice for use with brain images from other ethnic groups, because structural differences between occidental and oriental brains have been reported. Therefore, in this study, we created an average brain atlas from 100 healthy Koreans (100 cases (M/F=53/47), 39.0±17.0 years). The purpose of this study was to make a Korean average-brain atlas and to measure its differences from a widely accepted average brain atlas built on an occidental population. The average brain atlas for Koreans was developed using widely accepted tools and procedures. The comparison between the Korean and occidental averages was performed using tissue probability maps and a registration tool, and it was shown that the global pattern of differences between the two average brains found in this work agreed with previously reported differences: Korean brains are wider and shorter in size, and smaller in volume, yet no hemispheric volume asymmetry was found.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.981-984
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• 2014

Volumetric measurement of hippocampus using IBASPM, the 20's normal adults 10 people's brain images were acquired in order to assess the changes according to the type of the Atlas. Images was obtained using MPRAGE of a 3-D gradient echo pulse sequence on Head matrix coil of 1.5T MRI system. The results of Paired t-test using obtained volume of hippocampus depending on the type of the Atlas, Atlas69-Altas84, Atlas69-Atlas116(p=0.729, 0.729) in the left hippocampus and Atlas69-Atlas84, Atlas69-Atlas116(p=0.219, 0.219) in right hippocampal formation were no significant differences but in the area except this, there was significant difference(p=0.000). The volume of the hippocampus using Atlas84 and Atlas116, represented the same value and there was no significant difference. In the image analysis using the overlay of atlas image and original image, Atlas71 could be found that the area of hippocampus did mismatch. In the case of atlas used in this study, because it has been developed by the westerners, there are differences between brain of asian. It would be needed to development of new Atlas for high accuracy measurement of the volume of hippocampus.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.233-234
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• 1995

Tri-planar interpolation of the orthogonal digital brain Atlas is proposed to achieve a higher resolution of a volume-metric atlas. With these expanded dataset, the brain mapping will be accomplished with fewer registration errors.

• The Magazine of the IEIE
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• v.36 no.11
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• pp.66-82
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• 2009

Probabilistic atlases for the human brain structure are more suitable than single brain atlases for representing population anatomy. In this study, we hypothesized the group-specific probabilistic atlas for accurate characteristic feature coding. Our proposed method for a new group comparison study, using a subpopulation specific probabilistic atlas, was based on this hypothesis. A knowledge-based automatic labeling technique using nonlinear registration was applied to encode group-specific regional probabilistic information. Direct atlas-based comparison using volume counting above the probability threshold, distance measurement and correlation analysis were performed based on the probabilistic atlas. Here, we applied this method for comparison between Korean and occidental groups. The results showed that this method could provide simple but intuitive regions of interest-based group analysis for the entire cortex area.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.171-172
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• 1995

In this paper, an anatomical labeling system for assisting localization of region of interest on human brain imaging is represented. Model image for labeling anatomical name on the other image is Atlas. Object image to be labeled, such as CT, MR, and PET, is registered onto Atlas. And then, anatomical name for region of interest is appeared on a window by clicking mouse button on object image. The same part named anatomically on that region is labeled and drawn on object image.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• Journal of KIISE
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• v.43 no.1
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.