• 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.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.100-106
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• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.247-250
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• 2003

This paper describes the extraction method for brain region using cellular automata from the brain MR image. In the first removing the background from the brain MR image, and then extracting the brain region by applying the cellular automata rule obtained from histogram analysis information. The results on some experimental results showed that the PSNR is 42.11(dB) on image quality and also the correlation factor is estimated 98.46%. The result of this study can be used as the auto-diagnostics system.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1020-1028
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• 2019

Cerebrovascular disease is a common disease in the elderly population. However, we do not have enough understanding of brain-related diseases. Recent advances in microscopy technology have resulted in the acquisition of vast amounts of image data sets for small organs, and it has become possible to handle vast amounts of image data sets due to improved computer performance and software technology. In this paper, the author proposes introduce a method for classifying and analysing only cerebrovascular information in the mouse brain image, as well as a quantitative measure of the portion of the cerebrovascular in the mouse brain. The study of the cerebrovascular structure is significant, and it can be helpful to improve the understanding of cerebrovasculature. As a result, the author expects that this study will be useful for neuroscientists conducting clinical research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1006-1016
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• 2000

This study is to segment white matter, gray matter, and cerebrospinal fluid(CSF) on a brain MR image and to calculate the volume of each. First, after removing the background on a brain MR image, we segmented the whole region of a brain from a skull and a fat layer. Then, we calculated the partial volume of each component, which was present in scanning finite thickness, with the arithmetical analysis of gray value from the internal region of a brain showing the blurring effects on the basis of the MR image forming principle. Calculated partial volumes of white matter, gray matter and CSF were used to determine the threshold for the segmentation of each component on a brain MR image showing the blurring effects. Finally, the volumes of segmented white matter, gray matter, and CSF were calculated. The result of this study can be used as the objective diagnostic method to determine the degree of brain atrophy of patients who have neurodegenerative diseases such as Alzheimer's disease and cerebral palsy.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.450-453
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• 2002

In radiotherapy treatment planning, it is critical to deliver the radiation dose to tumor and protect surrounding normal tissue. Recent developments in functional imaging and radiotherapy treatment technology have been raising chances to control tumor saving normal tissues. A brain phantom which could be used for image registration technique of CT-MR and CT-SPECT images using surface matching was developed. The brain phantom was specially designed to obtain imaging dataset of CT, MR, and SPECT. The phantom had an external frame with 4 N-shaped pipes filled with acryl rods, Pb rods for CT, MR, and SPECT imaging, respectively. 8 acrylic pipes were inserted into the empty space of the brain phantom to be imaged for geometric evaluation of the matching. For an optimization algorithm of image registration, we used Downhill simplex algorithm suggested as a fast surface matching algorithm. Accuracy of image fusion was assessed by the comparison between the center points of the section of N-shaped bars in the external frame and the inserted pipes of the phantom and minimized cost functions of the optimization algorithm. Technique with partially transparent, mixed images using color on gray was used for visual assessment of the image registration process. The errors of image registration of CT-MR and CT-SPECT were within 2mm and 4mm, respectively. Since these errors were considered within a reasonable margin from the phantom study, the phantom is expected to be used for conventional image registration between multimodal image datasets..

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.203-206
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• 2001

The fMRI which can express the function of brain as MR image is now being studied. The study on the functional image has usually been performed with the MRI in 4 tesla class in goneral, but if gradient echo imaging method could be used, it might make the most of what it has with the MRI in 1.5 tesla class. However, the lack of adequate image post-processing software prevents it from being used as widely as it could be. For the image post-processing algorithm of the functional image, subtraction method and several statistical methods are used with continuous introduction of new method recently. In this paper, we suggest adaptation of wavelet algorithm for obtaining a more reliable brain function map.

• Journal of radiological science and technology
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• v.42 no.4
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• pp.279-284
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• 2019

The purpose of this study was to find the best protocol for balance of image quality and dose in brain CT scan. Images were acquired using dual-source CT and AAPM water phantom, noise and dose were measured, and effective dose was calculated using computer simulation program ALARA(S/W). In order to determine the ratio of image quality and dose by each protocol, FOM (figure of merits) equation with normalized DLP was presented and the result was calculated. judged that the ratio of image quality and dose was excellent when the FOM maximized. Experimental results showed that protocol No. 21(120 kVp, 10 mm, 1.5 pitch) was the best, the organ with the highest effective dose was the brain(33.61 mGy). Among organs with high radiosensitivity, the thyroid gland was 0.78 mGy and breast 0.05 mGy. In conclusion, the optimal parameters and the organ dose in the protocol were also presented from the experiment, It may be helpful to clinicians who want to know the protocol about the optimum state of image quality and dose.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.