• Journal of Magnetics
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• v.21 no.1
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• pp.148-152
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• 2016

The main goal was to evaluate effectiveness of a modified TSE sequence compared with DIR (double inversion recovery) sequence in acquisition of fast flow brain vessel images using signal void effect. 32 healthy volunteers (10 men and 22 women; mean age of 31 years; ranging between 28-43 years) who underwent black blood DIR sequence (group A) and the modified TSE sequence (group B) were enrolled in our study. Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) of the internal carotid arteries' lumen were compared in T1 and T2 weighted images for both group A and B. The images obtained from group B showed lower SNR values in internal carotid artery than the group A in both of the T1 and T2 weighted images (11.49% and 13.66% respectively). While the CNR values were higher in the group B than the group A in both of the T1 and T2 weighted images (8.69% and 7.55 % respectively). The qualitative score of all categories were not significantly different between the two groups. Furthermore approximately 49% of the total scan time was reduced from group B. Our study is to shorten the scanning time and minimize the inconveniences of the patients in acquisition of the black blood images of brain by using the signal void effect in the modified TSE technique while keeping the diagnostic value of the test.

• Current Optics and Photonics
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• v.2 no.5
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• pp.453-459
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• 2018

We have developed a high-performance signal-processing and image-rendering heterogeneous computation system for optical coherence tomography (OCT) on mobile processor. In this paper, we reveal it by demonstrating real-time OCT image processing using a Snapdragon 800 mobile processor, with the introduction of a heterogeneous image visualization architecture (HIVA) to accelerate the signal-processing and image-visualization procedures. HIVA has been designed to maximize the computational performances of a mobile processor by using a native language compiler, which targets mobile processor, to directly access mobile-processor computing resources and the open computing language (OpenCL) for heterogeneous computation. The developed mobile image processing platform requires only 25 ms to produce an OCT image from $512{\times}1024$ OCT data. This is 617 times faster than the naïve approach without HIVA, which requires more than 15 s. The developed platform can produce 40 OCT images per second, to facilitate real-time mobile OCT image visualization. We believe this study would facilitate the development of portable diagnostic image visualization with medical imaging modality, which requires computationally expensive procedures, using a mobile processor.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.259-259
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• 1994

Protein kinase C (PKC) participates in many cellular signal transduction. Previously we found that PKC activity of whole rat brain was altered after an exposure to cold temperature of 4 $^{\circ}C$ (Lee and Choi, Exp. Neurobiol., 2, 6, 1993). In this time PKC activity in each region of rat brain was investigated in order to know each regions is affected mostly by the stress.

• Parasites, Hosts and Diseases
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• v.47 no.1
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• pp.53-56
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• 2009

The authors report here a rare case of cerebellar schistosomiasis identified by pathological diagnosis, lacking extracranial involvement. The clinical symptoms included headache, dizziness, and nausea. Studies in blood were normal and no parasite eggs were detected in stool. Computed tomography of brains showed hypodense signal, and magnetic resonance imaging showed isointense signal on T1-weighted images, hyperintense signal on T2-weighted images, and intensely enhancing nodules in the right cerebellum after intravenous administration of gadolinium. A high-grade glioma was suspected, and an operation was performed. The pathologic examination of the biopsy specimen revealed schistosomal granulomas scattered within the parenchyma of the cerebellum. The definitive diagnosis was cerebellar schistosomiasis japonica. A standard use of praziquantel and corticosteroid drugs was applied, and the prognosis was good. When the pattern of imaging examinations is present as mentioned above, a diagnosis of brain schistosomiasis should be considered.

• Journal of radiological science and technology
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• v.23 no.1
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• pp.39-47
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• 2000

In this study, we showed a comparison and analysis making use of DWI(diffusion weighted image) using early diagnosis of cerebral Infarction and with the classified T2 weighted image, FLAIR images signal intensity for brain infarction period. period of cerebral infarction after the condition of a disease by ischemic stroke. To compare 3 types of image, we performed polynomial warping and affined transform for image matching. Using proposed algorithm, calculated signal intensity difference between T2WI, DWI, FLAIR and DWI. The quantification values between hand made and calculated data are almost the same. We quantified the each period and performed pseudo color mapping by comparing signal intensity each other according to previously obtained hand made data, and compared the result of this paper according to obtained quantified data to that of doctors decision. The examined mean and standard deviation for each brain infarction stage are as follows ; the means and standard deviations of signal intensity difference between DWI and T2WI for each period are $197.7{\pm}6.9$ in hyperacute, $110.2{\pm}5.4$ in acute, and $67.8{\pm}7.2$ in subacute. And the means and standard deviations of signal intensity difference between DWI and FLAIR for each period are $199.8{\pm}7.5$ in hyperacute, $115.3{\pm}8.0$ in acute, and $70.9{\pm}5.8$ in subacute. We can quantificate and decide cerebral infarction period objectively. According to this study, DWI is very exact for early diagnosis. We classified the period of infarction occurrence to analyze the region of disease and normal region in DW, T2WI, FLAIR images.

• Journal of IKEEE
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• v.23 no.2
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• pp.727-734
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• 2019

This paper confirms whether the movement or specific operation of the muscles in the process of transferring a person from the brain can find a signal showing an essential feature of a certain part of the brain. As a rule, the occurrence of EEG(Electroencephalogram) changes when a signal is received from a specific action or from an induced action. These signals are very vague and difficult to distinguish from the naked eye. Therefore, it is necessary to define a signal for analysis before classification. The EEG form can be divided into the alpha, beta, delta, theta and gamma regions in the frequency ranges. The specific size of these signals does not reflect the exact behavior or intention, since the band or energy difference of the activated frequencies varies depending on the EEG measurement domain. However, if different actions are performed in a specific method, it is possible to classify the movement based on EEG activity and to determine the EEG tendency affecting the movement. Therefore, in this article, we first study the EEG expression pattern based on the activation of the left and right biceps EMG, and then we determine whether there is a significant difference between the EEG due to the activation of the left and right muscles through EEG. If we can find the EEG classification criteria in accordance with the EMG activation, it can help to understand the form of the transmitted signal in the process of transmitting signals from the brain to each muscle. In addition, we can use a lot of unknown EEG information through more complex types of brain signal generation in the future.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Biomolecules & Therapeutics
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• v.32 no.3
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• pp.309-318
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• 2024

Compared to other organs, the brain has limited antioxidant defenses. In particular, the hippocampus is the central region for learning and memory and is highly susceptible to oxidative stress. Glial cells are the most abundant cells in the brain, and sustained glial cell activation is critical to the neuroinflammation that aggravates neuropathology and neurotoxicity. Therefore, regulating glial cell activation is a promising neurotherapeutic treatment. Quinic acid (QA) and its derivatives possess anti-oxidant and anti-inflammatory properties. Although previous studies have evidenced QA's benefit on the brain, in vivo and in vitro analyses of its anti-oxidant and anti-inflammatory properties in glial cells have yet to be established. This study investigated QA's rescue effect in lipopolysaccharide (LPS)-induced behavior impairment. Orally administering QA restored social impairment and LPS-induced spatial and fear memory. In addition, QA inhibited proinflammatory mediator, oxidative stress marker, and mitogen-activated protein kinase (MAPK) activation in the LPS-injected hippocampus. QA inhibited nitrite release and extracellular signal-regulated kinase (ERK) phosphorylation in LPS-stimulated astrocytes. Collectively, QA restored impaired neuroinflammation-induced behavior by regulating proinflammatory mediator and ERK activation in astrocytes, demonstrating its potential as a therapeutic agent for neuroinflammation-induced brain disease treatments.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.