• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.81-87
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• 2012

The current defacing method for keeping an anonymity of brain images damages the integrity of a precise brain analysis due to over removal, although it maintains the patients' privacy. A novel method has been developed to create an anonymous face model while keeping the voxel values of an image exactly the same as that of the original one. The method contains two steps: construction of a mockup brain template from ten normalized brain images and a substitution of the mockup brain to the brain image. A level set segmentation algorithm is applied to segment a scalp-skull apart from the whole brain volume. The segmented mockup brain is coregistered and normalized to the subject brain image to create an anonymous face model. The validity of this modification is tested through comparing the intensity of voxels inside a brain area from the mockup brain with the original brain image. The result shows that the intensity of voxels inside from the mockup brain is same as ones from an original brain image, while its anonymity is guaranteed.

• Journal of electromagnetic engineering and science
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• v.6 no.1
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• pp.47-52
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• 2006

The objective of this study is to evaluate the effects of size and permittivity on the specific absorption rate(SAR) values of rat brains during microwave exposure at mobile phone frequency bands. A finite difference time domain (FDTD) technique with perfect matching layer(PML) absorbing boundaries is used for this evaluation process. A color coded digital image of the Sprague Dawley(SD) rat based on magnetic resonance imaging(MRI) is used in FDTD calculation with appropriate permittivity values corresponding to different tissues for 3, 4, 7, and 10 week old rats. This study is comprised of three major parts. First, the rat model structure is scaled uniformly, i.e., the rat size is increased without change in permittivity. The simulated SAR values are compared with other experimental and numerical results. Second, the effect of permittivity on SAR values is examined by simulating the microwave exposure on rat brains with various permittivity values for a fixed rat size. Finally, the SAR distributions in depth, and the brain-averaged SAR and brain 1 voxel peak SAR values are computed during the microwave exposure on a rat model structure when both size and permittivity have varied corresponding to different ages ranging from 3 to 10 weeks. At 900 MHz, the simulation results show that the brain-averaged SAR values decreased by about 54 % for size variation from the 3 week to the 10 week-old rat model, while the SAR values decreased only by about 16 % for permittivity variation. It is found that the brain averaged SAR values decreased by about 63 % when the variations in size and permittivity are taken together. At 1,800 MHz, the brain-averaged SAR value is decreased by 200 % for size variation, 9.7 % for permittivity variation, and 207 % for both size and permittivity variations.

• Investigative Magnetic Resonance Imaging
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• v.23 no.1
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• pp.38-45
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• 2019

Purpose: To demonstrate the high-resolution numerical simulation of the respiration-induced dynamic $B_0$ shift in the head using generalized susceptibility voxel convolution (gSVC). Materials and Methods: Previous dynamic $B_0$ simulation research has been limited to low-resolution numerical models due to the large computational demands of conventional Fourier-based $B_0$ calculation methods. Here, we show that a recently-proposed gSVC method can simulate dynamic $B_0$ maps from a realistic breathing human body model with high spatiotemporal resolution in a time-efficient manner. For a human body model, we used the Extended Cardiac And Torso (XCAT) phantom originally developed for computed tomography. The spatial resolution (voxel size) was kept isotropic and varied from 1 to 10 mm. We calculated $B_0$ maps in the brain of the model at 10 equally spaced points in a respiration cycle and analyzed the spatial gradients of each of them. The results were compared with experimental measurements in the literature. Results: The simulation predicted a maximum temporal variation of the $B_0$ shift in the brain of about 7 Hz at 7T. The magnitudes of the respiration-induced $B_0$ gradient in the x (right/left), y (anterior/posterior), and z (head/feet) directions determined by volumetric linear fitting, were < 0.01 Hz/cm, 0.18 Hz/cm, and 0.26 Hz/cm, respectively. These compared favorably with previous reports. We found that simulation voxel sizes greater than 5 mm can produce unreliable results. Conclusion: We have presented an efficient simulation framework for respiration-induced $B_0$ variation in the head. The method can be used to predict $B_0$ shifts with high spatiotemporal resolution under different breathing conditions and aid in the design of dynamic $B_0$ compensation strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.297-299
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• 2022

Malignant glioma has a poor prognosis with the reported median survival of between 6 months to 14 months. Thus, it is crucial to predict the accurate survival of patients with malignant glioma. In this paper, we propose a convolutional neural network to predict the overall survival and age of the patients. A total of four MRI modalities, T1, T1-contrast enhanced, T2, and fluid-attenuated inversion recovery, which effectively capture spatial characteristics of malignant glioma, were used as input images. Age is an important factor impacting the overall survival, thus incorporating it into the model will thereby improve the performance of the proposed model. Our model successfully predicted overall survival and age of the patients with pearson correlation coefficients of 0.1748 and 0.3056, respectively.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.39-50
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• 2019

This study aims to assess the influence of climate change on the hydrological cycle at a basin level in North Korea. The selected model for this study is MRI-CGCM 3, the one used for the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover, this study adopted the Spatial Disaggregation-Quantile Delta Mapping (SDQDM), which is one of the stochastic downscaling techniques, to conduct the bias correction for climate change scenarios. The comparison between the preapplication and postapplication of the SDQDM supported the study's review on the technique's validity. In addition, as this study determined the influence of climate change on the hydrological cycle, it also observed the runoff in North Korea. In predicting such influence, parameters of a runoff model used for the analysis should be optimized. However, North Korea is classified as an ungauged region for its political characteristics, and it was difficult to collect the country's runoff observation data. Hence, the study selected 16 basins with secured high-quality runoff data, and the M-RAT model's optimized parameters were calculated. The study also analyzed the correlation among variables for basin characteristics to consider multicollinearity. Then, based on a phased regression analysis, the study developed an equation to calculate parameters for ungauged basin areas. To verify the equation, the study assumed the Osipcheon River, Namdaecheon Stream, Yongdang Reservoir, and Yonggang Stream as ungauged basin areas and conducted cross-validation. As a result, for all the four basin areas, high efficiency was confirmed with the efficiency coefficients of 0.8 or higher. The study used climate change scenarios and parameters of the estimated runoff model to assess the changes in hydrological cycle processes at a basin level from climate change in the Amnokgang River of North Korea. The results showed that climate change would lead to an increase in precipitation, and the corresponding rise in temperature is predicted to cause elevating evapotranspiration. However, it was found that the storage capacity in the basin decreased. The result of the analysis on flow duration indicated a decrease in flow on the 95th day; an increase in the drought flow during the periods of Future 1 and Future 2; and an increase in both flows for the period of Future 3.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.385-388
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• 2011

Patients' privacy protection is a heated issue in medical business, as medical information in digital format transmit everywhere through networks without any limitation. A current protection method for brain images is to deface from the brain image for patient's privacy. However, the defacing process often removes important brain voxels so that the defaced brain image is damaged for medical analysis. An ad-hoc method is proposed to conceal patient's identification by adding cylindrical mask, while the brain keep all important brain voxels. The proposed lossless deformation of brain image is verified not to loose any important voxels. Futhermore, the masked brain image is proved not to be recognized by others.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.896-902
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• 2005

An orthopedic surgeon normally gets the operational parameters of total knee arthroplasty from medical images(CT, MRI). Anatomical axis, mechanical axis, the width and height of femur, or tibia are the most important parameters related with accomplishment of TKA. This paper presents a methodology of simulation that virtually operates TKA according to 2D medical images. Using this simulator, some important parameters for operation can be achieved before hand. The simulator provides the 3D computational model of a knee joint and then derives the proper size of implant corresponding to the joint. The whole process of TKA can be simulated such as clipping a knee joint, assembling the joint and its implants, visualizing all the operation steps, deriving some crucial parameters such as anatomical axis and cutting thickness, and predicting the result of TKA. Some examples are given and discussed to validate the methodology.

• Journal of Biomedical Engineering Research
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• v.23 no.2
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• pp.101-107
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• 2002

This paper presents a segmentation algorithm to extract endocardial contour and epicardial contour of left ventricle in MR Cardiac images. The algorithm is based on a generalized gradient vector flow(GGVF) snake and a prediction of initial contour(PIC). Especially. the proposed algorithm uses physical characteristics of endocardial and epicardial contours, cross profile correlation matching(CPCM), and a mixed interpolation model. In the experiment, the proposed method is applied to short axis MR cardiac image set, which are obtained by Siemens, Medinus, and GE MRI Systems. The experimental results show that the proposed algorithm can extract acceptable epicardial and endocardial walls. We calculate quantitative parameters from the segmented results, which are displayed graphically. The segmented left vents role is visualized volumetrically by surface rendering. The proposed algorithm is implemented on Windows environment using Visual C ++.

• Journal of the Optical Society of Korea
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• v.19 no.2
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• pp.175-181
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• 2015

Optical coherence tomography (OCT) is a noninvasive technique for microscopic investigation of tissue. We thought that the OCT method could be a potential tool for monitoring the healing process of a tendon. In this study we used two rat models, denervated and non-denervated groups, to observe a variety of healing phases of Achilles tendon (AT) injury. We made samples of AT injury lesions, to take OCT images and to make histopathological samples of serial sectional tissue. In an OCT image the denervated rat showed no specific finding, but the non-denervated rat showed a large defect lesion that was scaffolding tissue. OCT findings combined with pathologic findings showed advantages in visualization of tendon microstructure over other imaging modalities such as MRI and US, and OCT is beneficial to making a treatment plan, especially the timing and intensity of rehabilitation. Therefore a multimodal platform using OCT for evaluation of tendon injury may be potentially useful for many applications.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.