• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.351-364
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• 2002

Conventional 3D ultrasound imaging using mechanical ID arrays suffers from poor elevation resolution due to the limited depth-of-focus (DOF). On the other hand, 3D imaging systems using 2D phased arrays have a large number of active channels and hence require a very expensive and bulky beamforming hardware. To overcome these limitations, a new real-time volumetric imaging method using curved 2-D arrays is presented, in which a small subaperture, consisting of 256 elements, moves across the array surface to scan a volume of interest. For this purpose, a 2-D curved array is designed which consists of 90$\times$46 elements with 1.5λ inter-element spacing and has the same view angles along both the lateral and elevation directions as those of a commercial mechanical 1-D array. In the proposed method, transmit and receive subapertures are constructed by cutting the four corners of a rectangular aperture to obtain a required image qualify with a small number of active channels. In addition the receive subaperture size is increased by using a sparse array scheme that uses every other elements in both directions. To suppress the grating lobes elevated due to the increase in clement spacing, fold-over array scheme is adopted in transmit, which doubles the effective size of a transmit aperture in each direction. Computer simulation results show that the proposed method can provide almost the same and greatly improved resolutions in the lateral and elevation directions, respectively compared with the conventional 3D imaging with a mechanical 1-D array.

• Progress in Medical Physics
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• v.16 no.4
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• pp.183-191
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• 2005

The utilization of PET has been increased so fast since the usefulness of the PET has been proved in various clinical and research fields. Among the many applications, the PET Is especially useful in oncology and most of the clinical PET scans are peformed for the oncologic examination Including the different diagnosis of malignant and benign tumors and assessment of the treatment effects and recurrent tumors. As the PET-CT scanners are widely available, there is Increasing interest in the application of the PET Images to the radiation treatment planning. Although the CT images are conventionally used for the target volume determination in the radiation treatment planning, there are fundamental limitation In use of only the anatomical information. Therefore, the volume determination of the functionally active tumor region using the PET would be important for the treatment planning. However, the accurate determination of the tumor boundary is not simple in PET due to the relatively low spatial resolution of the currently available PET scanners. In this study, computer simulations were peformed to study the relationship between the lesion size, PET resolution, lesion to background ratio and the threshold of Image Intensity to determine the true tumor volume.

• Progress in Medical Physics
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• v.7 no.1
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• pp.3-7
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• 1996

The accuracy in target localization of CT, MR, and digital angiography were investigated for stereotactic radiosurgery. The images using CT and MR were obtained out of geometrical phantom which was designed to produce exact coordinates of several points within a 0.lmm error range. The slice interval was 3mm and FOV was 35cm for CT and 28cm for MR. These images were transferred to treatment planning computer using TCP/IP in forms of GE format. Measured 3-D coordinates of these images from planning computer were compared to known values by geometrical phantom. Anterior-posterior and lateral films were taken by digital angiography for measurement of spatial accuracy. Target localization errors were 1.2${\pm}$0.5mm with CT images, 1.7${\pm}$0.4mm with MR-coronal images, and 2.1${\pm}$0.7mm with MR-sagittal images. But, in case of MR-axial images, the target localization error was 4.7${\pm}$0.9mm. Finally, the target localization error of digital angiography was 0.9${\pm}$0.4mm. The accuracy of diagnostic machines such as CT, MR, and angiography depended on their resolutions and distortions. The target localization error mainly depended on the resolution due to slice interval with CT and the image distortion as well as the resolution with MR However, in case of digital angiography, the target localization error was closely related to the distortion of fiducial markers. The results of our study should be considered when PTV (Planning Target Volume) was determined.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1085-1095
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• 2019

In this paper, we perform a subjective quality assessment on low-resolution surveillance videos, which are encoded with a very low target bit-rate to use in an ultra-low band transmission system and investigate the encoding effects on the perceived video quality. The test videos are collected based on their spatial and temporal characteristics which affect the perceived quality. H.265/HEVC encoder is used to prepare the impaired sequences for three target bit-rates 20, 45, and 65 kbps and subjective quality assessment is conducted to evaluate the quality from a viewing distance of 3H. The experimental results show that the quality of encoded videos, even at target bit-rate of 45 kbps can satisfy the users. Also we compare objective image/video quality assessment methods on the proposed dataset to measure their correlation with subjective scores. The experimental results show that the existing methods poorly performed, that indicates the need for a better quality assessment method.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1383-1398
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• 2018

Sea surface wind is one of the most fundamental variables for understanding diverse marine phenomena. Although scatterometers have produced global wind field data since the early 1990's, the data has been used limitedly in oceanic applications due to it slow spatial resolution, especially at coastal regions. Synthetic Aperture Radar (SAR) is capable to produce high resolution wind field data. KOMPSAT-5 is the first Korean satellite equipped with X-band SAR instrument and is able to retrieve the sea surface wind. This study presents the validation results of sea surface wind derived from the KOMPSAT-5 backscattering coefficient data for the first time. We collected 18 KOMPSAT-5 ES mode data to produce a matchup database collocated with buoy stations. In order to calculate the accurate wind speed, we preprocessed the SAR data, including land masking, speckle noise reduction, and ship detection, and converted the in-situ wind to 10-m neutral wind as reference wind data using Liu-Katsaros-Businger (LKB) model. The sea surface winds based on XMOD2 show root-mean-square errors of about $2.41-2.74m\;s^{-1}$ depending on backscattering coefficient conversion equations. In-depth analyses on the wind speed errors derived from KOMPSAT-5 backscattering coefficient data reveal the existence of diverse potential error factors such as image quality related to range ambiguity, discrete and discontinuous distribution of incidence angle, change in marine atmospheric environment, impacts on atmospheric gravity waves, ocean wave spectrum, and internal wave.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Korea Water Resources Association
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• v.47 no.6
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• pp.537-546
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• 2014

Surface image velocimetry was introduced as an efficient and sage alternative to conventional river flow measurement methods during floods. The conventional surface image velocimetry uses a pair of images to estimate velocity fields using cross-correlation analysis. This method is appropriate to analyzing images taken with a short time interval. It, however, has some drawbacks; it takes a while to analyze images for the verage velocity of long time intervals and is prone to include errors or uncertainties due to flow characteristics and/or image taking conditions. Methods using spatio-temporal images, called STIV, were developed to overcome the drawbacks of conventional surface image velocimetry. The grayscale-gradient tensor method, one of various STIVs, has shown to be effectively reducing the analysis time and is fairly insusceptible to any measurement noise. It, unfortunately, can only be applied to the main flow direction. This means that it can not measure any two-dimensional flow field, e.g. flow in the vicinity of river structures and flow around river bends. The present study aimed to develop a new method of analyzing spatio-temporal images in two-dimension using cross-correlation analysis. Unlike the conventional STIV, the developed method can be used to measure two-dimensional flow substantially. The method also has very high spatial resolution and reduces the analysis time. A verification test using artificial images with lid-driven cavity flow showed that the maximum error of the method is less than 10 % and the average error is less than 5 %. This means that the developed scheme seems to be fairly accurate, even for two-dimensional flow.

• Korean Journal of Environment and Ecology
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• v.34 no.1
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• pp.42-54
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• 2020

This study aimed to analyze the rate of increase and spread patterns of dead trees of the conifer (Abies koreana and others) in subalpine zones by using the high-resolution aerial images in Jirisan National Park around 10 years ago. Furthermore, factors affecting the death of conifer were identified by analyzing the altitude, topographical information, solar radiation, and moisture environment of the site where the dead trees are located. The number of dead trees per unit area increased by two to five times in the Banyabong peak, Yeongsinbong peak, and Cheonwangbong peak in Jirisan National Park over the past decade. The increase was about 2 times in the Banyabong peak, about 3.9 times in the Yeongsinbong peak, and about 5.2 times in the Cheonwangbong peak, indicating the most notable increase in the Cheonwangbong peak. It is estimated that dead trees commonly occurred in the environments where the soil moisture content was low due to the high slope, amount of evaporation was high due to strong solar radiation as the location faced south, and the soil was dry due to strong solar radiation and short rain retention time. In other words, dead conifer trees in subalpine zones were concentrated in dry location environments, and the tendency was the same more than ten years ago.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.227-236
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• 2016

Dynamic positron emission tomography(dPET) is widely used medical imaging modality that can provide both physiological and functional neuro-image for diagnosing various brain disease. However, dPET images have low spatial-resolution and high noise level during spatio-temporal analysis (three-dimensional spatial information + one-dimensional time information), there by limiting clinical utilization. In order to overcome these issues for the spatio-temporal analysis, a novel computational technique was introduced in this paper. The computational technique based on singular value decomposition classifies multiple independent components. Signal components can be distinguished from the classified independent components. The results show that signal to noise ratio was improved up to 30% compared with the original images. We believe that the proposed computational technique in dPET can be useful tool for various clinical / research applications.