• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.2
• /
• pp.47-62
• /
• 2016

An integrated database(DB) system was developed to manage the marine geological data and geophysical data acquired from around the Korean peninsula from 2009 to 2013. Geological data such as size analysis data, columnar section images, X-ray images, heavy metal data, and organic carbon data of sediment samples, were collected in the form of text files, excel files, PDF files and image files. Geophysical data such as seismic data, magnetic data, and gravity data were gathered in the form of SEG-Y binary files, image files and text files. We collected scientific data from research projects funded by the Ministry of Oceans and Fisheries, data produced by domestic marine organizations, and public data provided by foreign organizations. All the collected data were validated manually and stored in the archive DB according to data processing procedures. A geographic information system was developed to manage the spatial information and provide data effectively using the map interface. Geographic information system(GIS) software was used to import the position data from text files, manipulate spatial data, and produce shape files. A GIS DB was set up using the Oracle database system and ArcGIS spatial data engine. A client/server GIS application was developed to support data search, data provision, and visualization of scientific data. It provided complex search functions and on-the-fly visualization using ChartFX and specially developed programs. The system is currently being maintained and newly collected data is added to the DB system every year.

• Journal of the Korean Society of Radiology
• /
• v.81 no.3
• /
• pp.632-643
• /
• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2480-2496
• /
• 2020

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.

• Journal of Korean Society of Archives and Records Management
• /
• v.20 no.2
• /
• pp.67-83
• /
• 2020

Many local governments in Korea provide online services for people to easily access the audio-visual archives of events occurring in the area. However, the current method of managing these archives of the local governments has several problems in terms of compatibility with other organizations and convenience for searching of the archives because of the lack of standard metadata and the low utilization of image information. To solve these problems, we propose the metadata design and machine learning-based automatic indexing technology for the efficient management of the image archives of local governments in Korea. Moreover, we design metadata items specialized for the image archives of local governments to improve the compatibility and include the elements that can represent the basic information and characteristics of images into the metadata items, enabling efficient management. In addition, the text and objects in images, which include pieces of information that reflect events and categories, are automatically indexed based on the machine learning technology, enhancing users' search convenience. Lastly, we developed the program that automatically extracts text and objects from image archives using the proposed method, and stores the extracted contents and basic information in the metadata items we designed.

• Journal of KIISE:Software and Applications
• /
• v.28 no.5
• /
• pp.389-401
• /
• 2001

This paper proposes an abstraction mechanism of the low-level digital video features for the automatic retrievals of the explosion scenes from the digital video library. In the proposed abstraction mechanism, the regional dominant colors of the key frame and the motion energy of the shot are defined as the primary abstractions of the shot for the explosion scene retrievals. It is because an explosion shot usually consists of the frames with a yellow-tone pixel and the objects in the shot are moved rapidly. The regional dominant colors of shot are selected by dividing its key frame image into several regions and extracting their regional dominant colors, and the motion energy of the shot is defined as the edge image differences between key frame and its neighboring frame. The edge image of the key frame makes the retrieval of the explosion scene more precisely, because the flames usually veils all other objects in the shot so that the edge image of the key frame comes to be simple enough in the explosion shot. The proposed automatic retrieval algorithm declares an explosion scene if it has a shot with a yellow regional dominant color and its motion energy is several times higher than the average motion energy of the shots in that scene. The edge image of the key frame is also used to filter out the false detection. Upon the extensive exporimental results, we could argue that the recall and precision of the proposed abstraction and detecting algorithm are about 0.8, and also found that they are not sensitive to the thresholds. This abstraction mechanism could be used to summarize the long action videos, and extract a high level semantic information from digital video archive.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.13 no.3
• /
• pp.102-109
• /
• 2009

Purpose: Flash 3D (pixon(R) method; 3D OSEM) was developed as a software program to shorten exam time and improve image quality through reconstruction, it is an image processing method that usefully be applied to nuclear medicine tomography. If perfoming brain diamox perfusion scan by reconstructing subtracted images by Flash 3D with shortened image acquisition time, there was a problem that SNR of subtracted image is lower than basal image. To increase SNR of subtracted image, we use LEAP collimators, and we emphasized on sensitivity of vessel dilatation than resolution of brain vessel. In this study, our purpose is to confirm possibility of application of LEAP collimators at brain diamox perfusion tomography, identify proper reconstruction factors by using Flash 3D. Materials and methods: (1) The evaluation of phantom: We used Hoffman 3D Brain Phantom with $^{99m}Tc$. We obtained images by LEAP and LEHR collimators (diamox image) and after 6 hours (the half life of $^{99m}Tc$: 6 hours), we use obtained second image (basal image) by same method. Also, we acquired SNR and ratio of white matters/gray matters of each basal image and subtracted image. (2) The evaluation of patient's image: We quantitatively analyzed patients who were examined by LEAP collimators then was classified as a normal group and who were examined by LEHR collimators then was classified as a normal group from 2008. 05 to 2009. 01. We evaluate the results from phantom by substituting factors. We used one-day protocol and injected $^{99m}Tc$-ECD 925 MBq at both basal image acquisition and diamox image acquisition. Results: (1) The evaluation of phantom: After measuring counts from each detector, at basal image 41~46 kcount, stress image 79~90 kcount, subtraction image 40~47 kcount were detected. LEAP was about 102~113 kcount at basal image, 188~210 kcount at stress image and 94~103 at subtraction image kcount were detected. The SNR of LEHR subtraction image was decreased than LEHR basal image about 37%, the SNR of LEAP subtraction image was decreased than LEAP basal image about 17%. The ratio of gray matter versus white matter is 2.2:1 at LEHR basal image and 1.9:1 at subtraction, and at LEAP basal image was 2.4:1 and subtraction image was 2:1. (2) The evaluation of patient's image: the counts acquired by LEHR collimators are about 40~60 kcounts at basal image, and 80~100 kcount at stress image. It was proper to set FWHM as 7 mm at basal and stress image and 11mm at subtraction image. LEAP was about 80~100 kcount at basal image and 180~200 kcount at stress image. LEAP images could reduce blurring by setting FWHM as 5 mm at basal and stress images and 7 mm at subtraction image. At basal and stress image, LEHR image was superior than LEAP image. But in case of subtraction image like a phantom experiment, it showed rough image because SNR of LEHR image was decreased. On the other hand, in case of subtraction LEAP image was better than LEHR image in SNR and sensitivity. In all LEHR and LEAP collimator images, proper subset and iteration frequency was 8 times. Conclusions: We could archive more clear and high SNR subtraction image by using proper filter with LEAP collimator. In case of applying one day protocol and reconstructing by Flash 3D, we could consider application of LEAP collimator to acquire better subtraction image.

• Weed & Turfgrass Science
• /
• v.6 no.1
• /
• pp.55-60
• /
• 2017

Shade affects turf quality by reducing light for photosynthesis. The shade tolerance of the tropical grasses, Digitaria longiflora and Axonopus compressus were evaluated against Stenotaphrum secundatum (St. Augustinegrass). The grasses were established under shade structures that provide 0%, 50%, 75% or 90% shade level for 30 days. A suite of leaf traits, recorded from similar leaf developmental stage, displayed distinct responses to shade conditions. Leaf length, relative to control, increased in all three species as shade level increased. The mean leaf extension rate was lowest in St. Augustinegrass (80.42%) followed by A. compressus (84.62%) and D. longiflora (90.78%). The higher leaf extension rate in D. longiflora implied its poor shade tolerance. Specific leaf area (SLA) increased in all species with highest mean SLA increase in D. longiflora ($348.55cm^2mg^{-1}$)followed by A. compressus ($286.88cm^2mg^{-1}$) and St. Augustinegrass ($276.28cm^2mg^{-1}$). The highest SLA increase in D. longiflora suggested its lowest performance under shade. The percent green cover, as estimated by digital image analysis, was lowest in D. longiflora (53%) under 90% shade level compared to both species. The relative shade tolerance of the three turfgrasses could be ranked as St. Augustinegrass > A. compressus > D. longiflora.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.1
• /
• pp.79.1-79.1
• /
• 2012

We present here $UBVI$ and H${\alpha}$ photometric results of stellar sources in the bubble nebula NGC 7635. The early type members are selected from the photometric membership criteria. H${\alpha}$ photometry allows us to detect 11 pre-main sequence candidates with H${\alpha}$emission. In addition, we performed PSF photometry for the Spitzer IRAC and MIPS 24${\mu}m$ images from archive (program ID 20726, PI: J. Hester) in order to search for the young stellar objects (YSOs). Total 19 sources are classified as YSOs (7 class I, 11 class II, and 1 transitional disk candidates) in the color-color diagrams according to the classification scheme of Gutermuth et al.. Among them, 7 YSOs have counterparts in optical photometric data. These stars can be divided into two groups at given color indices. It implies that there occurred the star formation events more than twice. We would like to discuss the star formation history in the bubble nebula using the results from SED fitter (Robitaille et al.), color composite image from IRAC bands, and spatial distribution of early type stars and YSOs.

• Weed & Turfgrass Science
• /
• v.5 no.2
• /
• pp.88-94
• /
• 2016

Vehicular traffic on turf results in loss of green cover due to direct tearing of shoots and indirect long-term soil compaction. Protection of turfgrass crowns from wear could increase the ability of turf to recover from heavy traffic. Plastic turfpavers have been installed in trafficked areas to reduce soil compaction and to protect turfgrass crowns from wear. The objectives of this study were to evaluate traffic performance of turfgrasses (Zoysia matrella and Axonopus compressus) and soil mixture (high, medium and low sand mix) combinations on turf-paver complex. The traffic performance of turf and recovery was evaluated based on percent green cover determined by digital image analysis and spectral reflectance responses by NDVI-meter. Bulk density cores indicated significant increase in soil compaction from medium and low sand mixtures compared to high sand mixture. Higher reduction of percent green cover was observed from A. compressus (30-40%) than Z. matrella (10-20%) across soil mixtures. Both turf species displayed higher wear tolerance when established on higher sand (>50% sand) than low sand mixture. Positive turf recovery was also supported by complementary spectral responses. Establishment of Zoysia matrella turf on turfpaver complex using high sand mixture will result in improved wear tolerance.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.1-11
• /
• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.