• KOREAN JOURNAL OF CROP SCIENCE
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• v.31 no.2
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• pp.249-252
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• 1986

Saponins of Gynostemma pentaphyllum that collected from Geochang region(Korea) and Dokujima(Japan) were extracted by the method for ginseng saponin. Comparison by retention time in chromatogram(HPLC) of G. pentaphyllum to that of ginseng showed that it is hardly to find out a common saponin between penta-phyllum and ginseng saponins. Saponin content extracted from G. pentaphyllum growing in Geochang region was higher than that growing in Dokujima region.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.803-808
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• 2002

This paper describes automatic methods for detection of kidney and kidney tumor on abdominal CT scans. The abdominal CT images were digitalized using a film digitizer and a gray-level threshold method was used to segment the kidney. Based on texture analysis results, which were perform on sample images of kidney tumors, SEED region of kidney tumor was selected as result of homogeneity test. The average and standard deviation, which are representative statistical moments, were used to as an acceptance criteria for homogeneous test. Region growing method was used to segment the kidney tumor from the center pixel of selected SEED region using a gray-level value as an acceptance criteria for homogeneity test. These method were applied to 113 images of 9 cases, which were scanned by GE Hispeed Advantage CT scanner and digitalized by Lumisvs LS-40 film digitizer. The sensitivity was 85% and there was no false-positive results.

• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.239-246
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• 2000

In this paper. we propose a semi-automatic segmentation algorithm to extract organ in 3D medical data by using a manually segmentation result in a sing1e slice. Generally region glowing based tracking method consists of 3 steps object projection. seed extraction and boundary decision by region growing. But because the boundary between organs in medical data is vague, improper seeds make the boundary dig into the organ or extend to the false region. In the proposed algorithm seeds are carefully extracted to find suitable boundaries between organs after region growing. And the jagged boundary at low gradient region after region growing is corrected by post-processing using Fourier descriptor. Also two-path tracking make it possible to catch up newly appeared areas. The proposed algorithm provides satisfactory results in segmenting 1 mm distance kidneys from X-rav CT body image set of 82 slices.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.2C
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• pp.178-183
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• 2008

The videotext brings important semantic clues into video content analysis. In this paper, we propose an automatic binarization method of text region using a stroke filter. Proposed text binarization method consists of stroke filtering, text color polarity determination, and local region growing. By using the responses of dark and bright stroke filters, we can determine color polarity of text region automatically. And the method is robust against complex background, because it considers stroke information of videotexts by using a stroke filter. The effectiveness of our method is verified by experiments on a challenging database.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.59-68
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• 1999

In this paper, we propose an algorithm to locate the corpus callosum automatically from midsagittal brain MR images using the statistical characteristics and shape information of the corpus callosum. In the proposed algorithm, we first extract regions satisfying the statistical characteristics of the corpus callosum and then find a region matching the shape information. In order to match the shape information, a new directed window region-growing algorithm is proposed instead of using conventional contour matching algorithms. Using the proposed algorithm, we adaptively relax the statistical requirement until we find a region matching the shape information. Experiments show promising results.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.56-65
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• 2004

In this paper, we proposed the detection algorithm of microcalcification based on region growing method with contrast and edge sharpness in digital X-ray mammographic images. We extracted the local maximum pixel and watershed regions by using watershed algorithm. Then, we used the mean slope between local maximum and neighborhood pixels to extract microcalcification candidate pixels among local maximum pixels. During increasing threshold value to grow microcalcification region, at the maximum threshold value of the contrast and edge sharpness, the microcalcification area is decided. The regions of which area of grown candidate microcalfication region is larger than that of watershed region are excluded from microcalcifications. We showed the diagnosis algorithm can be used to aid diagnostic-radiologist in the early detection breast cancer.

• Asian Journal of Business Environment
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• v.11 no.3
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• pp.21-32
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• 2021

Purpose: Research aims to analyze the influence of ICT on regional economic growth in Indonesia Provinces are grouped using Klassen's typology, which divides four quadrants based on economic growth and GRDP per capita. Considering similarity characteristics, four typology categories are finally simplified into two named categories: the "rapidly developed and growing" region and the "relatively lagged" region. Research design, data and methodology: The study uses panel data of 33 provinces in Indonesia from 2012 to 2018. It employs panel regression analysis to determine the impact of ICT on the regional economic growth of both regions. Results: The study reveals the percentage of households that own computer and the percentage of households who have accessed the internet in the last three months have a positive and significant influence on the GRDP per capita in the "relatively lagged" region. Meanwhile, mean years of schooling has a positive and significant impact on both regions. Conclusions: In the "rapidly developed and growing" region, only mean years of schooling has a positive and significant effect on GRDP per capita, whilst in the "relatively lagged" region, percentage of households that own computer, percentage of households who have accessed the internet in the last three months, and mean years of schooling have a positive and significant impact on GRDP per capita in Indonesia.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.139-142
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• 2001

This paper presents a highly fast and accurate facial region extraction method by using the skin-color-reference map and motion information. First, we construct the robust skin-color-reference map and eliminate the background in image by this map. Additionally, we use the motion information for accurate and fast detection of facial region in image sequences. Then we further apply region growing in the remaining areas with the aid of proposed criteria. The simulation results show the improvement in execution time and accurate detection.