• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.64-72
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• 2009

The method to generate a digital elevation model(DEM) from contour lines causes a problem in which the low relief landform cannot be clearly presented due to the fact that it is significantly influenced by the expression of micro landform elements according to the interval of contours. Thus, this study attempts to develop a landcover burning method that recovers the micro relief landform of the DEM, which applies buffering and map algebra methods by inputting the elevation information to the landcover. In the recovering process of the micro landform, the DEM was recovered using the buffering method and elevation information through the map algebra for the landcover element for the micro landform among the primary DEM generation, making landcover map, and landcover elements. The recovering of the micro landform was applied based on stream landforms. The recovering of landforms using the buffering method was performed for the bar, which is a polygonal element, and wetland according to the properties of concave/convex through generating contours with a uniform interval in which the elevation information applied to the recovered landform. In the case of the linear elements, such as bank, road, waterway, and tributary, the landform can be recovered by using the elevation information through applying a map algebra function. Because the polygonal elements, such as stream channel, river terrace, and artificial objects (farmlands) are determined as a flat property, these are recovered by inputting constant elevation values. The results of this study were compared and analyzed for the degree of landform expression between the original DEM and the recovered DEM. In the results of the analysis, the DEM produced by using the conventional method showed few expressions in micro landform elements. The method developed in this study well described wetland, bar, landform around rivers, farmland, bank, river terrace, and artificial objects. It can be expected that the results of this study contribute to the classification and analysis of micro landforms, plain and the ecology and environment study that requires the recovering of micro landforms around streams and rivers.

• Progress in Medical Physics
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• v.7 no.1
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• pp.65-77
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• 1996

Using the 2-D and 3-D Hoffman brain phantom, 3-D Jaszczak phantom and Single Photon Emission Computed Tomography, the effects of data acquisition parameter, attenuation, noise, scatter and reconstruction algorithm on image quantitation as well as image quality were studied. For the data acquisition parameters, the images were acquired by changing the increment angle of rotation and the radius. The less increment angle of rotation resulted in superior image quality. Smaller radius from the center of rotation gave better image quality, since the resolution degraded as increasing the distance from detector to object increased. Using the flood data in Jaszczak phantom, the optimal attenuation coefficients were derived as 0.12cm$\^$-1/ for all collimators. Consequently, the all images were corrected for attenuation using the derived attenuation coefficients. It showed concave line profile without attenuation correction and flat line profile with attenuation correction in flood data obtained with jaszczak phantom. And the attenuation correction improved both image qulity and image quantitation. To study the effects of noise, the images were acquired for 1min, 2min, 5min, 10min, and 20min. The 20min image showed much better noise characteristics than 1min image indicating that increasing the counting time reduces the noise characteristics which follow the Poisson distribution. The images were also acquired using dual-energy windows, one for main photopeak and another one for scatter peak. The images were then compared with and without scatter correction. Scatter correction improved image quality so that the cold sphere and bar pattern in Jaszczak phantom were clearly visualized. Scatter correction was also applied to 3-D Hoffman brain phantom and resulted in better image quality. In conclusion, the SPECT images were significantly affected by the factors of data acquisition parameter, attenuation, noise, scatter, and reconstruction algorithm and these factors must be optimized or corrected to obtain the useful SPECT data in clinical applications.