Topsoils, mainly from crop fields, orchards, forests, and barns around the Pyeongchang River, were collected to investigate their heavy metal concentrations. Pollution load index, ecological risk index, and enrichment factor were applied to assess levels of heavy metal contamination for topsoils. The concentrations of cadmium (Cd) (1.7 mg/kg) and chromium (Cr) (4.1 mg/kg) exceeded the troublesome level in one site, whereas zinc (Zn) (396.7 to 711.1 mg/kg) and nickel (Ni) (40.1 to 95.3 mg/kg) in several topsoils exceeded the troublesome to countermeasure levels, according to soil contamination standards for the study areas. A significant risk of contamination was observed for mercury (Hg) by all indices, although the concentration in most of the topsoils was below the guideline. As expected, a positive linear correlation was observed for the values of pollution load index and ecological risk index, demonstrating lower heavy metal contamination in upstream areas compared to those downstream. High to extremely high ecological risk was observed in several samples for Zn and Ni, while all of the soils were unpolluted to slightly polluted, according to the pollution load index. A baseline study was not performed earlier for these sites, so these assessed values of heavy metals should be used as reference values for further assessment.
In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was 40×40 pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.
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