This study was initiated to estimate productivity of Quercus variabilis stand. However the practical objective of this study was to provide some information to establish the basis of selecting the suitable site for Quercus variabilis. The productivity measured in terms of DBH, height, basal area and stem volume was hypothesized, respectively, to be a function of a group of factors. This study considered 32 factors, 20 of which were related to the forest environmental factors such as tree age, latitude, percent slope, etc. and the rest of which were related to soil factors such as soil moisture, total nitrogen, available $P_2O_5$, etc. The data on 4 productivity measurements of Quercus variabilis growth and related factors cited were collected from 99 sample plots in Kyeongbook and chungbook provinces. Some factors considered were, in nature, discrete variables and the others continuous variables. Each kind of factor was classified into 3 or 4 categories and total numbers of such categories were eventually amounted to 110. Then each category was treated as an independent variable. This is amounted to saying that individual variable was treated a dummy variable and assigned a value 1 or 0. However the first category of each factor was deleted from the normal equation for statistical consideration. First of all, each of 4 productivity measurements of Quercus variabilis growth was regressed and, at the same time, those 110 categories. Secondly, the partial correlation coefficients were measured between each pair of 4 productivity measurements and 32 individual foctors. Finally, the relative scores were estimated in order to derive the category ranges. The result of these statistical analyses could be summarized as follows: 1) Growth measurement in terms of height seems to be a more significant criterion for estimation of productivity of Quercus variabilis. 2) Productivity of forest on stocked land may better be estimated in terms of forest environmental factors, on the other hand, that of unstocked land may be estimated in terms of physio-chemical factors of soil. 3) The factors that a strongly positive relation to all growth factors of tree are age group, effective soil, soil moisture, etc. This implies that these factors might effectively be used for criteria for selecting the suitable site for Quercus variabilis. 4) Parent rock, latitude, total nitrogen, age group, effective soil depth, soil moisture, organic matter, etc., had more significant category range for tree growth. Therefore, the suitable site for Quercus variabilis may be selected, based on this information. In conclusion, the above results obtained by the multivariable analysis can be not only the important criteria for estimating the growth of Quercus variabilis but also the useful guidance for selecting the suitable sites and performing the rational of Quercus variabilis forest.
This paper analyzes U.S. demand for imports from Asian NIEs and Japan, utilizing the Almost Ideal Demand System (AIDS) developed by Deaton and Muellbauer, with an emphasis on the effect of changes in the exchange rate. The empirical model assumes a two-stage budgeting process in which the first stage represents the allocation of total U.S. demand among three groups: the Asian NIEs and Japan, six Western developed countries, and the U.S. domestic non-tradables and import competing sector. The second stage represents the allocation of total U.S. imports from the Asian NIEs and Japan among them, by country. According to the AIDS model, the share equation for the Asia NIEs and Japan in U.S. nominal GNP is estimated as a single equation for the first stage. The share equations for those five countries in total U.S. imports are estimated as a system with the general demand restrictions of homogeneity, symmetry and adding-up, together with polynomially distributed lag restrictions. The negativity condition is also satisfied for all cases. The overall results of these complicated estimations, using quarterly data from the first quarter of 1972 to the fourth quarter of 1989, are quite promising in terms of the significance of individual estimators and other statistics. The conclusions drawn from the estimation results and the derived demand elasticities can be summarized as follows: First, the exports of each Asian NIE to the U.S. are competitive with (substitutes for) Japan's exports, while complementary to the exports of fellow NIEs, with the exception of the competitive relation between Hong Kong and Singapore. Second, the exports of each Asian NIE and of Japan to the U.S. are competitive with those of Western developed countries' to the U.S, while they are complementary to the U.S.' non-tradables and import-competing sector. Third, as far as both the first and second stages of budgeting are coneidered, the imports from each Asian NIE and Japan are luxuries in total U.S. consumption. However, when only the second budgeting stage is considered, the imports from Japan and Singapore are luxuries in U.S. imports from the NIEs and Japan, while those of Korea, Taiwan and Hong Kong are necessities. Fourth, the above results may be evidenced more concretely in their implied exchange rate effects. It appears that, in general, a change in the yen-dollar exchange rate will have at least as great an impact, on an NIE's share and volume of exports to the U.S. though in the opposite direction, as a change in the exchange rate of the NIE's own currency $vis-{\grave{a}}-vis$ the dollar. Asian NIEs, therefore, should counteract yen-dollar movements in order to stabilize their exports to the U.S.. More specifically, Korea should depreciate the value of the won relative to the dollar by approximately the same proportion as the depreciation rate of the yen $vis-{\grave{a}}-vis$ the dollar, in order to maintain the volume of Korean exports to the U.S.. In the worst case scenario, Korea should devalue the won by three times the maguitude of the yen's depreciation rate, in order to keep market share in the aforementioned five countries' total exports to the U.S.. Finally, this study provides additional information which may support empirical findings on the competitive relations among the Asian NIEs and Japan. The correlation matrices among the strutures of those five countries' exports to the U.S.. during the 1970s and 1980s were estimated, with the export structure constructed as the shares of each of the 29 industrial sectors' exports as defined by the 3 digit KSIC in total exports to the U.S. from each individual country. In general, the correlation between each of the four Asian NIEs and Japan, and that between Hong Kong and Singapore, are all far below .5, while the ones among the Asian NIEs themselves (except for the one between Hong Kong and Singapore) all greatly exceed .5. If there exists a tendency on the part of the U.S. to import goods in each specific sector from different countries in a relatively constant proportion, the export structures of those countries will probably exhibit a high correlation. To take this hypothesis to the extreme, if the U.S. maintained an absolutely fixed ratio between its imports from any two countries for each of the 29 sectors, the correlation between the export structures of these two countries would be perfect. Therefore, since any two goods purchased in a fixed proportion could be classified as close complements, a high correlation between export structures will imply a complementary relationship between them. Conversely, low correlation would imply a competitive relationship. According to this interpretation, the pattern formed by the correlation coefficients among the five countries' export structures to the U.S. are consistent with the empirical findings of the regression analysis.
Background: Accurate staging is important to determine treatment modalities and to predict prognosis for the patients with lung cancer. The simple two-stage system of the Veteran's Administration Lung Cancer study Group has been used for staging of small cell lung cancer(SCLC) because treatment usually consists of chemotherapy with or without radiotherapy. However, this system does not accurately reflect segregation of patients into homogenous prognostic groups. Therefore, a variety of new staging system have been proposed as more intensive treatments including either intensive radiotherapy or surgery enter clinical trials. We evaluate the prognostic importance of TNM staging, which has the advantage of providing a uniform detailed classification of tumor spread, in patients with SCLC. Methods: The medical records of 166 patients diagnosed with SCLC between January 1989 and December 1996 were reviewed retrospectively. The influence of TNM stage on survival was analyzed in 147 patients, among 166 patients, who had complete TNM staging data. Results: Three patients were classified in stage I / II, 15 in stage III a, 78 in stage IIIb and 48 in stage IV. Survival rate at 1 and 2 years for these patients were as follows: stage I / II, 75% and 37.5% ; stage IIIa, 46.7% and 25.0% ; stage III b, 34.3% and 11.3% ; and stage IV, 2.6% and 0%. The 2-year survival rates for 84 patients who received chemotherapy(more than 2 cycles) with or without radiotherapy were as follows: stage I / II, 37.5% ; stage rna, 31.3% ; stage IIIb 13.5% ; and stage IV 0%. Overall outcome according to TNM staging was significantly different whether or not received treatment. However, there was no significant difference between stage IIIa and stage IIIb though median survival and 2-year survival rate were higher in stage IIIa than stage IIIb. Conclusion: These results suggest that the TNM staging system may be helpful for predicting the prognosis of patients with SCLC.
A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.
Kim, Uk-Kyu;Kim, Yong-Deok;Byun, June-Ho;Shin, Sang-Hun;Chung, In-Kyo
Journal of the Korean Association of Oral and Maxillofacial Surgeons
/
v.29
no.5
/
pp.301-314
/
2003
The purposes of this retrospective study were to assess the change of serum parameters in oral and maxillofacial surgery patients after operation and to determine what laboratory parameters on treatment periods were associated with the recovery of systemic condition. For purposes of assessing systemic nutritional status, several serum parameters were chosen. The sample patients were randomsubjects extracted from three category patient groups- oral cancer, odontogenic abscess, facial bone fracture based on treated patients at department of oral and maxillofacial surgery in Pusan National University Hospital from September 1, 1998, to September 1, 2002. Each groups were consisted with 10 patients. Each patient chart was examined and blood sample parameters were reviewed with clinical signs, symptoms and vital sign at preoperative day, postoperative 1 day, postoperative 1 week. Several parameters were analyzed statistically for extraction of mean values and differences between the periods groups. The findings of serum parameters of cancer, abscess and fracture groups were as follows: 1. In cancer patients, Hb, MCV, albumin, cholesterol, LDH, AST, ALT, neutrophil, platelet, leukocyte, Na, K, Cl, BUN, creatinine were analyzed. Values of Hb, albumin, AST, neutrophil, leukocyte, Cl showed significantly differences according to periods. 2. In abscess patients, CRP, ESR, leukocyte, body temperature, neutrophil were analyzed. Values of CRP, leukocyte, body temperature, neutrophil showed significanlty differences according to periods. 3. In fracture patients, same parameters with cancer patient's were chosen. Values of platelet, Cl only showed significantly differences according to periods. 4. In cancer patients, data regarding correlation was analyzed statistically as Pearson's value. A positive correlation was found between Hb and albumin, K, Na(P<0.05). A positive correlation was also found between neutrophil and leukocyte(P<0.05). Positive correlations were found between cholesterol and ALT, LDH and platelet, creatinine both, Platelet and BUN, Na and K(P<0.01). 5. In abscess patients, Peason's correlation values were analyzed on parameters. A positive correlation was found only between CRP and neutrophil(P<0.05). 6. In fracture patients, The correlations of parameters also were statistically analyzed. Positive correlations were found between MCV and K, albumin and LDH, AST and three parameters of creatinine, Na, Cl, K and neutrophil, neutrophil and three parameters of leukocyte, BUN, K(P<0.05). Positive correlations were found between LDH and AST, ALT and AST, creatinine both(P<0.01). This retrospective clinical study showed the CRP levels only on abscess patients may be useful in determination of clinical infected status, but the levels of other parameters on cancer, fracture patients did not showed significant values as diagnostic aids for clinical status.
In this study, by-products of rice bran, rice hull, bean curd dregs and food waste were fermented with different ratios of 26, 26, 34 and 14% respectively and the experimental diets were prepared mixing it with various ratios of commercial diets (fermented feed commercial feed : A 80 : 20, B 70 : 30, C 60 : 40, D 50:50). In Experiment I, feed intake, nutrients digestibilities and nitrogen retention were investigated and body weight gain in Experiment 1. For chemical composition of experimental diets, crude protein contents were 13.73, 13.78, 14.45 and 15.14% in A, B, C and D respectively and the contents of crude fat and crude fiber were highest in A as 8.66 and 27.82% respectively. 2. Dry hurler intakes were significantly higher (P<0.05) in A(362.06g/d) and C(358.49g/d) than B and D. Intakes of crude protein and crude fat were not significantly different (P>0.05) among treatments however those of crude fiber and crude ash were significantly higher (P>0.05) in A(101.47g/d). 3. Dry matter digestibilities in the range of 53.38∼68.81% in all treatments have shown the highest value in C of 60% fermented feed plus 40% commercial diet but the lowest in A of 80% of fermented feed plus 20% commercial diet (P<0.05). 59.85% of digestibility of crude protein in A was also lowest among all treatments (p<0.05), 4. 8.47g/d of nitrogen intake in C was recorded highest (P<0.05) however the highest nitrogen retention was marked in B of 50% fermented feed plus 50% of commercial diet due mainly to lower excretion of nitrogen through feces. 5. The data of live weight gain in Experiment II has not been shown as a result since the proper daily gain of body weight was achieved only in D as 88.89g/d and the goats in other treatments have shown frequent diarrhea. However, neglecting the animals with diarrhea, higher amounts of concentrates in the diets (C and D) showed the tendency of higher weight gain. 6. In this study, feeding 60% fermented feed manufactured with domestic agricultural by-products of rice bran, rice hull, bean curd dregs and food waste to Korean native goats have shown satisfactory results of intake and digestibility and it indicates that utilization of domestic agricultural by-products in goats could be improved by the process of fermentation. However it's effects on body weight gain and nitrogen retention were below than expected. Different sources of feedstuff for fermentation may result in different performances of animals. However, to draw overall conclusion from this study, 50∼60% of fermented feed can be recommended in the case of mixing with concentrates.
Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.
Nowadays, it is common that most consumers are purchasing goods in e-stores. The e-stores eager to attract, revisit, retain, and finally convert them into loyal customers. The e-store marketers have planned and executed numerous marketing efforts. As one of the marketing activities, e-store managers attempt to build web sites that meet customers' functional and psychological needs. A wide array of studies has been done to identify factors that could affect customers' response of web sites. Majority of studies were conducted to verify technology-related and functional variables of the website which facilitate transactions and enhance customer responses such as purchase intention and website loyalty. However, there has been little research on the external cues of website and psychological variables of consumer that could have positive influences on customer response. The purpose of this study is to investigate the influence of e-store personality on e-store loyalty through mediating variables such as e-store identification, e-store trust, and e-store engagement. The authors of this study develop the model and set up the six main hypotheses and a set of sub-hypotheses based on a literature review, shown in
. This model is composed of four paths such as dimensions of e-store personality${\rightarrow}$e-store identification, e-store identification${\rightarrow}$e-store loyalty, e-store identification ${\rightarrow}$e-store trust${\rightarrow}$e-store loyalty, and e-store identification${\rightarrow}$e-store engagement${\rightarrow}$e-store loyalty. II. Research Method Ladies under 30s were the respondents of this survey. Data were collected from January 20th to February 26th in 2010. A total of 200 questionnaires were distributed and 169 respondents were analysed finally to test hypotheses because 31 questionnaires had incorrect or missing responses. SPSS 12.0 and LISREL 7.0 program were used to test frequency, reliability, factor, and structural equation modeling analysis. III. Result and Conclusion According to results from factor analysis, eigen value was over 1.0 and items which were below 0.6 were deleted. Consequently, 9 factors(% of total variance is 72.011%) were searched. All Cronbach's ${\alpha}$ values are over the recommended level(${\alpha}$ > 0.7). The overall fit indices are acceptable such as ${\chi}^2$=2028.36(p=0.00), GFI=0.87, AGFI=0.82, CFI=0.81, IFI=0.92, RMR=0.075. All factor loadings were over the recommended level. As the result of discriminant validity check with chi-square difference test between paired constructs, each construct has good discriminant validity. The overall fit indices of final model are acceptable such as ${\chi}^2$=340.73(df=36, p=0.00), GFI=0.92, AGFI=0.81, CFI=0.91, IFI=0.91, RMR=0.085. As test results, 5 out of 6 hypotheses are supported because there are statistically significant casual relationships in structural equation model, shown in
. First of all, hypothesis 1 is partially supported because sub-hypothesis 1-1 and 1-2 are supported, whereas sub-hypothesis 1-3, 1-4, and 1-5 are rejected. Specifically, it reveals that warmth and sophistication dimensions in e-store personality have positive influence on e-store identification, however, activity, progressiveness, and strictness does not have any significant relationship on e-store identification. Secondly, hypothesis 2 was supported. Therefore, it can be said that e-store identification has a positive impact on e-store trust. Thirdly, hypothesis 3 is also supported. Hence, there is a positive relationship between e-store identification and e-store engagement. Fourthly, hypothesis 4 is supported too. e-store identification has a positive influence on e-store loyalty. Fifthly, hypothesis 5 is also accepted. This indicates that e-store trust is a precedent variable which positively affects e-store loyalty. Lastly, it reveals that e-store engagement has a positive impact on e-store loyalty. Therefore, hypothesis 6 is supported. The findings of the study imply that some dimensions of e-store personality have a positive influence on e-store identification, and that e-store identification has direct and indirect influence on e-store loyalty through e-store trust and e-store engagement positively. These results also suggest that the e-store identification in e-store personality is a precedent variable which positively affects e-store loyalty directly and indirectly through e-store trust and engagement as a mediating variable. Therefore, e-store marketers need to implement website strategy based on e-store personality, e-store identification, e-store trust, and e-store engagement to meet customers' psychological needs and enhance e-store loyalty. Finally, the limitations and future study directions based on this study are discussed.
Background : Generally $VO_2$ max is higher in treadmill exercise than cycle ergometer exercise. According to Hassen and Wasserman, $VO_2$ max with treadmill exercise is higher at ratio of 1.11 than that with cycle ergometer. $VO_2$ max also is influenced by race, sociocultural background, exercise habit In this study, $VO_2$ max and AT were evaluated between Treadmill and cycle exercise in male Korean college students. Method: Study subjects were 44 male college students. We randomized them into 2 groups; 24 students did treadmill exercise at first and 1 week later did cycle ergometer. Another 20 students did in opposite method. They made symptom limited maximal exercise. Author defined maximal exercise as followings: 1) respiratory exchange ratio(RER)> 1.1, 2) plateau>30 sec, 3) heart rate reserve(HRR) <15%, or 4) breathing reserve (BR)<30%. Otherwise their results are excluded as submaximal exercise. Anaerobic threshold(AT) was estimated by V-slope method. Results: $VO_2$ max and AT was $45.1{\pm}6.66m\ell$/kg/min and $26.0{\pm}6.78m\ell$/kg/min in treadmill and $34.9{\pm}5.89m\ell$/kg/min, $19.5{\pm}4.77m\ell$/kg/min in Cycle Ergometer. The measured-$VO_2max$/pred-$VO_2max$ was $98.8{\pm}13.24%$ in treadmill; $84.4{\pm}13.42%$ in cycle ergometer. Comparing $VO_2$ max in treadmill with that obtained by Hassen's method, there were significant differences.(p<0.01). At maximal exercise there were differences in HRR, $O_2$/pulse, BR, $V_E$/MVV, $V_E/VCO_2$ between treadmill and cycle but not in $V_E/VO_2$, Vd/Vt, Ti/Ttot. At AT there were differences in $O_2$/pulse, BR, $V_E$/MVV, Ti/Ttot between treadmill and cycle, otherwise not. Conclusion: According to the result of this study, there are larger gap between treadmill and cycle ergometer in normal Korean adults than foreign data, and it needs further study to obtain reference value of Korea.
Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.
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