Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds / 生物医学工程学杂志

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.

Class-imbalance Prediction and High-dimensional Risk Factor Identification of Adverse Reactions of Traditional Chinese Medicine with Centralized Monitoring in Real-world Hospitals / 中国实验方剂学杂志

ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.

Yield of Repeat Targeted Direct in-Bore Magnetic Resonance-Guided Prostate Biopsy (MRGB) of the Same Lesions in Men Having a Prior Negative Targeted MRGB

OBJECTIVE: This study's purposes were to determine the yield of repeat direct in-bore magnetic resonance-guided prostate biopsy (MRGB) (MRGB-2) after the first one was found to be negative (MRGB-1), to correlate with clinical parameters, and to present the subgroup analyses of patients with positive repeat biopsies, despite having a negative initial biopsies. MATERIALS AND METHODS: We retrospectively included patients with MRGB-2 after a negative MRGB-1 both between January 2006 and August 2016. This study included 62 patients (median age, 63 years; interquartile range [IQR], 58–66 years) with 75 sampled lesions during MRGB-2 left for analysis, and 63 lesions were resampled and 12 new lesions were sampled. Included patients had a prostate specific antigen (PSA) at MRGB-1 of 13 ng/mL (IQR, 5.8–20.0) and a PSA at MRGB-2 of 15 ng/mL (IQR, 9.0–22.5). All anonymized magnetic resonance imaging (MRI) data were retrospectively reassessed according to Prostate Imaging-Reporting and Data System version 2 by two radiologists. Images of MRGB were compared to determine whether the same prostate lesion was biopsied during MRGB-1 and MRGB-2. Descriptive statistics were utilized to determine the yield of clinically significant prostate cancer (csPCa) at MRGB-2. Gleason score of ≥ 3 + 4 was considered csPCa. RESULTS: In 16/75 (21%) lesions csPCa was detected during MRGB-2. Of 63 resampled lesions, 13 (21%) harbored csPCa at MRGB-2. In two patients, csPCa was detected on repeat biopsy, while the volume of the lesion decreased between MRGB-1 and MRGB-2. CONCLUSION: Patients could benefit from repeat biopsy after negative initial MRGB, especially in the case of increasing PSA values and persisting PCa suspicion in MRI. Further research is needed to establish predictors for positive repeat targeted biopsies.

Methods of analysis and number of replicates for trials with large numbers of soybean genotypes / Métodos de análise e número de repetições para ensaios com grande número de genótipos de soja

ABSTRACT: The aim of this study was to evaluate the experimental precision of different methods of statistical analysis for trials with large numbers of soybean genotypes, and their relationship with the number of replicates. Soybean yield data (nine trials; 324 genotypes; 46 cultivars; 278 lines; agricultural harvest of 2014/15) were used. Two of these trials were performed at the same location, side by side, forming a trial with six replicates. Each trial was analyzed by the randomized complete block, triple lattice design, and use of the Papadakis method. The selective accuracy, least significant difference, and Fasoulas differentiation index were estimated, and model assumptions were tested. The resampling method was used to study the influence of the number of replicates, by varying the number of blocks and estimating the precision measurements. The experimental precision indicators of the Papadakis method are more favorable as compared to the randomized complete block design and triple lattice. To obtain selective accuracy above the high experimental precision range in trials with 324 soybean genotypes, two repetitions can be used, and data can be analyzed using the randomized complete block design or Papadakis method.

RESUMO: O objetivo deste estudo foi avaliar a precisão experimental de diferentes métodos de análise estatística para ensaios com grande número de genótipos de soja e sua relação com o número de repetições. Foram usados dados de produtividade de grãos de soja (nove ensaios, 324 genótipos, 46 cultivares, 278 linhagens, safra agrícola de 2014/15). Dois destes ensaios foram realizados no mesmo local, lado a lado, constituindo um ensaio com seis repetições. Cada ensaio foi analisado pelos delineamentos de blocos ao acaso, látice triplo e uso do método de Papadakis. Foram estimados a acurácia seletiva, diferença mínima significativa e índice de diferenciação de Fasoulas, e, ainda foram testados os pressupostos do modelo. O método de reamostragem foi usado para estudar a influência do número de repetições, variando o número de blocos e estimando as medidas de precisão. Os indicadores de precisão experimental do método de Papadakis são mais favoráveis, quando comparados com os delineamentos de blocos ao acaso e látice triplo. Para obter acurácia seletiva acima da faixa de alta precisão experimental em ensaios com 324 genótipos de soja, pode-se usar duas repetições e analisar os dados, usando o delineamento de blocos completos ao acaso ou método de Papadakis.

Sample size for estimation of the Pearson correlation coefficient in cherry tomato tests / Tamanho de amostra para estimar o coeficiente de correlação de Pearson em ensaios com tomate cereja

ABSTRACT: The aim of this study was to determine the required sample size for estimation of the Pearson coefficient of correlation between cherry tomato variables. Two uniformity tests were set up in a protected environment in the spring/summer of 2014. The observed variables in each plant were mean fruit length, mean fruit width, mean fruit weight, number of bunches, number of fruits per bunch, number of fruits, and total weight of fruits, with calculation of the Pearson correlation matrix between them. Sixty eight sample sizes were planned for one greenhouse and 48 for another, with the initial sample size of 10 plants, and the others were obtained by adding five plants. For each planned sample size, 3000 estimates of the Pearson correlation coefficient were obtained through bootstrap re-samplings with replacement. The sample size for each correlation coefficient was determined when the 95% confidence interval amplitude value was less than or equal to 0.4. Obtaining estimates of the Pearson correlation coefficient with high precision is difficult for parameters with a weak linear relation. Accordingly, a larger sample size is necessary to estimate them. Linear relations involving variables dealing with size and number of fruits per plant have less precision. To estimate the coefficient of correlation between productivity variables of cherry tomato, with a confidence interval of 95% equal to 0.4, it is necessary to sample 275 plants in a 250m² greenhouse, and 200 plants in a 200m² greenhouse.

RESUMO: O objetivo deste trabalho foi determinar o tamanho de amostra necessário para estimar o coeficiente de correlação de Pearson entre variáveis do tomate cereja. Foram instalados dois ensaios de uniformidade em ambiente protegido na primavera/verão de 2014. As variáveis observadas em cada planta foram comprimento médio de fruto, largura média de fruto, peso médio de fruto, número de cachos, número de frutos por cacho, número de frutos e peso total de frutos, sendo calculada a matriz de correlação de Pearson entre elas. Foram planejados 68 tamanhos de amostra em uma estufa e 48 em outra, com tamanho inicial composto de 10 plantas e os demais obtidos acrescentando cinco plantas. Para cada tamanho de amostra planejado foram obtidas 3000 estimativas do coeficiente de correlação de Pearson através de reamostragens "bootstrap" com reposição. O tamanho de amostra de cada coeficiente de correlação foi determinado quando o valor da amplitude do intervalo de confiança de 95% foi menor ou igual a 0,4. A obtenção das estimativas do coeficiente de correlação de Pearson com elevada precisão é difícil para caracteres com relação linear fraca e, consequentemente, maior é o tamanho amostra necessário para estima-los. As relações lineares envolvendo as variáveis relacionadas com o tamanho e o número de frutos por planta tem menor precisão. Para estimar o coeficiente de correlação entre variáveis produtivas do tomate cereja, com intervalo de confiança de 95% igual a 0,4, é necessário amostrar 275 plantas na estufa de 250m², e 200 plantas na estufa de 200m².

Tamanho de amostra para avaliar a densidade populacional de percevejos em lavouras de soja / Sample size to evaluate the population density of bugs in soybean field

RESUMO: O objetivo deste trabalho foi estimar o tamanho de amostra para medir a densidade populacional de espécies de percevejos, variando métodos de coleta, altitude e cultivares de soja. Foram utilizadas 100 lavouras de soja, distribuídas em nove municípios da região central do Rio Grande do Sul, em três safras agrícolas (2010/2011, 2011/2012, 2012/2013). Em cada lavoura, foram demarcados 30 pontos distantes em 20 metros entre si. Em cada ponto, foram coletados percevejos (adultos + ninfas) das espécies Dichelops sp., Piezodorus guildinii e Euchistus heros, por meio dos métodos pano-de-batida largo e rede entomológica, totalizando 6.000 coletas. Para cada lavoura, método de coleta e espéciede percevejo, foram estimadas a média da densidade populacional e o tamanho de amostra por meio de reamostragem. O tamanho de amostra (número de pontos) para determinar a densidade populacional de percevejos varia com o método de coleta e a densidade populacional. Usando o pano-de-batida largo para a coleta de percevejos em soja, para um erro de estimação (amplitude do intervalo de credibilidade) igual a dois percevejos e densidade populacional na classe de 1,5 a 2,0 percevejos m-2, o tamanho de amostra é 13, 77 e 15, respectivamente, para as espécies Dichelops sp., P. guildinii e E. heros.

ABSTRACT: The objective was to estimate the sample size to evaluate the population density of bugs species, varying collection methods, altitude and soybean cultivars. There were used 100 soybean fields, distributed in nine districts of the central region of Rio Grande do Sul on three growing seasons (2010/2011, 2011/2012, 2012/2013). In each field, 30 equidistant points were marked at 20 meters. Bugs (adults and nymphs) of Dichelops sp., Piezodorus guildinii and Euchistus heros species were collected at each point using beat cloth and sweep net, totaling 6,000 collections. For each field, collection method and bug speciesit was estimated average population density, and sample size by resampling. The sample size (number of points) to estimate the population density of bugs vary with the species, altitude, soybean cultivar and the collection method. Using the beat cloth method to collect bugs in soybean, for an estimate error equal to two bugs and population density at class of 1.5 to 2.0 bugs m-2, the number of sampling points are 13, 77 and 15, respectively, for Dichelops sp., P. guildinii and E. heros.

Número de folhas e de plantas para estimação da média do índice spad em crambe / Number of leaves and plants to estimate the average spad index in crambe

O objetivo deste trabalho foi determinar o número de folhas e de plantas necessário para a estimação da média do índice SPAD em crambe, cultivar 'FMS Brilhante'. Em um experimento foram selecionadas, aleatoriamente, 66 plantas. Nessas plantas, foi mensurado o índice SPAD em seis folhas, escolhidas aleatoriamente em cada planta, aos 34 e 40 dias após a emergência, totalizando 396 folhas em cada avaliação. A partir dos dados do índice SPAD, foram calculadas medidas de tendência central e de variabilidade, verificadas a normalidade e a homogeneidade de variâncias. Foram realizadas análises de variância com base no modelo hierárquico (variação entre plantas e variação entre folhas dentro de plantas). O número de folhas, necessário para a estimação da média do índice SPAD, em cada momento de avaliação (34 e 40 dias após emergência), foi determinado por reamostragens, com reposição. Mensurar uma folha por planta, em 36 plantas (36 folhas), é suficiente para a estimação da média do índice SPAD de crambe, com amplitude do intervalo de confiança de 95% igual a três.

The objective of this work was to determine the number of leaves and plants required to estimate the average SPAD index in crambe, cultivar 'FMS Brilhante'. In one experiment were randomly selected 66 plants. These plants, the SPAD index was measured in six leaves, selected randomly from each plant at 34 and 40 days after emergence, totaling 396 leaves on each evaluation. From data of the SPAD index it was calculated measures of central tendency and variability. Analyses of variance based hierarchical model (variation between plants and variation among leaves into plants) were carried. The number of leaves required to estimate the average SPAD index, at each evaluation point (34 and 40 days after emergence), was determined by resampling with replacement. Measure a leaf per plant on 36 plants (36 leaves), is sufficient to estimate the average SPAD index of crambe, with amplitude of confidence interval of 95% equal to three.

The analysis of threshold effect using Empower Stats software / 中华流行病学杂志

In many studies about biomedical research factors influence on the outcome variable,it has no influence or has a positive effect within a certain range.Exceeding a certain threshold value,the size of the effect and/or orientation will change,which called threshold effect.Whether there are threshold effects in the analysis of factors (x) on the outcome variable (y),it can be observed through a smooth curve fitting to see whether there is a piecewise linear relationship.And then using segmented regression model,LRT test and Bootstrap resampling method to analyze the threshold effect.Empower Stats software developed by American X & Y Solutions Inc has a threshold effect analysis module.You can input the threshold value at a given threshold segmentation simulated data.You may not input the threshold,but determined the optimal threshold analog data by the software automatically,and calculated the threshold confidence intervals.

Tamanho de amostra para a estimação da média do comprimento, diâmetro e massa de sementes de feijão de porco e mucuna cinza / Sample size for estimating the average length, diameter and weight of seeds of jack bean and velvet bean

O objetivo deste trabalho foi determinar o tamanho de amostra necessário para a estimação da média do comprimento, dos diâmetros maior e menor e da massa de sementes de feijão de porco (Canavalia ensiformis) e de mucuna cinza (Stizolobium cinereum). Em 300 sementes de feijão de porco e em 300 sementes de mucuna cinza, foram mensurados os seguintes caracteres: comprimento, diâmetros maior e menor e massa. Foram calculadas medidas de tendência central e de variabilidade. Após, foram testadas as hipóteses de igualdade entre as médias e de homogeneidade entre as variâncias. Foi determinado o tamanho de amostra por meio de reamostragem, com reposição de 10.000 amostras. Para a estimação da média do comprimento, dos diâmetros maior e menor e da massa, com intervalo de confiança de 95% igual a 10% da estimativa da média, 117 e 66 sementes são suficientes, respectivamente, para feijão de porco e mucuna cinza.

The objective of this research was to determine the sample size (number of seeds) to estimate the average of length, major and minor diameters and weight of seeds of jack bean (Canavalia ensiformis) and velvet bean (Stizolobium cinereum). In 300 seeds of jack bean and 300 seeds of velvet bean it was measured following characters: length, major and minor diameters and weight. It was calculated measures of central tendency and variability. After the hypothesis of equality between the means and homogeneity of variances, were tested. It was determined the sample size using resampling with replacement of 10,000 samples. For estimating the average of length, major and minor diameters and weight, with amplitude of confidence interval of 95%, equal 10% of average estimate, 117 and 66 seeds are sufficient, respectively, for jack bean and velvet bean.

Número de anos para a estimação da média decendial de duração diária do brilho solar no Rio Grande do Sul / Number of years to estimate the ten-day average of daily sunshine duration in Rio Grande do Sul State, Brazil

O objetivo deste trabalho foi determinar o tamanho de amostra (número de anos) para a estimação da média decendial de duração diária de brilho solar em 30 locais do Rio Grande do Sul. Com os dados de duração de brilho solar do período de 1960 a 2007, formaram-se 1.080 séries temporais (30 locais x 36 decêndios) de média decendial de duração diária de brilho solar. Testou-se a aleatoriedade e a normalidade dos dados, de cada série temporal, por meio dos testes de sequência (run test) e de Lilliefors, respectivamente. Para cada decêndio e local, foi determinado o tamanho de amostra por meio de reamostragem bootstrap, com reposição de 3.000 amostras. Vinte e cinco anos de observações são suficientes para a estimação da média decendial de duração diária de brilho solar, com amplitude do intervalo de confiança de bootstrap de 95% igual a 2,00 horas dia-1.

The objective of this research was to determine the sample size (number of years) to estimate the ten-day average of daily sunshine duration in 30 locations of the Rio Grande do Sul State, Brazil. With sunshine data duration of the period from 1960 to 2007, 1,080 time series (30 locations x 36 ten-days) of ten-day of daily sunshine duration average were formed. The aleatory and normality, in each time series, was verified through the run test and Lilliefors test, respectively. For each ten-day and locality, it was determined the sample size using bootstrap resampling with replacement of 3,000 samples. Twenty-five years of data are enough to predict the ten-day average of daily sunshine duration, with amplitude of bootstrap confidence interval of 95%, equal to 2.00 hour day-1.

Tamanho de amostra para a estimação da média de duração dos períodos larval, pupal e larval mais pupal de Microtheca spp. por bootstrap / Sample size for estimating the average duration of larval, pupal and larval stage plus pupal periods in Microtheca spp. by bootstrap

O objetivo deste trabalho foi determinar o tamanho de amostra (número de insetos) para a estimação da média de duração dos períodos larval, pupal e larval + pupal de Microtheca ochroloma e de Microtheca semilaevis (Coleoptera: Chrysomelidae). Foram coletados adultos dessas espécies em uma área experimental de 0,25ha de nabo forrageiro (Raphanus sativus L.) e foi estabelecida uma criação em laboratório (temperatura de 25±2°C, umidade relativa de 60±10 por cento e fotoperíodo de 12 horas). Após, foram mensurados os períodos larval, pupal e larval + pupal, em dias, de 119 e 81 insetos, respectivamente, de M. ochroloma e de M. semilaevis. Foram calculadas medidas de tendência central e de variabilidade. Foi determinado o tamanho de amostra por meio de bootstrap, com reposição de 10.000 amostras. Para a estimação da média dos períodos larval, pupal e larval + pupal, com intervalo de confiança de bootstrap de 95 por cento de um dia, 42 e 35 insetos são suficientes, respectivamente, para M. ochroloma e M. semilaevis.

The objective of this research was to determine the sample size (number of insects) to estimate the average duration for larval, pupal and larval + pupal periods for M. ochroloma and M. semilaevis. Adults of both species were collected at a 0.25ha experimental area of forage turnip (Raphanus sativus L.) and for laboratory rearing (temperature 25±2°C, relative humidity 60±10 percent and photoperíod 12 hours). Afterwards larval, pupal and larval + pupal periods were measured, in days, of 119 and 81 insects, respectively, M. ochroloma and M. semilaevis. It was calculated measures of central tendency and variability and determined the sample size using bootstrap with replacement of 10000 samples. For estimating the average larval, pupal and larval + pupal periods, with amplitude of bootstrap confidence interval of 95 percent, equal a day, 42 and 35 insects are sufficient, respectively for both M. ochroloma and M. semilaevis.

Tamanhos de parcela e de ensaio de uniformidade em nabo forrageiro / Plot sizes and uniformity assays in turnip

Os objetivos deste trabalho foram determinar o tamanho ótimo de parcela e o tamanho de ensaio de uniformidade para a estimação do tamanho ótimo de parcela, a fim de avaliar a massa verde de nabo forrageiro (Raphanus sativus L.). Foram usados os dados de massa verde de 3.456 unidades experimentais básicas de 0,5m x 0,5m (0,25m²) e planejados 46 tamanhos de ensaio de uniformidade. O tamanho ótimo de parcela foi determinado por meio do método da curvatura máxima do modelo do coeficiente de variação. O tamanho de ensaio de uniformidade foi determinado a partir de bootstrap, com reposição de 2.000 amostras de cada tamanho de ensaio planejado. O tamanho ótimo de parcela para avaliar a massa verde de nabo forrageiro é de 4,82 unidades experimentais básicas de 0,25m² (1,20m²). Ensaios de uniformidade com 225 unidades experimentais básicas de 0,25m² (56,25m²) são suficientes para a estimação do tamanho ótimo de parcela, para uma amplitude do intervalo de confiança de bootstrap de 95 por cento, igual a uma unidade experimental básica.

The objectives of this research was to determine the optimum plot size and uniformity assay size for estimating the optimum plot size to evaluate the fresh weight of turnip (Raphanus sativus L.). Data were used from the fresh weight of 3,456 basic experimental units of 0.5m x 0.5m (0.25m²) and 46 uniformity assays sizes were planned. The optimum plot size was determined by the method of maximum curvature of the coefficient of variation model. The uniformity assay size, based on bootstrap with replacement of 2,000 samples, of each planned uniformity assay size, was determined. The optimum plot size to evaluate the fresh weight of turnip is the basic experimental units 4.82 (1.20m²). Uniformity assays with 225 basic experimental units (56.25m²) are sufficient to estimate the optimum plot size, in the bootstrap confidence interval of 95 percent, equal to one basic experimental unit.

Estimation of typical value and confidence interval of gamma-radiation dose rate from tank-transported copper ore by means of bootstrap resampling method / 中华放射医学与防护杂志

Objective To investigate the typical value of gamma-radiation dose rate and its confidence interval in tank-transported copper ore by using bootstrap resampling techniques. Methods Bootstrap resampling method, coupled with kernel density estimation, introduced to acquire the typical value of gamma-radiation dose rate in copper ore. Results The typical value of gamma-radiation dose rate in copper ore was expressed as the central tendency of the means of resampling, and two kinds of confidence interval, empirical percentile and bias-corrected accelerated confidence interval, were provided as standard error. Conclusion It is clearly demonstrated that this method has an advantage to give a robust description in explanation of central tendency and variation range of gamma-radiation dose rate data profiles.

Effect of distributed parallel-computing on the time of resampling of digitized human / 第三军医大学学报

Objective To explore the effect of distributed parallel-computing on the time of resampling of digitized human with different amounts of client.Methods Computers were randomly divided into one group of serial-computing and five groups of parallel-computing,and the same data of digitized human was resampled by each group.The resampling time of clients and server in each group,speedup of parallel-computing were compared.Results Compared with the group of serial-computing,the time of resampling of the groups of parallel-computing decreased greatly(P

A Study on the Improvement of Digital Periapical Images using Image Interpolation Methods / 대한구강악안면방사선학회지

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared ; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(p0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods(p<0.05). 5. The speed of computation time was the fastest with nearest neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.