Spearman's rank-order correlation (the linear correlation of the ranks) was computed and the significance of the correlation coefficient was assigned as described in [ 44].
Malvidin-3-glucoside (Extrasynthese Co., Genay, France), cyanidin-3-glucoside (Extrasynthese Co., Genay, France) and quercetin-3-glucuronide (Sigma-Aldrich, St Louis, MO, USA) were quantified with their respective standard curves over three orders of magnitudes, with linear correlation coefficients greater than 0.999.
Moreover, four corresponding indicators, such as Spearman rank-order correlation coefficient (SROCC), Kendall rank-order correlation coefficient (KROCC), Pearson linear correlation coefficient (PLCC), and root mean square error (RMSE), are used to compare the performance of the index objectively [59].
The performance of the objective models is evaluated using the Spearman Rank Order Correlation Coefficient, the Pearson Linear Correlation Coefficient, the Root-Mean-Square Error (RMSE) and the Outlier Ratio.
In this work, we propose to replace tumor SUV by the tumor-to-blood standard uptake ratio (SUR) in order to distinctly improve the linear correlation with K m.
In the following experiments, we use four evaluation criteria to compare the performance of the FR-IQA methods: the Spearman rank order correlation coefficient (SROCC), the Kendall rank order correlation coefficient (KROCC), the Pearson linear correlation coefficient (PLCC), and the root-mean-squared error (RMSE).
Experiments on the LIVE2 database show that the Spearman rank-order correlation coefficient (SROCC) and linear correlation coefficient (LCC) values exceed 0.85, while the scale or distortion type of the training set changes, which indicates stability.
There was a statistically significant linear correlation between item order and mean scores for meaningfulness (R=-0.6, p=0.004), but not for priority.
In order to reduce lost information, the linear correlation coefficient (LCC) is computed through 436-dimension vector as follows: (2) LCC (S ) = 436 ∑ i = 1 436 x i x i − − ∑ i = 1 436 x i ∑ i = 1 436 x i − 436 ∑ i = 1 436 x i 2 − (∑ i = 1 436 x i ) 2 ∗ 436 ∑ i = 1 436 x i − 2 − (∑ i = 1 436 x i − ) 2, where x i is the jth 2-gram feature value and x i - is the mean value of jth 2-gram feature.
It can be seen from Table 3 that the linear correlation coefficients for first order are not good when compared to the second-order model.
