is the number of layer maps (N = 5). Each column of this matrix was normalized using the standard deviation of the data in that column. The correlation matrix (340,0006340,000) was computed using the normalized data set. PCA was used to determine the eigenvalues and eigenvectors of the correlation matrix [65]. The eigenvalue is the sample variance of the projected data points. The components of the eigenvector are the cosines of the angles between the original variable axis and the corresponding principal axis. PCA seeks the order of determinants of a linear combination of the original variables so that the variance of the resulting values is maximum. The components of the eigenvectors provide the coefficients that define the linear combination, while the resulting scores are the projected points. The first two primary components with SDs higher than those of standardized original images (eigenvalues: PC1, c = 1.87; PC2, c = 1.50) are shown in Fig. 7A. References 1. 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