The five features discovered include ADC, Sum Average, Entropy, Elongation and Sum Variance.
iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.
The extracted 13 co-occurrence features were as follows: angular second moment, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient [ 14].
Some commonly used features are calculated from co-occurrence matrix, including: energy, correlation, inertia, entropy, inverse difference moment, sum average, sum variance, sum entropy, difference average, difference variance, difference entropy, and information measure of correlation.
The texture features included angular second moment, contrast, correlation, inverse difference moment, average of sum, variance of sum, entropy of sum, entropy, average of difference, variance of difference, entropy of difference, measurement of correlation 1 information, and measurement of correlation 2 information [ 40].
Haralick et al. defined 10 grey-level co-occurrence matrix (GLC M enhancement features (energy, maximum probability, contrast, homogeneity, entropy, correlation, sum average, sum variance, difference average and difference variance) to describe texture [ 30], and these features were used to characterise lesions with and without mass effect.
Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance (measurement error) to the total variance (sum of sampling error and measurement error).
When considering all 11 dimensions, the uninfected group is indeed the tightest, with a total variance (sum of the 11 variances) that is fairly constant over time, whereas the infected groups show higher and changing total variance (Table 6).
When more than one variable is used to characterize an ecological function, both the summed variance and co-variance components of the CV2 measure are expected to scale linearly with the squared sum of means.
For the all the images presented in this paper, the summed variance for the first three principal components accounts for more than 80% of the variance over pixels.
The time frame with the best standard predictions (least summed variance from known values; usually highest r2 value of the regression line) is selected for sample quantification.
