Table 1.
List of the defined set of 354 features to identify the DRT presence
| No. of features | Category | Features |
|---|---|---|
| [1 − 15] | Global intensity-based features (GIBS) | Maximum, minimum, mean, median, std, variance, 25th percentile, 75th percentile, skewness, and maximum likelihood estimates for normal distribution |
| [16 − 20] | Gray-level intensity histogram (GLIH) | Obliquity, kurtosis, energy, and entropy |
| [21 − 36] | Gray-level co-occurrence matrix (GLCM) | Contrast, energy, correlation, and homogeneity |
| [37 − 117] | Histogram of oriented gradients (HOG) | 9 windows per bound box and 9 histogram bins |
| [118 − 245] | Gabor | Mean and std. orientations = 8 and scale = 8 |
| [246 − 309] | Local binary pattern (LBP) | Mean and std. number of neighbors = (4, 8, 12, 16) and filter radius: 1–8 |
| [310 − 337] | LAWS | A collection of convolutional kernels that search for characteristic texture patterns |
| [338 − 340] | Fractal dimension (FD) | Mean, std, and lacunarity |
| [341 − 347] | Gray-level run length image (GLRLI) | SRE, LRE, GLN, RP, RLN, LGRE, and HGRE |
| [348 − 354] | Retinal thickness analysis | The maximum height of the OPL/ISOS, ILM/ISOS, ILM/RPE, and the ratio between these regions |
SRE short run emphasis, LRE long run emphasis, GLN gray-level non-uniformity, RP run percentage, RLN run length non-uniformity, LGRE low gray-level run emphasis, HGRE high gray-level emphasis