“…Other than our proposed textural model, we evaluated the robustness and efficiency of our HPIL model in comparison with other existing texture descriptors using our VELscope ® database, such as gradient directional pattern (GDP) [ 60 ], gradient directional pattern 2 (GDP2), geometric local textural patterns (GLTP) [ 61 ], improved Weber local descriptor (IWLD) [ 62 ], localized angular phase (LAP) [ 63 ], local binary pattern (LBP) [ 64 ], local directional pattern (LDIP) [ 65 ], local directional pattern variance (LDiPv), inverse difference moment standardized (IDN) [ 66 ], local directional number pattern (LDNP) [ 67 ], local gradient increasing pattern (LGIP) [ 68 ], local gradient patterns (LGP) [ 69 ], local phase quantization (LPQ) [ 70 ], local ternary pattern (LTeP) [ 71 ], local tetra pattern (LTrP) [ 72 ], monogenic binary coding (MBC) [ 73 ], local frequency descriptor (LFD) [ 74 ], and local mapped pattern (LMP) [ 75 ]; however, the overall results with these textural descriptors were quite modest (as shown in Table 5 ) as compared to those obtained with our HPIL model, which makes it not suitable for classifying OPMD and standard regions.…”