Table 5.
The Comparison of the Features Selected in SVM-based Space in Evaluation Process by Using the Genetic Algorithm in our Research with Published Literature. Seven New Features Represented in SVM-based Feature Space have been Found to be Important on Differentiating SPNs, and these Features should also be Included in Feature Index of a CBIR System for SPNs
| Feature name | Times selected | Literature published | Description | Significance |
|---|---|---|---|---|
| HIST10 | 24 | Matsuki et al.1 | Pixel range [136 HU, ∼] | Calcification |
| KURT | 23 | Matsuki et al.,1 McNitt-Gray et al.,2 Nakamura et al.,3 Kawata et al.,5 Shah et al.7 | Kurtosis of the nodule | Shape |
| HIST4 | 22 | Nakamura et al.,3 Kawata et al.5 | Pixel range [−104 HU, −65 HU] | The range of fat |
| ENTR4 | 21 | McNitt-Gray et al.2 | The entropy of averaged concurrence matrix, step = 4 | Uniformity or complexity of the texture |
| MEAND | 19 | Matsuki et al.1, McNitt-Gray et al.,2 Nakamura et al.,3 Kawata et al.,5 Shah et al.7 | Mean diameters of the nodule | Shape |
| HIST1 | 18 | McNitt-Gray et al.,2 Shah et al.7 | Pixel range [∼, −185 HU] | Low attenuation pixels |
| ENTR3 | 18 | McNitt-Gray et al.2 | The entropy of averaged concurrence matrix, step = 3 | Uniformity or complexity of texture |
| GAB2 | 17 | Small Gabor filter responses at 45° | The spectrum of local image | |
| COMP | 16 | Nakamura et al.3 | Compactness of nodule | Roundness of the nodule |
| ENTR1 | 13 | McNitt-Gray et al.2 | The entropy of averaged concurrence matrix, step = 1 | Uniformity or complexity of the texture |
| PERI | 12 | Matsuki et al.,1 McNitt-Gray et al.,2 Nakamura et al.,3 Kawata et al.,5 Shah et al.7 | Perimeter of the nodule | Shape |
| ENTR2 | 11 | McNitt-Gray et al.2 | The entropy of averaged concurrence matrix, step = 2 | Uniformity or complexity of the texture |
| MEANV | 10 | Matsuki et al.,1 McNitt-Gray et al.,2 Nakamura et al.3 | Mean of the nodule pixels | Mean of nodule density |
| CORR1 | 10 | The correlation of averaged concurrence matrix, step = 1 | Correlation of local image | |
| HS2 | 10 | The standard deviation of Hurst parameters, scale = 2 | Roughness of an image | |
| CORR2 | 8 | The correlation of averaged concurrence matrix, step = 2 | Correlation of local image | |
| CORR4 | 8 | The correlation of averaged concurrence matrix, step = 4 | Correlation of local image | |
| GAB12 | 8 | Small Gabor filter responses at 135° | The spectrum of local image | |
| HIST3 | 7 | Nakamura et al.,3 Kawata et al.5 | Pixel range [−144 HU, −105 HU] | Nodule density |
| HIST5 | 7 | Nakamura et al.,3 Kawata et al.5 | Pixel range [−64 HU, −25 HU] | Nodule density |
| IDM2 | 7 | Nakamura et al.3 | The inverse difference moment of averaged concurrence matrix, step = 2 | Homogeneity of the texture |
| IDM3 | 7 | Nakamura et al.3 | The inverse difference moment of averaged concurrence matrix, step = 3 | Homogeneity of the texture |
| HM3 | 7 | The mean of Hurst parameters, scale = 3 | Roughness of an image |