Table 8.
Different methods for detection of blood vessels.
| Algorithm | Image processing techniques | Database | Color space | Sensitivity | Specificity | Accuracy |
|---|---|---|---|---|---|---|
| Bhatia et al. [28] | Gaussian filter, Canny edge detection, morphological operations, and Otsu thresholding | DRIVE STARE |
Green channel | 70.31% | 97.35% | 95.23% |
|
| ||||||
| Fraz et al. [78] | Ensemble system of bagged and boosted decision trees, Gabor filter, and morphological transformation | STARE DRIVE CHASE_DB1 |
Green channel | 0.75 0.74 0.72 |
0.97 0.98 0.97 |
0.95 0.94 0.94 |
|
| ||||||
| Nguyen et al. [79] | Vessel segmentation based on the line detectors at varying scale | STARE DRIVE |
Green channel | — | — | 0.93 Acc 0.94 Acc |
|
| ||||||
| Fraz et al. [80] | Multiscale line detection method, Gabor filter | DRIVE STARE Messidor |
Gray scale | 0.73 0.73 0.77 |
0.97 0.97 0.98 |
0.94 0.95 0.96 |
|
| ||||||
| Yin et al. [81] | Spectral clustering technique based on morphological features, Hessian matrix | DRIVE REVIEW |
Green channel | — | — | Above 94% |
|
| ||||||
| Vega et al. [82] | Lattice Neural Networks with Dendritic Processing | STARE | Green channel | — | — | 99.8% |
|
| ||||||
| Marín et al. [83] | Neural Network | DRIVE STARE |
Green channel | — | — | 0.95 0.97 |
|
| ||||||
| Hou [84] | Multidirectional morphological top-hat transform, rotating structuring element | DRIVE STARE |
Green channel | 0.73 0.73 |
0.96 0.96 |
0.94 0.93 |
|
| ||||||
| Shami et al. [85] | Morphological operations | Nikookari | Green channel | 85.82% | 99.98% | — |