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| Diagnostic techniques for AMD |
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| Image analysis techniques for AMD | Color fundus image analysis | Texture-based segmentation | Gabor filter and wavelet analysis [33] |
| Statistical structural information-based texture analysis [34] |
| The Artificial Neural Network (ANN) based supervised classifiers [35] |
| Expectation Maximization (EM) based unsupervised classifiers [36] |
| Thresholding-based segmentation | Probability based thresholding [37] |
| Otsu method for background selection and intensity thresholding [38] |
| Histogram based adaptive local thresholding [39] |
| Edge based thresholding [11] |
| Clustering-based segmentation | Spatial histogram and similarity based classification [40] |
| Distance based clustering [41] |
| Wavelet based feature extraction & SVM based classification [42] |
| Edge and template matching | Gaussian template matching [43] |
| Gradient based segmentation [44] |
| OCT image analysis techniques | Graph-based segmentation | Graph and dynamic programming [31] |
| A graph-based multilayer segmentation approach [45] |
| Active contour-based segmentation | Anisotropic noise suppression and deformable splines [46] |
| Based on active contours and Markov random fields [47] |
| Polynomial curve-fitting based technique | Local convexity condition and fitting second- or fourth-order polynomials [48, 49] |
| Based on the distance between the abnormal RPE and the normal RPE floor [19, 50] |
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