Detection of diabetic retinopathy and age-related macular degeneration from fundus images through local binary patterns and random forests

This work focuses on differentiating between pathological and healthy fundus images. The goal is to distinguish between diabetic retinopathy (DR), age-related macular degeneration (AMD) and normal images by analysing the texture of the retina background. Local Binary Patterns (LBP) are used as texture descriptors. The two class problems DR vs. normal and AMD vs. normal, as well as the three class problem of DR, AMD, and normal, have been tested and have obtained promising results. An average sensitivity and specificity higher than 0.86 in all the cases and almost of 0.96 for AMD detection were achieved with a random forest classifier. These results suggest that LBP is a robust texture descriptor for retinal images and the method proposed in this paper, analysing the retina background directly and avoiding difficult lesion segmentation, can be useful for diagnostic aid.