Automated tracking and change detection for Age-related Macular Degeneration Progression using retinal fundus imaging

In this paper, we propose an automated framework to extract three parameters (the area of newly appeared, disappeared and steady drusen) for tracking the changes in the retinal fundus images over the time. Since the retinal images of different times or visits are not necessarily exactly same, instead they could have different locations, scaling, and rotated but there are common regions (i.e., pathologies). We need to find the changes in these common regions of the images. For finding the common region, we propose an image registration algorithm based on vessel geometric shape. Following the registration of two images, we detect the optic disc based on an adaptive threshold, region growing and circle findings, and drusen based on surrounding intensity ratio. Finally, we extract three parameters for tracking the changes of drusen over time. We have tested our proposed method on 22 retinal red-free fundus images, which are randomly selected from the NAT-2 dataset and manually graded the drusen by an experienced grader. The Pearson Correlation Coefficient of 0.94 is achieved for all these three parameters for tracking the changes of the drusen by our proposed method which is better than the state-of-the-art methods. We envisage that the method will help researchers in finding the correlation between the progression of drusen and many eyes and retinal diseases.