Supervised pixel classification for segmenting geographic atrophy in fundus autofluorescene images

Age-related macular degeneration (AMD) is the leading cause of blindness in people over the age of 65. Geographic atrophy (GA) is a manifestation of the advanced or late-stage of the AMD, which may result in severe vision loss and blindness. Techniques to rapidly and precisely detect and quantify GA lesions would appear to be of important value in advancing the understanding of the pathogenesis of GA and the management of GA progression. The purpose of this study is to develop an automated supervised pixel classification approach for segmenting GA including uni-focal and multi-focal patches in fundus autofluorescene (FAF) images. The image features include region wise intensity (mean and variance) measures, gray level co-occurrence matrix measures (angular second moment, entropy, and inverse difference moment), and Gaussian filter banks. A k-nearest-neighbor (k-NN) pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. A voting binary iterative hole filling filter is then applied to fill in the small holes. Sixteen randomly chosen FAF images were obtained from sixteen subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by certified graders. Two-fold cross-validation is applied for the evaluation of the classification performance. The mean Dice similarity coefficients (DSC) between the algorithm- and manually-defined GA regions are 0.84 ± 0.06 for one test and 0.83 ± 0.07 for the other test and the area correlations between them are 0.99 (p < 0.05) and 0.94 (p < 0.05) respectively.