Automatic Drusen Detection and Quantification for Diagnosis of Age-Related Macular Degeneration

Purpose: To develop a new algorithm for a reliable fully-automatic method for the detection and quantification of drusen in color fundus images. Methods: Sixty color fundus images of 15 patients with age-related macular degeneration (AMD) and of 15 control subjects, centered on the macula with a field of view of 60?, were used. Images were acquired with a flood illuminated 3CCD fundus camera (TRX 50DX, Topcon Medical Systems). Two trained graders annotated all visible drusen in the 60 images. One was used as reference standard and the other as second observer. The proposed method uses a two-step classification. In a first step, candidate drusen objects were extracted using a k-nearest neighbor (kNN) classifier and Gaussian filter outputs. In a second step, these were classified as being true drusen or not by a support vector machine (SVM) classifier, using features based on shape, context, intensity and color. Results: The proposed algorithm was evaluated using a patient-based 3-fold cross-validation scheme. The figure of Merit of the FROC was 0.4016 using JAFROC analysis. The second observer has a sensitivity of 0.82 at a false positive rate of 30.12 per image. At the same false positive rate, the system obtained a sensitivity of 0.79 which was comparable to the second observer. Conclusions: A method based on a SVM classifier was presented for drusen detection and quantification on color fundus images. The method is able to detect drusen automatically in the central and peripheral zone. By not only detecting, but also quantifying the drusen, this method opens the way for automatic diagnosis and classification of AMD.