A Model-Based Approach for Automated Feature Extraction in Fundus Images

A new approach to automatically extract the main features in color fundus images are proposed in this paper. Optic disk is localized by the principal component analysis (PCA) and its shape is detected by a modified active shape model (ASM). Exudates are extracted by the combined region growing and edge detection. A fundus coordinate system is further set up based on the fovea localization to provide a better description of the features in fundus images. The success rates achieved are 99%, 94%, and 100% for disk localization, disk boundary detection, and fovea localization respectively. The sensitivity and specificity for exudate detection are 100% and 71%. The success of the proposed algorithms can be attributed to the utilization of the model-based methods.