Automatic Drusen Detection from Colour Retinal Images

Assessment of the risk for development of age-related macular degeneration (ARMD) requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. Colour retinal images are used presently to visually identify the presence of drusens. Segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. Automated detection and analysis can provide vital information about the quantity and quality of the drusens. In this paper, we report on two methods that we have developed to reliably detect and count drusens. The methods exploit the morphological characteristics of the drusens such as texture and their 3D profiles. We compare the results of using these two methods and make recommendations for automated drusen analysis.