Assessment of the risk for the development of age-related macular degeneration 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. 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. This paper presents a novel segmentation algorithm for the automatic detection and mapping of drusen in retina images acquired with the aid of a digital Fundus camera. We employ a modified adaptive histogram equalization, namely the multilevel histogram equalization (MLE) scheme, for enhancing local intensity structures. For the detection of drusen in retina images, we develop a novel segmentation technique, the histogram-based adaptive local thresholding (HALT), which extracts the useful information from an image without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background. The performance of the algorithm is established through statistical analysis of the results. This analysis indicates that the proposed drusen detector gives reliable detection accuracy in both position and mass size.