A novel segmentation algorithm for the detection of abnormalities in human eye’s retina

Assessment of the risk for the development of Age related Macular Degeneration requires reliable detection of retinal abnormalities that are considered as precursors of the disease. Typical symptoms of the later are the so called drusen, which appear as abnormal white-yellow deposits on the retina. Conventional image processing techniques are inappropriate for the segmentation of these features, since they are sensitive to non-uniform illumination and non-homogeneous background. This paper presents a novel segmentation technique for the automatic detection of drusen in retina images acquired with the aid of a digital fundus camera. Homomorphic filtering and adaptive histogram equalization are used for non-uniform illumination compensation and enhancement. 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 and are hard to be segmented by other conventional techniques.