A real-time fuzzy morphological algorithm for retinal vessel segmentation

Abstract

The detection of vessels is the first step towards an automatic diagnosis and in-depth study of retinal images to aid ophthalmologists. In this paper, a real-time algorithm based on fuzzy morphological techniques is introduced to segment vessels in retinal images. This framework provides a good trade-off between expressive power and computational requirements, since the information in the local neighbourhood is quickly processed by combining a series of fast procedures. Specifically, this method is based on the fuzzy black top-hat transform, which proves to be a simple yet very effective technique. The algorithm processes images of the DRIVE and STARE datasets, in average, in 37 and 57 ms, respectively. Thus, it can be employed while a patient is being examined, embedded into more complex systems or as a pre-screening method for large volumes of data. It outstands when it is compared with other state-of-the-art methodologies in terms of its real-time processing time and its competitive performance.