Abstract
In a pattern classification setup, image segmentation is achieved by assigning each pixel to one of two classes: object or background. The special case of vessel segmentation is characterized by a strong disproportion between the number of representatives of each class (i.e. class skew) and also by a strong overlap between classes. These difficulties can be solved using problem-specific knowledge. The proposed hysteresis classification makes use of such knowledge in an efficient way. We describe a novel, supervised, hysteresis-based classification method that we apply to the segmentation of retina photographies. This procedure is fast and achieves results that comparable or even superior to other hysteresis methods and, for the problem of retina vessel segmentation, to known dedicated methods on similar data sets.