Vessel segmentation in 2D-projection images using a supervised linear hysteresis classifier

2D projection imaging is a widely used procedure for vessel visualization. For the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length or the number of vessel segments are needed. To achieve these goals vessel enhancement and segmentation are required. While there are already many vasculature specific vessel segmentation algorithms, we describe in this contribution a more general supervised segmentation method which includes a feature extraction step followed by feature selection and segmentation based on the hysteresis classification paradigm. The method was tested on retina photographies. The rates of false positives and correct classifications were comparable with dedicated methods on similar data sets while it needed less time for both training and providing a segmentation result.