Vessel scale-selection using MRF optimization

Many feature detection algorithms rely on the choice of scale. In this paper, we complement standard scale-selection algorithms with spatial regularization. To this end, we formulate scale-selection as a graph labeling problem and employ Markov random field multi-label optimization. We focus on detecting the scales of vascular structures in medical images. We compare the detected vessel scales using our method to those obtained using the selection approach of the well-known vesselness filter (Frangi et al 1998). We propose and discuss two different approaches for evaluating the goodness of scale-selection. Our results on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database show an average reduction in these error measurements by more than 15%.