Live-Vessel: Interactive Vascular Image Segmentation with Simultaneous Extraction of Optimal Medial and Boundary Paths Ryan Dickie (ryand@ece.ubc.ca) Ghassan Hamarneh (hamarneh@cs.sfu.ca) Rafeef Abugharbieh (rafeef@ece.ubc.ca) Abstract Vessel analysis is important for a wide range of clinical diagnoses and disease research such as diabetes and malignant brain tumours. Vessel segmentation is a crucial first step in such analysis but is often complicated by structural diversity and pathology. Existing automated techniques have mixed results and difficulties with non-idealities such as imaging artifacts, tiny vessel structures and regions with bifurca- tions. In this paper we propose Live-Vessel as a novel and intuitive semi-automatic vessel segmentation technique. Our contribution is two-fold. First, we extend the classic Live-Wire technique from user- guided contours to user guided paths along vessel centrelines with au- tomated boundary detection. Live-Vessel achieves this by globally op- timizing vessel filter responses over both spatial (x, y) and non-spatial (radius) variables simultaneously. Secondly, our approach incorporates colour gradient and quaternion curvature image information in the segmentation process unlike the majority of current techniques which employ greyscale values or a single colour channel. We validate our method using real medical data from the Digital Retinal Images for Vessel Extraction (DRIVE) database. Quantitative results show that, on average, Live-Vessel resulted in a 8-fold reduction in overall manual segmentation task time, at a 95% accuracy level. We also compare Live-Vessel to state-of-the-art methods and highlight its important ad- vantages in providing the correct topology of the vascular tree hierarchy as well as the associated vessel medial (skeletal) curves and thickness profiles without the need for subsequent error-prone post processing. 1 Software: http://livevessel.cs.sfu.ca/