A Semi-automatic Method for Vascular Image Segmentation

Vascular diseases are major public heath problem around the world. Vessel segmentation has been widely concerned because it is a key step for diagnosis and surgical planning. Among past strategies, multi-scale line filters are very popular detectors. However, multi-scale integration results in undesirable diffusion when two vessels are closely located. To avoid this problem, we use gradient vector flow as vector field and introduce a vesselness measure to detect vessel which gives high and homogeneous output for line structure so that it is more suitable for segmentation over Frangi's vesselness measure. Level set method is applied to perform vessel segmentation. Our model is tested on real images. Experimental results demonstrate that our approach can successfully separate closely adjacent vessels and address the problems of low contrast and varying vessel width. It shows better performance than multi-scale approach. Furthermore, gradient vector flow makes the contour moving into boundary concavities.