Vessel Extraction Using Crossing-Adaptive Minimal Path Model With Anisotropic Enhancement And Curvature Constraint
Li Liu, Da Chen, Laurent David Cohen, Huazhong Shu, Michel Pâques · 2019
In this work, we propose a new minimal path model with a dynamic Riemannian metric to overcome the shortcuts problem in vessel extraction. The invoked metric consists of a crossing-adaptive anisotropic radius-lifted tensor field and a front freezing indicator. It is able to reduce the anisotropy of the metric on the crossing points and steer the front evolution by freezing the points causing high curvature of a geodesic. We validate our model on the DRIVE and IOSTAR datasets, and the segmentation accuracy is 0.861 and 0.881, respectively. The proposed method can extract the centreline position and vessel width efficiently and accuracy.