Efficient Critical Point Detection for Curvilinear Structures Using a Ring-Like Ray-Shooting Model

The correct location and identification of critical points, including terminations, three-branch points, and four-branch points, for curvilinear structures in images is a crucial task in many curvilinear structure reconstruction processes. Few methods have been proposed to detect all three types of critical points simultaneously. In this article, we upgraded the existing ray-shooting model to a multifunctional ring-like ray-shooting model that can efficiently detect all types of critical points for curvilinear structures at the same time. First, we measured the minimum and maximum radii of the region of interest based on a sphere-growing method and the modified ray-burst model, respectively. Second, the estimated minimum and maximum radii are used to determine the inner and outer radii of the ring-like ray-shooting model, allowing the proposed model to adaptively extract the pixel intensity distribution feature along the potential branches. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to detect critical points of curvilinear structures by analyzing the number of clusters of pixel intensity distribution extracted from the ring-like ray-shooting model. The experimental results on multiple data sets confirmed that the proposed method can achieve very accurate detection results efficiently and provide the reconstruction of curvilinear structures with beneficial information.