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Vascular tracking of angiographic image sequences is one of the mostclinically important tasks in the diagnostic assessment and interventionalguidance of cardiac disease. However, this task can be challenging toaccomplish because of unsatisfactory angiography image quality and complexvascular structures. Thus, this study proposed a new greedy graph search-basedmethod for vascular tracking. Each vascular branch is separated from thevasculature and is tracked independently. Then, all branches are combined usingtopology optimization, thereby resulting in complete vasculature tracking. Agray-based image registration method was applied to determine the trackingrange, and the deformation field between two consecutive frames was calculated.The vascular branch was described using a vascular centerline extraction methodwith multi-probability fusion-based topology optimization. We introduce anundirected acyclic graph establishment technique. A greedy search method wasproposed to acquire all possible paths in the graph that might match thetracked vascular branch. The final tracking result was selected by branchmatching using dynamic time warping with a DAISY descriptor. The solution tothe problem reflected both the spatial and textural information betweensuccessive frames. Experimental results demonstrated that the proposed methodwas effective and robust for vascular tracking, attaining a F1 score of 0.89 ona single branch dataset and 0.88 on a vessel tree dataset. This approachprovided a universal solution to address the problem of filamentary structuretracking.

Greedy Graph Searching for Vascular Tracking in Angiographic Image Sequences