A model-based consecutive scanline tracking method for extracting vascular networks from 2-D digital subtraction angiograms.
We propose a new model-based algorithm for the automated tracking of vascular networks in 2-D digital subtraction angiograms. Consecutive scanline profiles are fitted by a parametric imaging model to estimate local vessel center point, radius, edge locations and direction. An adaptive tracking strategy is applied with appropriate termination criteria to track each vessel segment. When tracking stops, to prevent premature termination and to detect bifurcations, a look ahead detection scheme is used to search for possible continuation points of the same vessel segment or those of its bifurcated segments. The proposed algorithm can automatically extract the majority of the vascular network without human interaction other than initializing the start point and direction. Compared to other tracking methods, the proposed method highlights accurate estimation of local vessel geometry. Accurate geometric information and a hierarchical vessel network are obtained which can be used for further quantitative analysis of arterial networks to obtain flow conductance estimates.
Zou
Ping
P
Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering, University of Sheffield, S1 3JD Sheffield, UK.
Chan
Philip
P
Rockett
Peter
P
D000465:Algorithms; D015901:Angiography, Digital Subtraction / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008955:Models, Cardiovascular; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*