A centerline-based algorithm for estimation of blood vessels radii from 3D raster images

Abstract

Two approaches to Hessian-based estimation of tubular blood-vessel radius from 3D raster images are compared. In the proposed approach, binary skeleton is found for each tubular vessel-tree branch by thresholding the Hessian-derived vesselness image. Coordinates of the binary skeleton are approximated with smooth 3D spline functions. Their derivatives with respect to arc length give local tangent vectors, and thus planes normal to the vessel centerline. A proposed image intensity profile model is then least-squares fitted to the vessel cross-section by those planes, at each skeleton point. The circular vessel local radius is one of the model parameters. In the reference method, the vessel centerline direction is defined by the local Hessian eigenvector corresponding to the smallest eigenvalue. The radius is estimated using a square root of the vessel cross-section area (as obtained by an adaptive thresholding), divided by π. The impact of Frangi Hessian filter parameters and scale selection on the methods' performance is examined. Higher accuracy, precision and robustness to image noise and artifacts is demonstrated for the proposed method. Example of the method suitability for modeling of brain vasculature magnetic resonance images is also presented in this paper.