Partition-based extraction of cerebral arteries from CT angiography with emphasis on adaptive tracking

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions.

Toward interactive user guiding vessel axis extraction from gray-scale angiograms: an optimization framework

We propose a novel trace-based method to extract vessel axes from gray-scale angiograms without preliminary segmentations. Our method traces the axes on an optimization framework with the bounded spherical projection images and the sum of squared difference metric. It does not take alternate steps to search the next axial point and its tangent as in other trace-based algorithms, instead the novel method finds the solution simultaneously. This helps avoid U-turns of the trace and large spatial discontinuity of the axial points. Another advantage of the method is that it enables interactive user guidance to produce continuous tracing through regions that contain furcations, disease portions, kissing vessels (vessels in close proximity to each other) and thin vessels, which pose difficulties for the other algorithms and make re-initialization inevitable as illustrated on synthetic and clinical data sets.

Vessel axis tracking using topology constrained surface evolution

An approach to 3-D vessel axis tracking based on surface evolution is presented. The main idea is to guide the evolution of the surface by analyzing its skeleton topology during evolution, and imposing shape constraints on the topology. For example, the intermediate topology can be processed such that it represents a single vessel segment, a bifurcation, or a more complex vascular topology. The evolving surface is then reinitialized with the newly found topology. Reinitialization is a crucial step since it creates probing behavior of the evolving front, encourages the segmentation process to extract the vascular structure of interest and reduces the risk on leaking of the curve into the background. The method was evaluated in two computed tomography angiography applications: 1) extracting the internal carotid arteries including the region in which they traverse through the skull base, which is challenging due to the proximity of bone structures and overlap in intensity values; 2) extracting the carotid bifurcations including many cases in which they are severely stenosed and contain calcifications. The vessel axis was found in 90% (18/20 internal carotids in ten patients) and 70% (14/20 carotid bifurcations in a different set of ten patients) of the cases.