Model-based multiscale detection of 3D vessels

In this paper, we present a new approach to segment vessels from 3D angiography of the brain. Our approach is based on a vessel model and uses a multiscale analysis in order to extract the vessel network surrounding an aneurysm. Our model allows us to choose a criterion based on the eigenvalues of the Hessian matrix for selecting a subset of interesting points near the vessel center. It also allows us to choose a good parameter for a γ-normalization of the single scale response. The response at one scale is obtained by integrating along a circle the first derivative of the intensity in the radial direction. Once the multiscale response is obtained, we create a smoothed skeleton of the vessels combined with a MIP or a volume rendering to enhance their visualization. The method has been tested on a large variety of 3-D images of the brain, with excellent results. Vessels of various size and contrast are detected with a remarkable robustness, and most junctions are preserved