Fully Automatic Skull-Stripping in 3D Time-of-Flight MRA Image Sequences

Abstract

In this paper we present a robust skull-stripping method for the isolation of cerebral tissue in 3D Time-of-Flight (TOF) magnetic resonance angiographic images of the brain. 3D TOF images are often acquired in case of cerebral vascular diseases, because of their good blood-to-background-contrast. Skull-stripping is an essential preprocessing step towards a better segmentation as well as direct visualization of the vascular system. Our approach consists of three main steps. After preprocessing in order to reduce signal inhomogeneities and noise the first main step is the segmentation of the surrounding skull using a region growing approach. The second step is the automatic extraction of distinctive points at the border of the brain, based on the segmentation of the skull, which are then used as supporting points for a graph based contour extraction. The third step is a slicewise correction based on a non-linear registration in order to improve sub-optimal segmentation results. The method proposed was validated using 18 manually stripped datasets. The calculated similarity measures show that the proposed method leads to good segmentation results with only a few segmentation errors. At the same time the mean rate of vessel voxels included by the brain segmentation is 99.18%. In summary the procedure suggested allows a fast and fully automatic segmentation of the brain and is especially helpful as a preprocessing step towards an automatic segmentation of the vessel system or direct volume rendering.