[en] Segmentation of the human cranial bone from MRI data is challenging, because compact bone is characterized by very short transverse relaxation times and typically produces no signal when using conventional magnetic resonance imaging (MRI) sequences. In this work, we propose a fully automated segmentation algorithm, which uses dual-echo, ultra-short echo-time (UTE) MRI data. The segmentation was initialized by interval thresholding of approximated T${}_2^{\ast <!--\; ???\; -->}$ relaxation time maps in the range of 1 ms < T${}_2^{\ast <!--\; ???\; -->}$< 3 ms. This parameter range was derived from a manual region-of-interest analysis of high resolution data of the cranial layers, resulting in average T${}_2^{\ast <!--\; ???\; -->}$ relaxation times of 1.7 ± 0.3 ms in the lamina externa, 2.5 ± 0.3 ms in the diploe and 1.7 ± 0.2 ms in the lamina interna. Segmentations were performed based on data of 8 healthy volunteers that were acquired with different acquisition parameters and spatial resolutions to test the stability of the algorithm. Comparison with computed tomography data demonstrated close agreement with the segmented UTE MRI data. Visualization of the segmented cranial bone was performed by volumetric rendering and by using the approximated T${}_2^{\ast <!--\; ???\; -->}$ values for color coding, clearly visualizing the cranial sutures as well as their intersections.