Bone Segmentation in CT Angiography Data Using a Probabilistic Atlas

Automatic segmentation of bony structures in CT angiography datasets is an essential pre-processing step necessary for most visualization and analysis tasks. Since traditional density and gradient operators fail in non-trivial cases (or at last require extensive operator work), we propose a new method for segmentation of CTA data based on a probabilistic atlas. Storing densities and masks of previously manually segmented tissues to the atlas can constitute a statistical information base for latter accurate segmentation. In order to eliminate dimensional and anatomic variability of the atlas input datasets, these have to be spatially normalized (registered) first by applying a non-rigid transformation. After this transformation, densities and tissue masks are statistically processed (e.g. averaged) within the atlas. Records in the atlas can be later evaluated for estimating the probability of bone tissue in a voxel of an unsegmented dataset.