Segmentation of scalp and skull in neonatal MR images using probabilistic atlas and level set method

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted neonatal head MR images. First, the probabilistic scalp and skull atlases are constructed. Second, the scalp outer surface is extracted based on an active mesh method. Third, maximum number of boundary points corresponding to the scalp inner surface is extracted using the constructed scalp probabilistic atlas and a set of knowledge based rules. In the next step, the skull inner surface and maximum number of boundary points of the outer surface are extracted using a priori information of the head anatomy and the constructed skull probabilistic atlas. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from nine newborns (including three atlas and six test subjects) were compared with manual segmented data provided by an expert radiologist. The average similarity indices for the scalp and skull segmented regions were equal to 89% and 71% for the atlas and 84% and 63% for the test data, respectively.