Skull and fontanel segmentation from neonatal CT data by model based variational level set using localized coefficient

Neonatal skull is different from adults' skull. It is composed of ossified parts of flat bone and fontanels. Fontanels are fibrous membranes that at this stage of development connect the already ossified flat bones of the cranium. Since these two different tissue types have different electrical conductivities, it is important to model the geometry of the fontanels if one aims to solve the inverse problem as e.g. for source localization. In neonatal Computer Tomography (CT) images fontanels are identifiable as gaps between the bones forming the cranium. In this paper we propose an automatic method based on a level set algorithm with shape prior to segment skull and fontanels from neonatal CT images. Quantitative evaluation based on similarity between automatically and manually segmented skulls and fontanels using ten subjects show that the proposed method can appropriately segment skull and fontanels.