Automatic fontanel extraction from newborns’ CT-images using a model based level set method

The newborn’s skull is composed of already ossified parts of the flat bone connected by areas of fibrous membrane not yet ossified, which are called fontanels. At birth, an infant has six of such fontanels. These two different tissue types forming the outer part of the neuro-cranium have different electrical conductivities. Thus, it is important to determine the exact geometry of the fontanels if one aims to solve the inverse problem as e.g. for source localization. Computer Tomography (CT) imaging provides an excellent tool for the non-invasive study of bone which here can easily be identified due to its high contrast as compared to other tissue. Fontanels correspond to not yet ossified cartilage and give less contrast, thus they can be indirectly reconstructed by extrapolation for closing of the gaps between the flat bones forming the skull. In this paper, we propose an automatic model based method using level set to extract the fontanels from CT images. The automatically determined fontanels show good agreement with the manually extracted ones.

Keywords:

newborn , CT image , fontanel , level set , segmentation