Automatic Fontanel Extraction from Newborns’ CT Images Using Variational Level Set
A realistic head model is needed for source localization methods used for the study of epilepsy in neonates applying Electroencephalographic (EEG) measurements from the scalp. The earliest models consider the head as a series of concentric spheres, each layer corresponding to a different tissue whose conductivity is assumed to be homogeneous. The results of the source reconstruction depend highly on the electric conductivities of the tissues forming the head.The most used model is constituted of three layers (scalp, skull, and intracranial). Most of the major bones of the neonates’ skull are ossified at birth but can slightly move relative to each other. This is due to the sutures, fibrous membranes that at this stage of development connect the already ossified flat bones of the neurocranium. These weak parts of the neurocranium are called fontanels. Thus it is important to enter the exact geometry of fontaneles and flat bone in a source reconstruction because they show pronounced in conductivity. Computer Tomography (CT) imaging provides an excellent tool for non-invasive investigation of the skull which expresses itself in high contrast to all other tissues while the fontanels only can be identified as absence of bone, gaps in the skull formed by flat bone. Therefore, the aim of this paper is to extract the fontanels from CT images applying a variational level set method. We applied the proposed method to CT-images of five different subjects. The automatically extracted fontanels show good agreement with the manually extracted ones.
Keywordsnewborns fontanel source reconstruction level set segmentation
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