Segmentation of fetal skulls using ellipse fitting and active appearance models

In this study, we use ultrasound (US) imaging modality frequently employed in prenatal diagnosis and axial skull images used primarily in the examination of fetal neural tubes and work on the segmentation of skull (and brain) structures. The segmentation performance of the mentioned structures is vital in that, applications such as automatic diagnosis systems can provide better feature extraction and classification performance with the aid of such a preprocessing. Our approach works with the principles of coarsely localizing the skull and brain structures present in US images acquired in transverse sections of fetal skulls using model (ellipse) fitting and successively obtaining more accurate segmentation with Active Appearance Models, which is a learning-based segmentation algorithm.