Fuzzy control based thick rubber model for extracting cerebral surface from neonatal MR images
Takuma Oshiba, Syoji Kobashi, M. Ogawa, Kengo Ando, Reiichi Ishikura, Shun Hirota, S Imawaki, Yutaka Hata · World Automation Congress · 2008
Volumetric cerebrum and measurement of cerebral surface area using MR images plays a fundamental role in computer-aided diagnosis (CAD) of neonatal cerebral diseases such as hypoxic ischemic encephalopathy. There are many conventional methods for brain extraction from adult MR images. However, it is hard to apply these methods for neonatal MR images that image features are different from adult one. This paper proposes a novel method for extracting neonatal cerebral surface with sub-voxel accuracy using Thick Rubber Model (TRM). This method extracts a cerebral surface by deforming TRM so that pseudo MR images synthesized from TRM are identical to the given MR images. Experimental results showed that the proposed method extracted the cerebral surface with higher accuracy (mean RMS distance was 9.0 mm) in comparison with the conventional method (mean RMS distance was 15.7).