Shape and appearance knowledge based brain segmentation for neonatal MR images
Aya Hashioka, Syoji Kobashi, Kei Kuramoto, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata · World Automation Congress · 2012
The neonatal cerebral disorders might deform the brain shape, and reduce the cerebral function. For the diagnosis of cerebral disorders, it is effective to measure cerebral volume and surface area using head magnetic resonance (MR) image. The measurement should require a brain segmentation process. However, there are few studies for neonatal brain. This study proposes a brain segmentation method for a neonatal brain. In this study, we propose a shape and appearance knowledge based brain segmentation (SABS) method. SABS method segments a brain and cerebrospinal fluid (CSF) region by using a brain atlas model. Next, it classifies a brain and CSF region into some classes by using Bayesian classification with Gaussian mixture model, and optimizes the brain surface by using fuzzy rule-based active surface model method. Experimental results in 14 neonatal subjects (revised age between −1 month and 1 month) showed that the proposed method segmented the brain region with higher accuracy than the conventional methods.