Neonatal brain MR image segmentation based on system-of-systems in engineering technology
Aya Hashioka, Kosuke Yamaguchi, Syoji Kobashi, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Kei Kuramoto, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata · 2011
Measurement of cerebral volume and surface area using magnetic resonance (MR) image is effective for quantitative diagnosis of cerebral diseases. The measurement should require a brain segmentation process. Although many approaches for adult brain have been studied, there are few studies for neonatal brain. This study proposes a brain segmentation method for neonatal brain. Based on system of systems engineering technology, the proposed approach is composed from two systems; automated fuzzy logic based skull striping (AFSS) system and contour shape based modeling (CSM) system. AFSS segments the cerebral region based on Bayesian classification with Gaussian mixture model. CSM evaluates the skull stripping result of AFSS, and updates AFSS system parameters. Experimental results in 34 neonates (revised age between -2 weeks 1 day and 2 years 5 months) showed that the proposed approach segmented the brain region with sensitivity of 98.1% and false-positive rate of 27.9%.