NDLI: Shape and appearance knowledge based brain segmentation for neonatal MR images

Content Provider	IEEE Xplore Digital Library
Author	Hashioka, Aya Kobashi, Syoji Kuramoto, Kei Wakata, Yuki Ando, Kumiko Ishikura, Reiichi Ishikawa, Tomomoto Hirota, Shozo Hata, Yutaka
Copyright Year	2012
Description	Author affiliation: Ishikawa Hospital, Himeji, JAPAN (Ishikawa, Tomomoto) \|\| Hyogo College of Medicine, Nishinomiya, JAPAN (Wakata, Yuki; Ando, Kumiko; Ishikura, Reiichi; Hirota, Shozo) \|\| Himeji Initiative in Computational Medical and Health Technology, Graduate School of Engineering, University of Hyogo, JAPAN (Hashioka, Aya; Kobashi, Syoji; Kuramoto, Kei; Hata, Yutaka)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	807726
Page Count	6
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Image segmentation Head Brain modeling Pediatrics Accuracy Shape fuzzy logic magnetic resonance images neonates brain segmentation head contour brain shape modeling Bayesian classification
Content Type	Text
Resource Type	Article

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