NDLI: Brain volume segmentation in newborn infants using multi-modal MRI with a low inter-slice resolution

Content Provider	IEEE Xplore Digital Library
Author	Despotovic, I. Vansteenkiste, E. Philips, W.
Copyright Year	2010
Description	Author affiliation: Faculty of Electrical Engineering, Ghent University, TELIN-IPI-IBBT, Sint-Pietersnieuwstraat 41, 9000, Belgium (Despotovic, I.; Vansteenkiste, E.; Philips, W.)
Abstract	Brain volume segmentation from neonatal magnetic resonance images (MRI) offers the possibility of exploring the developmental changes, measuring the brain growth, detecting early disorders and three-dimensional (3D) volume reconstruction. However, such segmentation is challenging mainly due to the fast growth process, complex anatomy of the developing brain and often poor MRI quality. Existing techniques are mainly developed for adult brain and are not applicable to neonates or require additional corrections. In this paper we present an algorithm for brain volume segmentation in neonates using T1-weighted (T1-w) and T2-weighted (T2-w) MRI with a low inter-slice resolution. The method incorporates both intensity and edge information and consists of three main steps: image pre-processing, brain segmentation and 3D brain reconstruction. Our algorithm is tested on real neonatal brain MRI with a gestational age between 39–41 weeks and achieves performance comparable to manual segmentation. Also, experimental segmentation results show that our method is effective and more accurate than segmentation methods originally developed for adults.
Starting Page	5038
Ending Page	5041
File Size	849228
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Pediatrics Image segmentation Histograms Head Image resolution Nonhomogeneous media
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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