NDLI: Neonatal brain segmentation using 4-D fuzzy object model

Content Provider	IEEE Xplore Digital Library
Author	Kobashi, S. Nakano, R. Kuramoto, K. Wakata, Y. Ando, K. Ishikura, R. Ishikawa, T. Hirota, S. Hata, Y. Kamiura, N.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Eng., Univ. of Hyogo, Himeji, Japan (Kobashi, S.; Nakano, R.; Kuramoto, K.; Kamiura, N.) \|\| Dept. of Radiol., Hyogo Coll. of Med., Nishinomiya, Japan (Wakata, Y.; Ando, K.; Ishikura, R.; Hirota, S.) \|\| Ishikawa Hosp., Ishikawa, Japan (Ishikawa, T.) \|\| Grad. Sch. of Simulation Studies, Univ. of Hyogo, Himeji, Japan (Hata, Y.)
Abstract	Brain region segmentation in neonatal magnetic resonance (MR) images is an essential task for computer-aided diagnosis of neonatal brain disorders using MR images. We have proposed a neonatal brain segmentation method using a fuzzy object model (FOM), which represents a prior knowledge of brain shape and location. The FOM is constructed from multiple neonatal brain MR images whose revised age was between 0 and 4 weeks. The method segmented the brain region with a good accuracy for subjects whose age matches of the training data set. To enhance the method, we need multiple FOMs for each age. The other solution is to develop a growable model. This paper introduces 4-D FOM and applies it to neonatal brain segmentation. This paper introduces a neonatal brain segmentation method using 4-D FOM. The proposed method consists of three components. The first part proposes a method for estimating the brain development progress, called growth index in this study, from MR images based on Manifold learning. The second part shows a procedure for generating 4-D FOM using the estimated growth index. The third part is to segment brain region based on fuzzy-connectedness image segmentation using 4-D FOM. The proposed method was applied to 16 neonatal subjects. The results show that 4-D FOM is superior to stable 3-D FOM for segmenting neonatal brain region from MR images.
Starting Page	1
Ending Page	7
File Size	2980880
Page Count	7
File Format	PDF
ISBN	9781479951796
e-ISBN	9781479951802
DOI	10.1109/ICIEV.2014.6850710
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-23
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Pediatrics Image segmentation Brain segmentation Fuzzy connectedness image segmentation Neonatal brain Training data 4-D fuzzy object model Brain modeling Educational institutions Manifold learning Indexes
Content Type	Text
Resource Type	Article

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