NDLI: Sequence tolerant segmentation system of brain MRI

Content Provider	IEEE Xplore Digital Library
Author	Gu, Y. Hall, L.O. Goldgof, D. Kanade, P.M. Murtagh, F.R.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA (Gu, Y.; Hall, L.O.; Goldgof, D.; Kanade, P.M.)
Abstract	An automatic human brain segmentation system for magnetic resonance images is presented. It has two main parts: a fuzzy clustering algorithm and a set of cluster combination rules. Images are segmented into ten classes by the unsupervised fuzzy c-means clustering algorithm. Then a knowledge-based system labels the clusters into the tissues of interest: cerebrospinal fluid, gray matter and white matter. This approach can process MRI data that comes from different scanners with different sequences and head coils, using several different spin-echo images (with different echo times) and different slice thickness. The system adapts without manual intervention. Segmented synthetic image data from the brainWeb simulated normal brain database resulted in a one voxel away accuracy of 90%. The results from real data from various magnetic resonance imagers were compared with a radiologist's segmentation and found to generally agree within 10%, the typical range of inter-rater radiologist agreement.
Starting Page	2936
Ending Page	2943
File Size	557587
Page Count	8
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571596
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Image segmentation Magnetic resonance Clustering algorithms Humans Fuzzy sets Knowledge based systems Magnetic heads Coils Brain modeling white matter MRI fuzzy c-means knowledge-based system brain segmentation CSF gray matter
Content Type	Text
Resource Type	Article

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