NDLI: Automatic symmetry-integrated brain injury detection in MRI sequences

Content Provider	IEEE Xplore Digital Library
Author	Yu Sun Bhanu, B. Bhanu, S.
Copyright Year	2009
Description	Author affiliation: Sch. of Med., Univ. of California, San Francisco, CA, USA (Bhanu, S.) \|\| Center for Res. in Intell. Syst., Univ. of California, Riverside, CA, USA (Yu Sun; Bhanu, B.)
Abstract	This paper presents a fully automated symmetry-integrated brain injury detection method for magnetic resonance imaging (MRI) sequences. One of the limitations of current injury detection methods often involves a large amount of training data or a prior model that is only applicable to a limited domain of brain slices, with low computational efficiency and robustness. Our proposed approach can detect injuries from a wide variety of brain images since it makes use of symmetry as a dominant feature, and does not rely on any prior models and training phases. The approach consists of the following steps: (a) symmetry integrated segmentation of brain slices based on symmetry affinity matrix, (b) computation of kurtosis and skewness of symmetry affinity matrix to find potential asymmetric regions, (c) clustering of the pixels in symmetry affinity matrix using a 3D relaxation algorithm, (d) fusion of the results of (b) and (c) to obtain refined asymmetric regions, (e) Gaussian mixture model for unsupervised classification of potential asymmetric regions as the set of regions corresponding to brain injuries. Experimental results are carried out to demonstrate the efficacy of the approach.
Starting Page	79
Ending Page	86
File Size	941201
Page Count	8
File Format	PDF
ISBN	9781424439942
ISSN	21607508
DOI	10.1109/CVPRW.2009.5204052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Training data Image sequence analysis Brain modeling Robustness Data mining Image texture analysis Brain injuries Biomedical imaging
Content Type	Text
Resource Type	Article

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