NDLI: Automated detection of Focal Cortical Dysplasia lesions on T1-weighted MRI using volume-based distributional features

Content Provider	IEEE Xplore Digital Library
Author	Chin-Ann Yang Kaveh, M. Erickson, B.J.
Copyright Year	2011
Description	Author affiliation: Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, United States (Erickson, B.J.) \|\| Electrical and Computer Engineering, University of Minnesota, Twin Cities, Minneapolis, United States (Chin-Ann Yang; Kaveh, M.)
Abstract	A new procedure is proposed for the automated detection of Focal Cortical Dysplasia (FCD) lesions on T1-weighted MRIs using volume-based discriminative features. Statistical features are obtained from of a set of neighboring voxels without using any computation that requires hard labeling of grey matter and white matter tissues. The significance of the proposed features is quantitatively evaluated with a Naive Bayes probabilistic approach, which is used for classification, and experiments are conducted on a total of 21 subjects with FCD lesions. The experimental results indicate that using the proposed features can achieve better detection rate and lower false positive rate for the FCD lesions compared to the widely used Antel's features [1].
Starting Page	865
Ending Page	870
File Size	338996
Page Count	6
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872541
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Feature extraction Magnetic resonance imaging Brain modeling Probabilistic logic Thickness measurement Computational modeling blurriness focal cortical dysplasia MRI detection cortical thickness
Content Type	Text
Resource Type	Article

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