NDLI: Semi-automatic classification of lesion patterns in patients with clinically isolated syndrome

Content Provider	IEEE Xplore Digital Library
Author	Crimi, A. Commowick, O. Ferre, J.C. Maarouf, A. Edan, G. Barillot, C.
Copyright Year	2013
Abstract	Multiple sclerosis (MS) is neuro-degenerative disease of the Central Nervous System characterized by the loss of myelin. A Clinically Isolated Syndrome (CIS) is a first neurological episode caused by inflammation/demyelination in the central nervous system which may lead to MS. Better understanding of the disease at its onset will lead to a better discovery of pathogenic mechanisms, allowing suitable therapies at an early stage. We propose an automatic segmentation algorithm for two different contrast agents, used within a framework for early characterization of CIS patients according to lesion patterns, and more specifically according to the nature of the inflammatory patterns of these lesions. We expect that the proposed framework can infer new prospective figures from the earliest imaging signs of MS since it can provide a classification of different types of lesions across patients. The lesion detection algorithm based on intensity normalization and subtraction of the used MRI data is a pivotal step, since it avoids the time-demanding task of manual delineation.
Sponsorship	IEEE
Starting Page	1102
Ending Page	1105
File Size	696478
Page Count	4
File Format	PDF
ISBN	9781467364560
ISSN	19457928
e-ISBN	9781467364553
DOI	10.1109/ISBI.2013.6556671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Magnetic resonance imaging Diseases Manuals Indexes Vectors Histograms longitudinal Study Segmentation MS USPIO
Content Type	Text
Resource Type	Article

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