NDLI: An A Contrario Approach for Change Detection in 3D Multimodal Images: Application to Multiple Sclerosis in MRI

Content Provider	IEEE Xplore Digital Library
Author	Rousseau, F. Faisan, S. Heitz, F. Armspach, J.P. Chevalier, Y. Blanc, F. de Seze, J. Rumbach, L.
Copyright Year	2007
Description	Author affiliation: CNRS, Illkirch (Rousseau, F.; Faisan, S.; Heitz, F.)
Abstract	Estimating significant changes between two images remains a challenging problem in medical image processing. This paper proposes a non-parametric region based method to detect significant changes in 3D multimodal Magnetic Resonance (MR) sequences. The proposed approach relies on an a contrario model which defines significant changes as events with very low probability. We adapt the a contrario framework to deal with multimodal images from which are extracted measures related to intensity and volume changes. Two fusion rules are carefully designed to handle a set of decision thresholds and a set of image measures. The final decision is taken using multiple testing procedures. The efficiency of the algorithm is demonstrated in the context of multiple sclerosis (MS) lesion analysis over time in multimodal MR sequences. We evaluate the proposed method on synthetic images using the Brainweb simulator. Finally, promising results on multimodal sequences on clinical data are presented.
Starting Page	2069
Ending Page	2072
File Size	693512
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4352728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiple sclerosis Magnetic resonance imaging Biomedical image processing Magnetic resonance Volume measurement Testing Lesions Image sequence analysis Algorithm design and analysis Brain modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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