NDLI: An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis

Content Provider	IEEE Xplore Digital Library
Author	Akselrod-Ballin, A. Galun, M. Basri, R. Brandt, A. Gomori, M.J. Filippi, M. Valsasina, P.
Copyright Year	2006
Description	Author affiliation: Weizmann Institute of Science, Rehovot, Israel (Akselrod-Ballin, A.)
Abstract	We present a novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detecting multiple sclerosis lesions in 3D MRI data. Our method uses segmentation to obtain a hierarchical decomposition of a multi-channel, anisotropic MRI scan. It then produces a rich set of features describing the segments in terms of intensity, shape, location, and neighborhood relations. These features are then fed into a decision tree-based classifier, trained with data labeled by experts, enabling the detection of lesions in all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments showing successful detections of lesions in both simulated and real MR images.
Starting Page	1122
Ending Page	1129
File Size	710903
Page Count	8
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiple sclerosis Image segmentation Lesions Brain Magnetic resonance imaging Biomedical imaging Anisotropic magnetoresistance Shape Decision trees Classification tree analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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