NDLI: Tracking distributions with an overlap prior

Content Provider	IEEE Xplore Digital Library
Author	Ben Ayed, I. Shuo Li Ross, I.
Copyright Year	2008
Description	Author affiliation: GE Healthcare, London, ON (Ben Ayed, I.; Shuo Li)
Abstract	Recent studies have shown that embedding similarity/dissimilarity measures between distributions in the variational level set framework can lead to effective object segmentation/tracking algorithms. In this connection, existing methods assume implicitly that the overlap between the distributions of image data within the object and its background has to be minimal. Unfortunately, such assumption may not be valid in many important applications. This study investigates an overlap prior, which embeds knowledge about the overlap between the distributions of the object and the background in level set tracking. It consists of evolving a curve to delineate the target object in the current frame. The level set curve evolution equation is sought following the maximization of a functional containing three terms: (1) an original overlap prior which measures the conformity of overlap between the nonparametric (kernel-based) distributions within the object and the background to a learned description, (2) a term which measures the similarity between a model distribution of the object and the sample distribution inside the curve, and (3) a regularization term for smooth segmentation boundaries. The Bhattacharyya coefficient is used as an overlap measure. Apart from leading to a method which is more versatile than current ones, the overlap prior speeds up significantly the curve evolution. Comparisons and results demonstrate the advantages of the proposed prior over related methods, and its usefulness in important applications such as the left ventricle tracking in magnetic resonance (MR) images.
Starting Page	1
Ending Page	7
File Size	410922
Page Count	7
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587428
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Level set Image segmentation Photometry Target tracking Object segmentation Equations Biomedical imaging Medical services Magnetic resonance Image sequences
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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