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Tracking monotonically advancing boundaries in image sequences using graph cuts and recursive kernel shape priors.

Authors
Type
Published Article
Journal
IEEE Transactions on Medical Imaging
1558-254X
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Volume
31
Issue
5
Pages
1008–1020
Identifiers
DOI: 10.1109/TMI.2011.2178122
PMID: 22156978
Source
Medline
License
Unknown

Abstract

We introduce a probabilistic computer vision technique to track monotonically advancing boundaries of objects within image sequences. Our method incorporates a novel technique for including statistical prior shape information into graph-cut based segmentation, with the aid of a majorization-minimization algorithm. Extension of segmentation from single images to image sequences then follows naturally using sequential Bayesian estimation. Our methodology is applied to two unrelated sets of real biomedical imaging data, and a set of synthetic images. Our results are shown to be superior to manual segmentation.

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