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Proceedings Paper

Image sequence segmentation combining global labeling and local relabeling and its application to materials science images
Author(s): Jarrell W. Waggoner; Jeff Simmons; Song Wang
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Paper Abstract

Accurately segmenting a series of 2D serial-sectioned images for multiple, contiguous 3D structures has important applications in medical image processing, video sequence analysis, and materials science image segmentation. While 2D structure topology is largely consistent across consecutive serial sections, it may vary locally because a 3D structure of interest may not span the entire 2D sequence. In this paper, we develop a new approach to address this challenging problem by considering both the global consistency and possible local inconsistency of the 2D structural topology. In this approach, we repeatedly propagate a 2D segmentation from one slice to another, and we formulate each step of this propagation as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Specifically, we divide the optimal labeling into two steps: a global labeling that enforces topology consistency, and a local labeling that identifies possible topology inconsistency. We justify the effectiveness of the proposed approach by using it to segment a sequence of serial-section microscopic images of an alloy widely used in material sciences and compare its performance against several existing image segmentation methods.

Paper Details

Date Published: 8 February 2012
PDF: 12 pages
Proc. SPIE 8296, Computational Imaging X, 829606 (8 February 2012); doi: 10.1117/12.906471
Show Author Affiliations
Jarrell W. Waggoner, Univ. of South Carolina (United States)
Jeff Simmons, Air Force Research Lab. (United States)
Song Wang, Univ. of South Carolina (United States)


Published in SPIE Proceedings Vol. 8296:
Computational Imaging X
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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