Image sequence segmentation combining global labeling and local relabeling and its application to materials science images

Jarrell W. Waggoner; Jeff Simmons; Song Wang

doi:10.1117/12.906471

8 February 2012 Image sequence segmentation combining global labeling and local relabeling and its application to materials science images

Jarrell W. Waggoner, Jeff Simmons, Song Wang

Author Affiliations +

Proceedings Volume 8296, Computational Imaging X; 829606 (2012) https://doi.org/10.1117/12.906471
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

Jarrell W. Waggoner, Jeff Simmons, and Song Wang "Image sequence segmentation combining global labeling and local relabeling and its application to materials science images", Proc. SPIE 8296, Computational Imaging X, 829606 (8 February 2012); https://doi.org/10.1117/12.906471

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

3D image processing

Materials science

Medical imaging

Titanium

Image processing algorithms and systems

Video

Show All Keywords

Keywords/Phrases

Search In:

Publication Years