A variant of the level set method and applications to image segmentation

In this paper we propose a variant of the level set formulation for identifying curves separating regions into different phases. In classical level set approaches, the sign of level set functions are utilized to identify up to 2n phases. The novelty in our approach is to introduce a piecewise const...

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Main Authors: Lie, Johan, Lysaker, Marius, Tai, Xue Cheng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2009
Subjects:
Online Access:https://hdl.handle.net/10356/91401
http://hdl.handle.net/10220/4604
http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:ISI_WOS_XML&id=doi:&genre=&isbn=&issn=0025-5718&date=2006&volume=75&issue=255&spage=1155&epage=1174&aulast=Lie&aufirst=%20J&auinit=J&title=MATHEMATICS%20OF%20COMPUTATION&atitle=A%20variant%20of%20the%20level%20set%20method%20and%20applications%20to%20image%20segmentation
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spelling sg-ntu-dr.10356-914012023-02-28T19:37:17Z A variant of the level set method and applications to image segmentation Lie, Johan Lysaker, Marius Tai, Xue Cheng School of Physical and Mathematical Sciences Norwegian Research Council DRNTU::Science::Mathematics::Analysis In this paper we propose a variant of the level set formulation for identifying curves separating regions into different phases. In classical level set approaches, the sign of level set functions are utilized to identify up to 2n phases. The novelty in our approach is to introduce a piecewise constant level set function and use each constant value to represent a unique phase. If phases should be identified, the level set function must approach 2n predetermined constants. We just need one level set function to represent 2n unique phases, and this gains in storage capacity. Further, the reinitializing procedure requested in classical level set methods is superfluous using our approach. The minimization functional for our approach is locally convex and differentiable and thus avoids some of the problems with the nondifferentiability of the Delta and Heaviside functions. Numerical examples are given, and we also compare our method with related approaches. Published version 2009-05-12T08:44:23Z 2019-12-06T18:05:01Z 2009-05-12T08:44:23Z 2019-12-06T18:05:01Z 2006 2006 Journal Article Lie, J., Lysaker, M., & Tai, X. C. (2006). A variant of the level set method and applications to image segmentation. Mathematics of Computation, 75(255), 1155-1174. 0025-5718 https://hdl.handle.net/10356/91401 http://hdl.handle.net/10220/4604 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:ISI_WOS_XML&id=doi:&genre=&isbn=&issn=0025-5718&date=2006&volume=75&issue=255&spage=1155&epage=1174&aulast=Lie&aufirst=%20J&auinit=J&title=MATHEMATICS%20OF%20COMPUTATION&atitle=A%20variant%20of%20the%20level%20set%20method%20and%20applications%20to%20image%20segmentation 10.1090/S0025-5718-06-01835-7 en Mathematics of Computation. Mathematics of Computation @ copyright 2006 American Mathematical Society. The journal's website is located at http://www.ams.org/mcom/2006-75-255/S0025-5718-06-01835-7/home.html. 20 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Mathematics::Analysis
spellingShingle DRNTU::Science::Mathematics::Analysis
Lie, Johan
Lysaker, Marius
Tai, Xue Cheng
A variant of the level set method and applications to image segmentation
description In this paper we propose a variant of the level set formulation for identifying curves separating regions into different phases. In classical level set approaches, the sign of level set functions are utilized to identify up to 2n phases. The novelty in our approach is to introduce a piecewise constant level set function and use each constant value to represent a unique phase. If phases should be identified, the level set function must approach 2n predetermined constants. We just need one level set function to represent 2n unique phases, and this gains in storage capacity. Further, the reinitializing procedure requested in classical level set methods is superfluous using our approach. The minimization functional for our approach is locally convex and differentiable and thus avoids some of the problems with the nondifferentiability of the Delta and Heaviside functions. Numerical examples are given, and we also compare our method with related approaches.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Lie, Johan
Lysaker, Marius
Tai, Xue Cheng
format Article
author Lie, Johan
Lysaker, Marius
Tai, Xue Cheng
author_sort Lie, Johan
title A variant of the level set method and applications to image segmentation
title_short A variant of the level set method and applications to image segmentation
title_full A variant of the level set method and applications to image segmentation
title_fullStr A variant of the level set method and applications to image segmentation
title_full_unstemmed A variant of the level set method and applications to image segmentation
title_sort variant of the level set method and applications to image segmentation
publishDate 2009
url https://hdl.handle.net/10356/91401
http://hdl.handle.net/10220/4604
http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:ISI_WOS_XML&id=doi:&genre=&isbn=&issn=0025-5718&date=2006&volume=75&issue=255&spage=1155&epage=1174&aulast=Lie&aufirst=%20J&auinit=J&title=MATHEMATICS%20OF%20COMPUTATION&atitle=A%20variant%20of%20the%20level%20set%20method%20and%20applications%20to%20image%20segmentation
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