Embedded geometric active contour with shape constraint for mass segmentation

Wang, Y; Gao, X; Li, X; Tao, D; Wang, B

Embedded geometric active contour with shape constraint for mass segmentation

Wang, Y Gao, X Li, X Tao, D

Wang, B

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5702 LNCS pp. 995 - 1002
Issue Date:: 2009-09-28

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Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Wang, B	en_US
dc.date.issued	2009-09-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5702 LNCS pp. 995 - 1002	en_US
dc.identifier.isbn	3642037666	en_US
dc.identifier.isbn	9783642037665	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17274
dc.description.abstract	Mass boundary segmentation plays an important role in computer aided diagnosis (CAD) system. Since the shape and boundary are crucial discriminant features in CAD, the active contour methods are more competitive in mass segmentation. However, the general active contour methods are not so effective for some cases, because most masses possess very blurry margin that easily induce the contour leaking. To the end, this paper presents an improved geometric active contour for mass segmentation. It firstly introduces the morphological concentric layer model for automatically initializing. Then an embedded level set is used to extract the adaptive shape constraints. For refining the boundary, a new shape constraint function and stopping function are designed for the enhanced geometric active contour method. The proposed method is tested on real mammograms containing masses, and the results suggest that the proposed method could effectively restrain the contour leaking and get better segmented results than general active contour methods. © 2009 Springer Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-03767-2_121	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Embedded geometric active contour with shape constraint for mass segmentation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5702 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Munster, Germany	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5702 LNCS	en_US

Abstract:

Mass boundary segmentation plays an important role in computer aided diagnosis (CAD) system. Since the shape and boundary are crucial discriminant features in CAD, the active contour methods are more competitive in mass segmentation. However, the general active contour methods are not so effective for some cases, because most masses possess very blurry margin that easily induce the contour leaking. To the end, this paper presents an improved geometric active contour for mass segmentation. It firstly introduces the morphological concentric layer model for automatically initializing. Then an embedded level set is used to extract the adaptive shape constraints. For refining the boundary, a new shape constraint function and stopping function are designed for the enhanced geometric active contour method. The proposed method is tested on real mammograms containing masses, and the results suggest that the proposed method could effectively restrain the contour leaking and get better segmented results than general active contour methods. © 2009 Springer Berlin Heidelberg.

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http://hdl.handle.net/10453/17274