The Gabor-based tensor level set method for multiregional image segmentation

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

The Gabor-based tensor level set method for multiregional image segmentation

Wang, B Gao, X Tao, D

Li, X Li, J

Permalink

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. 987 - 994
Issue Date:: 2009-09-28

Closed Access

	Filename	Description	Size
	2011001271OK.pdf		427.56 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, B	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Li, J	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. 987 - 994	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/10453/17296
dc.description.abstract	This paper represents a new level set method for multiregional image segmentation. It employs the Gabor filter bank to extract local geometrical features and builds the pixel tensor representation whose dimensionality is reduced by using the offline tensor analysis. Then multiphase level set functions are evolved in the tensor field to detect the boundaries of the corresponding image. The proposed method has three main advantages as follows. Firstly, employing the Gabor filter bank, the model is more robust against the salt-and-pepper noise. Secondly, the pixel tensor representation comprehensively depicts the information of pixels, which results in a better performance on the non-homogenous image segmentation. Thirdly, the model provides a uniform equation for multiphase level set functions to make it more practical. We apply the proposed method to synthetic and medical images respectively, and the results indicate that the proposed method is superior to the typical region-based level set method. © 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_120	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	The Gabor-based tensor level set method for multiregional image 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
dc.location.activity	Valencia, SPAIN
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:

This paper represents a new level set method for multiregional image segmentation. It employs the Gabor filter bank to extract local geometrical features and builds the pixel tensor representation whose dimensionality is reduced by using the offline tensor analysis. Then multiphase level set functions are evolved in the tensor field to detect the boundaries of the corresponding image. The proposed method has three main advantages as follows. Firstly, employing the Gabor filter bank, the model is more robust against the salt-and-pepper noise. Secondly, the pixel tensor representation comprehensively depicts the information of pixels, which results in a better performance on the non-homogenous image segmentation. Thirdly, the model provides a uniform equation for multiphase level set functions to make it more practical. We apply the proposed method to synthetic and medical images respectively, and the results indicate that the proposed method is superior to the typical region-based level set method. © 2009 Springer Berlin Heidelberg.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/17296