A unified tensor level set method for image segmentation

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

A unified tensor level set method for image segmentation

Gao, X Wang, B Tao, D

Li, X

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Publication Type:: Chapter
Citation:: 2011, 346 pp. 217 - 238
Issue Date:: 2011-05-09

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Wang, B	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2011-05-09	en_US
dc.identifier.citation	2011, 346 pp. 217 - 238	en_US
dc.identifier.isbn	9783642195501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118280
dc.description.abstract	This paper presents a new unified level set model for multiple regional image segmentation. This model builds a unified tensor representation for comprehensively depicting each pixel in the image to be segmented, by which the image aligns itself with a tensor field composed of the elements in form of high order tensor. Then the multi-phase level set functions are evolved in this tensor field by introducing a new weighted distance function. When the evolution converges, the tensor field is partitioned, and meanwhile the image is segmented. The proposed model has following main advantages. Firstly, the unified tensor representation integrates the information from Gaussian smoothed image, which results the model is robust against noise, especially the salt and pepper noise. Secondly, the local geometric features involved into the unified representation increase the weight of boundaries in energy functional, which makes the model more easily to detect the edges in the image and obtain better performance on non-homogenous images. Thirdly, the model offers a general formula for energy functional which can deal with the data type varying from scalar to vector then to tensor, and this formula also unifies single and multi-phase level set methods. We applied the proposed method to synthetic, medical and natural images respectively and obtained promising performance. © 2011 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-642-19551-8_7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A unified tensor level set method for image segmentation	en_US
dc.type	Chapter
utslib.citation.volume	346	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	346	en_US

Abstract:

This paper presents a new unified level set model for multiple regional image segmentation. This model builds a unified tensor representation for comprehensively depicting each pixel in the image to be segmented, by which the image aligns itself with a tensor field composed of the elements in form of high order tensor. Then the multi-phase level set functions are evolved in this tensor field by introducing a new weighted distance function. When the evolution converges, the tensor field is partitioned, and meanwhile the image is segmented. The proposed model has following main advantages. Firstly, the unified tensor representation integrates the information from Gaussian smoothed image, which results the model is robust against noise, especially the salt and pepper noise. Secondly, the local geometric features involved into the unified representation increase the weight of boundaries in energy functional, which makes the model more easily to detect the edges in the image and obtain better performance on non-homogenous images. Thirdly, the model offers a general formula for energy functional which can deal with the data type varying from scalar to vector then to tensor, and this formula also unifies single and multi-phase level set methods. We applied the proposed method to synthetic, medical and natural images respectively and obtained promising performance. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/118280