Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation

Kim, Junmo; Fisher, John W., III; Çetin, Müjdat; Yezzi, Anthony; Willsky, Alan S.

Title:

Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation

dc.contributor.author	Kim, Junmo
dc.contributor.author	Fisher, John W., III
dc.contributor.author	Çetin, Müjdat
dc.contributor.author	Yezzi, Anthony
dc.contributor.author	Willsky, Alan S.
dc.contributor.corporatename	Georgia Institute of Technology. School of Electrical and Computer Engineering	en_US
dc.contributor.corporatename	Massachusetts Institute of Technology. Laboratory for Information and Decision Systems	en_US
dc.date.accessioned	2013-09-09T17:46:07Z
dc.date.available	2013-09-09T17:46:07Z
dc.date.issued	2003-09
dc.description	© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.description	Presented at the 2003 IEEE International Conference on Image Processing (ICIP 2003), 14-18 September 2000, Barcelona, Spain.
dc.description	DOI: 10.1109/ICIP.2003.1246765
dc.description.abstract	An information-theoretic method for multiphase image segmentation, in an active contour-based framework is proposed. Our approach is based on nonparametric density estimates, and is able to solve problems involving arbitrary probability densities for the region intensities. This is achieved by maximizing the mutual information between the region labels and the image pixel intensities, in order to segment up to 2m regions using m curves. The method does not require any prior training regarding the regions of interest, but rather learns the probability densities during the evolution process. We present some illustrative experimental results, demonstrating the power of the proposed segmentation approach.	en_US
dc.embargo.terms	null	en_US
dc.identifier.citation	Kim, J.; Fisher, J.W.; Cetin, M.; Yezzi, A., Jr.; & Willsky, A.S. (2003). "Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation". Proceedings of the 2003 International Conference on Image Processing (ICIP 2003), Vol. 2, (September 2003), pp.II-655-8 Vol. 3.	en_US
dc.identifier.doi	10.1109/ICIP.2003.1246765
dc.identifier.isbn	0-7803-7750-8
dc.identifier.issn	1522-4880
dc.identifier.uri	http://hdl.handle.net/1853/48861
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Density estimates	en_US
dc.subject	Evolution	en_US
dc.subject	Image segmentation	en_US
dc.subject	Information-theoretic method	en_US
dc.title	Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation	en_US
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Yezzi, Anthony
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
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