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dc.contributor.authorLankton, Shawn
dc.contributor.authorNain, Delphine
dc.contributor.authorYezzi, Anthony
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2009-06-24T19:08:37Z
dc.date.available2009-06-24T19:08:37Z
dc.date.issued2007-02-18
dc.identifier.citationShawn Lankton, Delphine Nain, Anthony Yezzi, and Allen Tannenbaum, "Hybrid geodesic region-based curve evolutions for image segmentation," Medical Imaging 2007: Physics of Medical Imaging, Jiang Hsieh, Michael J. Flynn, Editors, Proc. of SPIE Vol. 6510, 65104U, (2007)en
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/1853/28604
dc.description©2007 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.709700en
dc.descriptionMedical imaging 2007: Physics of medical imaging, 18-22 February 2007, San Diego, California, USA.
dc.descriptionDOI:10.1117/12.709700
dc.description.abstractIn this paper we present a gradient descent flow based on a novel energy functional that is capable of producing robust and accurate segmentations of medical images. This flow is a hybridization of local geodesic active contours and more global region-based active contours. The combination of these two methods allows curves deforming under this energy to find only significant local minima and delineate object borders despite noise, poor edge information, and heterogeneous intensity profiles. To accomplish this, we construct a cost function that is evaluated along the evolving curve. In this cost, the value at each point on the curve is based on the analysis of interior and exterior means in a local neighborhood around that point. We also demonstrate a novel mathematical derivation used to implement this and other similar flows. Results for this algorithm are compared to standard techniques using medical and synthetic images to demonstrate the proposed method's robustness and accuracy as compared to both edge-based and region-based alone.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectAlgorithmsen
dc.titleHybrid geodesic region-based curve evolutions for image segmentationen
dc.typeProceedingsen
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.publisher.originalSociety of Photo-Optical Instrumentation Engineers


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