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dc.contributor.authorMory, Benoit
dc.contributor.authorArdon, Roberto
dc.contributor.authorYezzi, Anthony
dc.contributor.authorThiran, Jean-Philippe
dc.date.accessioned2013-10-07T16:05:32Z
dc.date.available2013-10-07T16:05:32Z
dc.date.issued2009-09
dc.identifier.citationMory, B.; Ardon, R.; Yezzi, A.J.; & Thiran, J. (2009). "Non-Euclidean Image-Adaptive Radial Basis Functions for 3D Interactive Segmentation". Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV 2009), September-October 2009, pp.787-794.en_US
dc.identifier.urihttp://hdl.handle.net/1853/49180
dc.description© 2009 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.descriptionDOI: 10.1109/ICCV.2009.5459245
dc.description.abstractIn the context of variational image segmentation, we propose a new finite-dimensional implicit surface representation. The key idea is to span a subset of implicit functions with linear combinations of spatially-localized kernels that follow image features. This is achieved by replacing the Euclidean distance in conventional Radial Basis Functions with non-Euclidean, image-dependent distances. For the minimization of an objective region-based criterion, this representation yields more accurate results with fewer control points than its Euclidean counterpart. If the user positions these control points, the non-Euclidean distance enables to further specify our localized kernels for a target object in the image. Moreover, an intuitive control of the result of the segmentation is obtained by casting inside/outside labels as linear inequality constraints. Finally, we discuss several algorithmic aspects needed for a responsive interactive workflow. We have applied this framework to 3D medical imaging and built a real-time prototype with which the segmentation of whole organs is only a few clicks away.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectControl pointsen_US
dc.subjectImage segmentationen_US
dc.subjectNon-Euclidean distancesen_US
dc.subjectRadial basis functionsen_US
dc.titleNon-Euclidean Image-Adaptive Radial Basis Functions for 3D Interactive Segmentationen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenamePhilips Healthcare. Medisys Research Laboratoryen_US
dc.contributor.corporatenameÉcole polytechnique fédérale de Lausanneen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ICCV.2009.5459245
dc.embargo.termsnullen_US


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