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ICCV
2009
IEEE

Non-Euclidean image-adaptive Radial Basis Functions for 3D interactive segmentation

8 years 3 months ago
Non-Euclidean image-adaptive Radial Basis Functions for 3D interactive segmentation
In 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 medic...
Benoit Mory, Roberto Ardon, Anthony J. Yezzi, Jean
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCV
Authors Benoit Mory, Roberto Ardon, Anthony J. Yezzi, Jean-Philippe Thiran
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