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...

• Type: Journal
• Year: 2009
• Where: ICCV
• Authors: Benoit Mory, Roberto Ardon, Anthony J. Yezzi, Jean-Philippe Thiran
• Keywords: Computer Vision, Control Points, Finite-dimensional Implicit Surface, ICCV 2009, Variational Image Segmentation