Local refinement for 3D deformable parametric surfaces
Autor: | Michael Unser, Anais Badoual, Daniel Schmitter |
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Rok vydání: | 2016 |
Předmět: |
Surface (mathematics)
Mathematical optimization deformable model Refinable function segmentation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Degrees of freedom (statistics) Basis function 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences local refinement Parametric surface Convergence (routing) refinable function 0202 electrical engineering electronic engineering information engineering Brain segmentation parametric surface splines 020201 artificial intelligence & image processing Segmentation 0101 mathematics Algorithm Mathematics |
Zdroj: | ICIP |
Popis: | Biomedical image segmentation is an active field of research where deformable models have proved to be efficient. The geometric representation of such models determines their ability to approximate the shape of interest as well as the speed of convergence of related optimization algorithms. We present a new tensor-product parameterization of surfaces that offers the possibility of local refinement. The goal is to allocate additional degrees of freedom to the surface only where an increase in local detail is required. We introduce the possibility of locally increasing the number of control points by inserting basis functions at specific locations. Our approach is generic and relies on refinable functions which satisfy the refinement relation. We show that the proposed method improves brain segmentation in 3D MRI images. |
Databáze: | OpenAIRE |
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