Name
Abstract | ||
|---|---|---|
In the field of image segmentation, most of level-set-based active contour approaches are based on a discrete representation of the associated implicit function. We present in this paper a different formulation where the level-set is modelled as a continuous parametric function expressed on a B-spline basis. Starting from the Mumford-Shah energy functional, we show that this formulation allows computing the solution as a restriction of the variational problem on the space spanned by the B-splines. As a consequence, the minimization of the functional is directly obtained in terms of the B-splines parameters. We also show that each step of this minimization may be expressed through a convolution operation. Because the B-spline functions are separable, this convolution may in turn be performed as a sequence of simple 1D convolutions, which yields a very efficient algorithm. The behaviour of this approach is illustrated on biomedical images from various fields. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISBI.2008.4540961 | 2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 |
Keywords | Field | DocType |
level-set, B-spline, variational methods | Spline (mathematics),B-spline,Mathematical analysis,Computer science,Level set,Image segmentation,Artificial intelligence,Energy functional,Active contour model,Pattern recognition,Convolution,Algorithm,Implicit function | Conference |
ISSN | Citations | PageRank |
1945-7928 | 6 | 0.44 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Bernard | 1 | 690 | 63.59 |
| Denis Friboulet | 2 | 403 | 32.65 |
| P Thévenaz | 3 | 439 | 37.17 |
| M Unser | 4 | 4335 | 499.89 |