Variational B-spline level-set: a linear filtering approach for fast deformable model evolution.
IEEE Trans Image Process
; 18(6): 1179-91, 2009 Jun.
Article
em En
| MEDLINE
| ID: mdl-19403364
ABSTRACT
In the field of image segmentation, most level-set-based active-contour approaches take advantage of a discrete representation of the associated implicit function. We present in this paper a different formulation where the implicit function is modeled as a continuous parametric function expressed on a B-spline basis. Starting from the active-contour energy functional, we show that this formulation allows us to compute 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-spline coefficients. 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 1-D convolutions, which yields an efficient algorithm. As a further consequence, each step of the level-set evolution may be interpreted as a filtering operation with a B-spline kernel. Such filtering induces an intrinsic smoothing in the algorithm, which can be controlled explicitly via the degree and the scale of the chosen B-spline kernel. We illustrate the behavior of this approach on simulated as well as experimental images from various fields.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
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Processamento de Sinais Assistido por Computador
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Modelos Estatísticos
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article