IEEE Trans Image Process - Distance regularized level set evolution and its application to image segmentation.

Tópicos

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Resumo

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.

Resumo Limpo

level set method wide use imag process comput vision convent level set formul level set function typic develop irregular evolut may caus numer error eventu destroy stabil evolut therefor numer remedi call reiniti typic appli period replac degrad level set function sign distanc function howev practic reiniti rais serious problem perform also affect numer accuraci undesir way paper propos new variat level set formul regular level set function intrins maintain level set evolut level set evolut deriv gradient flow minim energi function distanc regular term extern energi drive motion zero level set toward desir locat distanc regular term defin potenti function deriv level set evolut uniqu forwardandbackward fab diffus effect abl maintain desir shape level set function particular sign distanc profil near zero level set yield new type level set evolut call distanc regular level set evolut drlse distanc regular effect elimin need reiniti therebi avoid induc numer error contrast complic implement convent level set formul simpler effici finit differ scheme can use implement drlse formul drlse also allow use general effici initi level set function numer implement relat larg time step can use finit differ scheme reduc number iter ensur suffici numer accuraci demonstr effect drlse formul appli edgebas activ contour model imag segment provid simpl narrowband implement great reduc comput cost

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