Parametric kernel-driven active contours for image segmentation

Abstract

We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.