Active contours driven by weighted region-scalable fitting energy based on local entropy
In this paper, we present a scheme of improvement on the region-scalable fitting (RSF) model proposed by Li et al. (Minimization of region-scalable fitting energy for image segmentation, IEEE Transactions on Image Processing 17(10) (2008) 19401949) in terms of robustness to initialization and noise. First, the Gaussian kernel for the RSF energy is replaced with a mollifying kernel with compact support. Second, the RSF energy is redefined as a weighted energy integral, where the weight is local entropy deriving from a grey level distribution of image. The total energy functional is then incorporated into a variational level set formulation with two extra internal energy terms. The new RSF model not only handles better intensity inhomogeneity, but also allows for more flexible initialization and more robustness to noise compared to the original RSF model.
local entropy image segmentation intensity inhomogeneity active contour model level set method variational method