Fast two-stage segmentation model for images with intensity inhomogeneity

摘要

Based on the local correntropy-based K-means clustering active contour model, this paper proposes a fast two-stage segmentation method for intensity inhomogeneous images. Under our framework, the segmentation process is split into two stages. In the first stage, we preliminary segment the down-sampled images by the proposed relaxed anisotropic-isotropic local correntropy-based K-means clustering (AILCK) model, which can obtain a coarse segmentation result quickly. Subsequently, in the second stage, we further segment original images by an improved AILCK model, where we use the up-sampled coarse contour obtained by the first stage as the initialization. Following it, to obtain the global minima of energy functions fast, we incorporate a weighted difference of anisotropic and isotropic total variations into relaxed formulation of the two-stage active contour models. And then, we minimize them utilizing the difference-of-convex algorithm and the primal-dual hybrid gradient method. The experimental results on synthetic and real-world images demonstrate that the proposed method can achieve accurate segmentation results for intensity inhomogeneous images in a fast way.