A Three-Stage Variational Image Segmentation Framework Incorporating Intensity Inhomogeneity Information
Li Xu, Xiaoping Yang, Tieyong Zeng · SIAM Journal on Imaging Sciences · 2020
In this paper, we propose a new three-stage segmentation framework based on a convex variant of the Mumford--Shah model and the intensity inhomogeneity information of an image. The first stage in our framework is to perform a dimension lifting method. An intensity inhomogeneity image is added as an additional channel, which results in a vector-valued image. In the second stage, a convex variant of the Mumford--Shah model is applied to each channel of the vector-valued image to obtain a smooth approximation. We use the semi--proximal alternating direction method of multipliers (sPADMM) to solve this model and prove that the sPADMM for solving this convex model has Q-linear convergence rate. In the last stage, we apply a thresholding method to the smoothed vector-valued image to get the final segmentation. Experiments demonstrate clearly that the proposed methods can provide more accurate segmentation results in comparison with five state-of-the-art methods including a deep learning approach.