Two-stage image segmentation based on nonconvex l2 - lp approximation and thresholding

Image segmentation is of great importance in image processing. In this paper, we propose a two-stage image segmentation strategy based on the nonconvex l(2) - l(p) approximation of the Mumford-Shah (MS) model, where we use the nonconvex l(p) (0 < p < 1) regularizer to approximate the Hausdorff measure and to extract more boundary information. In the first stage, we solve the nonconvex variant of the MS model efficiently via the split-Bregman algorithm. Moreover, we use a closed-form p-shrinkage operator to deal with the l(p) quasi-norm subproblem, which is easy to implement. The second stage is segmenting the u obtained in the first stage into different phases with thresholds determined by the K-means clustering method. We compare our method with several state-of-the-art methods both qualitatively and quantitatively to demonstrate the effectiveness and advantages of our strategy. (C) 2021 Elsevier Inc. All rights reserved.