A Two-Stage Image Segmentation Model for Multi-Channel Images (Communications in Computational Physics, 2018)
This paper introduces a two-stage model for multi-channel image segmentation,which is motivated by minimal surface theory. Indeed, in the first stage, we acquirea smooth solution u from a convex variational model related to minimal surfaceproperty and different data fidelity terms are considered. This minimization problemis solved efficiently by the classical primal-dual approach. In the second stage, weadopt thresholding to segment the smoothed image u. Here, instead of using K-meansto determine the thresholds, we propose a more stable hill-climbing procedure to locatethe peaks on the 3D histogram of u as thresholds, in the meantime, this algorithmcan also detect the number of segments. Finally, numerical results demonstrate thatthe proposed method is very robust against noise and superior to other image segmentationapproaches.