Effective two-stage image segmentation: a new non-Lipschitz decomposition approach with convergent algorithm

TL;DR

This paper introduces a novel two-stage image segmentation method that effectively handles intensity inhomogeneity using a non-Lipschitz variational model and a convergent iterative algorithm, improving segmentation accuracy.

Contribution

The paper proposes a new non-Lipschitz variational decomposition model with a globally convergent algorithm for multiphase image segmentation, addressing intensity inhomogeneity issues.

Findings

Effective segmentation of inhomogeneous images

Good convergence and robustness in noisy conditions

Outperforms some state-of-the-art methods

Abstract

Image segmentation is an important median level vision topic. Accurate and efficient multiphase segmentation for images with intensity inhomogeneity is still a great challenge. We present a new two-stage multiphase segmentation method trying to tackle this, where the key is to compute an inhomogeneity-free approximate image. For this, we propose to use a new non-Lipschitz variational decomposition model in the first stage. The minimization problem is solved by an iterative support shrinking algorithm, with a global convergence guarantee and a lower bound theory of the image gradient of the iterative sequence. The latter indicates that the generated approximate image (inhomogeneity-corrected component) is with very neat edges and suitable for the following thresholding operation. In the second stage, the segmentation is done by applying a widely-used simple thresholding technique to the piecewise constant approximation. Numerical experiments indicate good convergence properties and effectiveness of our method in multiphase segmentation for either clean or noisy homogeneous and inhomogeneous images. Both visual and quantitative comparisons with some state-of-the-art approaches demonstrate the performance advantages of our non-Lipschitz based method.