A new level set method with global alternating minimization algorithm for image segmentation

Abstract

Due to adverse factors such as varying illumination, noise, and imaging artifacts, achieving fine-grained image segmentation of objects remains a significant challenge. To address this, we propose a level set method based on global alternating minimization. Specifically, a total variation (TV) regularization term weighted by a gradient-based edge indicator function is incorporated into a convex energy functional, enhancing the model’s ability to detect weak edges. Subsequently, an efficient segmentation framework is constructed based on the Alternating Direction Method of Multipliers (ADMM), providing a closed-form solution that improves both numerical stability and convergence speed. By adopting a convex optimization scheme, the proposed model eliminates explicit time-step dependence, thereby improving adaptability and flexibility in the temporal domain. Experimental results demonstrate that the proposed method possesses a global minimization property and consistently outperforms state-of-the-art segmentation models on publicly available datasets. Notably, compared to the Segment Anything Model (SAM), the proposed method reduces the maximum CT measurement error of the ball-plate standard by 65.66 %.