Image Segmentation via Mean Curvature Regularized Mumford-Shah Model and Thresholding

摘要

Due to the limitations in imaging devices and subject-induced susceptibility effect, general image segmentation is still an open problem. Typical challenges include image noise, intensity inhomogeneity and various image modalities. In this paper, we propose to use a two-step strategy. Specifically, we first utilize a mean curvature regularized Mumford-Shah model to recover an intermediate image with enhanced saliency, and then the segmentation is obtained by a thresholding procedure. For images with intensity inhomogeneity, a bias-corrected fuzzy K-means method is used to correct the bias field before K-means thresholding. The proposed model can be minimized efficiently using the augmented Lagrangian algorithm. Experimental results and comparison analysis demonstrate that the proposed framework is not only able to preserve the geometry of object shapes, especially object corners, but it is also more accurate than state-of-the-art methods.