Region scalable active contour model with global constraint

Existing Active Contour methods suffer from the deficiencies of initialization sensitivity, slow convergence, and being insufficient in the presence of image noise and inhomogeneity. To address these problems, this paper proposes a region scalable active contour model with global constraint (RSGC). The energy function is formulated by incorporating local and global constraints. The local constraint is a region scalable fitting term that draws upon local region information under controllable scales. The global constraint is constructed through estimating the global intensity distribution of image content. Specifically, the global intensity distribution is approximated with a Gaussian Mixture Model (GMM) and estimated by Expectation Maximization (EM) algorithm as a prior. The segmentation process is implemented through optimizing the improved energy function. Comparing with two other representative models, i.e. region-scalable fitting model (RSF) and active contour model without edges (CV), the proposed RSGC model achieves more efficient, stable and precise results on most testing images under the joint actions of local and global constraints.