Active contour model for inhomogenous image segmentation based on Jeffreys divergence

Abstract

Inhomogenous image segmentation has been a research challenge in recent years. To deal with this difficulty, we propose a new local and global active contour model based on Jeffreys divergence. First, unlike the local data fitting energy of the region-scalable fitting model, a new local data fitting energy based on Jeffreys divergence is proposed instead of Euclidean distance, which achieves relatively better segmentation. Second, to improve the versatility of the model, a new global data fitting energy based on Jeffreys divergence is proposed. Finally, the adaptive weights of the local and global data fitting energies are developed to increase the robustness to the initial curve. Experiments on real-world and medical images with inhomogeneities indicate that the proposed model can obtain accurate segmentation results efficiently and is not strictly dependent on setting up initial curves.