An efficient level set method based on global statistical information for image segmentation

Abstract

In this paper, an improved region-based active contour model in a novel variational level set formulation employing global information constraints is proposed for image segmentation. Using the image global characteristics, the proposed model can accurately segment objects with weak or blurred boundaries in the presence of noise or intensity inhomogeneity and achieve robustness against different kinds of images. We present a global intensity fitting energy functional based on the differences between the original image intensity and the global intensity means. This energy is then integrated into a geodesic variational level set formulation, from which a curve evolution equation is derived for energy minimization. The level set function is regularized by the Gaussian filter which keeps it smooth and eliminates the reinitialization. Experimental results on different images demonstrate the performance of our method in terms of accuracy and robustness. Compared with the well-known active contour models, our method is less sensitive to the initial contour and more computationally efficient.