Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

Abstract

The active contour model (ACM) is a popular approach for image segmentation. Many existing ACMs perform poorly in severe inhomogeneous images. To address this issue, a novel local and global ACM (LaG_ACM) is proposed in this paper. First, we define a global fitting image formulation that encodes the global property of an image and a global energy term using the relative entropy between the original image and the proposed global fitting image formulation. Then, a local image bias field formulation is defined to extract the local image information and to estimate the bias field. By integrating the proposed local image bias field formulation with the ACM, we specify a local energy term using the mean squared error to accommodate severe inhomogeneous images. More importantly, we define an adaptive weighting function using image entropy, which can automatically adjust the weight between the local and global energy terms according to the degree of intensity inhomogeneity. Finally, the experimental results on images with different degrees of intensity inhomogeneity validate the favorable performance of the LaG_ACM.