Local Region Statistics-Based Active Contour Model for Medical Image Segmentation

Abstract

This paper presents a novel active contour model for simultaneous segmentation and bias field estimation of medical images. Based on the additive model of images with intensity in homogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire domain to give a global criterion. In a level set formulation, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity in homogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.