Relative Entropy Pre-Fitting Model for Noisy and Intensity Inhomogeneous Image Segmentation

Abstract

This paper reports an improved Active Contour Model (ACM) for image segmentation. Despite the significant development of the ACMs, their performances for the noisy and intensity-inhomogeneous images are still deficient. To tackle the intensity-inhomogeneity and noises in segmentation, new construction of local pre-fitting for evolving active contours is proposed. Two locally pre-fitted images are constructed from the local intensity estimation, and the relative entropy measures are used to define the local energy functional that provides statistical information of these images with the original image. Thereby, the desired contour evolution of the proposed model is expedited, and its noise-immunity is increased. The proposed model has demonstrated more initialization robustness, faster contour evolution and higher segmentation accuracy over the prominent ACMs for all the test images both with and without noise and intensity-inhomogeneity.