A New Weighted Relative Entropy Pre-Fitting for Active Contour based Image Segmentation

The Active Contour Model (ACM) has demonstrated its promises for image segmentation. For developing an ACM, a new construction of local fitting energy with a weighted relative entropy is reported in this paper. With a level set method based on local pre-fitted images and local-dispersion based scaling, a weighted energy functional is formulated. To improve the performance of segmentation, the new energy functional relies on the relative entropy to include local similarity estimations for better curve evolution. Locally pre-fitted images are constructed to provide initialization robustness, noise endurance and to reduce computational complexity. Besides, the energy is scaled with the local dispersion to infuse edge mapping in the energy functional for improving accuracy and reducing the effect of inhomogeneity. The proposed model has thereby demonstrated the improvement in initialization, curve evolution, time efficiency, computational complexity, and noise endurance.

I. Introduction

The Active Contour Model (ACM) was introduced as ‘Snakes' by Kass et al. [1] as such an initialized contour slithers within an input image by the feature-driven forces to iteratively capture the image object(s). ACMs are widely studied for image segmentation [2] to attain faster curve evolution, noise tolerance, time efficiency, initialization robustness, which created their two major classes: edge-based [3] and region-based [4]–[9]. Edge-based models depend on gradient based edge stopping function (ESF), while the region-based models use certain region descriptor to guide the evolving contour and are relatively robust to initialization and noise.