Active Contours Driven by Global and Local Data Fitting Energy
Résumé
In this paper, a new region-based active contour model is proposed for image segmentation. By considering the image local and global characteristics, we define an energy functional with three terms, i.e., global term, local term and regularization term. The energy functional is then incorporated into a variational level set formulation with a level set regularization term. Due to using the local and global intensity information, the images with intensity inhomogeneity can be segmented accurately and efficiently. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Our method has been validated on synthetic images, real images and medical images. Experimental results show desirable performances of our method in terms of accuracy and robustness compared with some well-known active contour models.