| Popis: |
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. |
