Optimization of Topological Active Nets with Differential Evolution

The Topological Active Netmodel for image segmentation is a deformable model that integrates features of region–based and boundary– based segmentation techniques. The segmentation process turns into a minimization task of the energy functions which control the model deformation. We used Differential Evolution as an alternative evolutionary method that minimizes the decisions of the designer with respect to other evolutionarymethods such as genetic algorithms. Moreover, we hybridized Differential Evolution with a greedy search to integrate the advantages of global and local searches at the same time that the segmentation speed is improved.