Topological Active Models optimization with Differential Evolution

The Topological Active Model is an active model focused on segmentation tasks. It provides information about the surfaces and the inside of the detected objects in the scene. The segmentation process turns into a minimization task of the energy functions which control the model deformation. In this work we propose a new optimization method of the segmentation model that uses Differential Evolution as an alternative evolutionary method that minimizes the decisions of the designer with respect to others 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. We also included in the local search the possibility of topological changes to perform a better adjustment in complex surfaces, topological changes that introduce the necessary mechanism to divide the mesh in the case of the presence of several objects in the scene.

► A new method for the optimization of the Topological Active Model is proposed. ► This approach is an evolutionary method based on Differential Evolution (DE). ► The DE approach introduces advantages with respect to the classic GA algorithm. ► A hybrid combination of DE with a local greedy search was also developed. ► The hybridized DE introduces the possibility of topological changes for better segmentations in complex images.