Evolutionary multiobjective optimization of Topological Active Nets

In this work we used the evolutionary multiobjective optimization methodology for the optimization of Topological Active Nets. This is a deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. When the minimization task is performed by means of a greedy local search or a global search method, an experimental tuning of the energy parameters is needed to obtain a correct segmentation. This tuning must be done for each kind of image.Evolutionary multiobjective optimization gives a solution to this problem by considering the optimization of several objectives in parallel. We used the SPEA2 algorithm, adapted to our application, to the search of the Pareto optimal individuals. We tested the improvements and problems between the uses of the multiobjective optimization technique versus the use of a genetic algorithm and a greedy local search in our application of the optimization of the Topological Active Nets deformable model. We used several representative examples with images from different medical domains.