MRI segmentation using dialectical optimization

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T₁- and T₂-weighted synthetic brain images. Comparing our proposal to k-means, fuzzy c-means, and Kohonen´s self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.