MŽtodo DialŽtico de Busca e Otimiza�‹o para An‡lise de Imagens de Resson‰ncia MagnŽtica

MŽtodo DialŽtico de Busca e Otimiza�‹o para An‡lise de Imagens de Resson‰ncia MagnŽticaMultispectral image analysis is a relatively promising field of research with applications in several areas, such as remote sensing and medical imaging. Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence. However, Philosophy appears to be still considered a sort of enigmatic knowledge, despite the power of generalization and the systematic

Multispectral image analysis is a relatively promising field of research with applications in several areas, such as remote sensing and medical imaging. Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence. However, Philosophy appears to be still considered a sort of enigmatic knowledge, despite the power of generalization and the systematic nature of investigative methods like dialectics. Here we claim that Philosophy can be also considered as a source of inspiration. In this work we propose a new non-supervised classifier based on dialectics as defined by Hegel's works and the philosophical school of the Philosophy of Praxis, to classify synthetic multispectral magnetic resonance images. This work proved that such a classifier can reach results as good as those obtained by Kohonen's self-organized networks.