  The materialist dialectical method is a philosophical investigative method to
analyze aspects of reality. These aspects are viewed as complex processes
composed by basic units named poles, which interact with each other. Dialectics
has experienced considerable progress in the 19th century, with Hegel's
dialectics and, in the 20th century, with the works of Marx, Engels, and
Gramsci, in Philosophy and Economics. The movement of poles through their
contradictions is viewed as a dynamic process with intertwined phases of
evolution and revolutionary crisis. In order to build a computational process
based on dialectics, the interaction between poles can be modeled using fuzzy
membership functions. Based on this assumption, we introduce the Objective
Dialectical Classifier (ODC), a non-supervised map for classification based on
materialist dialectics and designed as an extension of fuzzy c-means
classifier. As a case study, we used ODC to classify 181 magnetic resonance
synthetic multispectral images composed by proton density, $T_1$- and
$T_2$-weighted synthetic brain images. Comparing ODC to k-means, fuzzy c-means,
and Kohonen's self-organized maps, concerning with image fidelity indexes as
estimatives of quantization distortion, we proved that ODC can reach almost the
same quantization performance as optimal non-supervised classifiers like
Kohonen's self-organized maps.
