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In this paper, a novel texture classfication method from two-dimensional electrophoresis gel image is presented. Such a method makes use of textural features that are reduced to a more compact and efficient subset of characteristics by means of a Genetic Algorithm-based feature selection technique. Then, the selected features are used as inputs for a classfier, in this case a Support Vector Machine. The accuracy of the proposed method is around 94%, and has shown to yield statistically better performances than the textural classification based on the entire feature set. We found that the most decisive and representative features for the textural classficiation of proteins are those related to the second order co-occurrence matrix. This classfication step can be very useful in order to discard over-segmented areas after a protein segmentation or identification process
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.: SABIA :. Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial