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In this paper, the influence of textural information is studied in two-dimensional electrophoresis gel images. A Genetic Algorithm-based feature selection technique is used in order to select the most representative textural features and reduced the original set (296 feat.) to a more efficient subset. Such a method makes use of a Support Vector Machines classifier. Different experiments have been performed, the pattern set has been divided into two parts (training and validation) extracting a total of 30%, 20% and 0% of the training data, and a 10-fold cross validation is used for validation. In case of extracting 0% means that training set is used for validation. For each division 10 different trials have been done. Experiments have been carried out in order to measure the behaviour of the system and to achieve the most representative textural features for the classification of proteins in two-dimensional gel electrophoresis images. This information can be useful for a protein segmentation process.

A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms