Texture classification based on the Boolean model and its application to HEp-2 cells

We investigated the Boolean model for the classification of textures. We were interested in three issues: 1. What are the best features for classification? 2. How does the number of Boolean models created from the original image influence the accuracy of the classifier? 3. Is decision tree induction the right method for classification? We are working on a real-world application which is the classification of HEp-2 cells. This kind of cells are used in medicine for the identification of antinuclear autoantibodies. Human experts describe the characteristics of these cells by symbolic texture features. We apply the Boolean model to this problem and assume that the primary grains are regions of random size and shape. We use decision tree induction in order to learn the relevant classification knowledge and the structure of the classifier.