Rough sets in identification of cellular automata for medical image processing

In this paper a method is proposed which enables identification of cellular automata (CA) that extract low-level features in medical images. The CA identification problem includes determination of neighbourhood and transition rule on the basis of training images. The proposed solution uses data mining techniques based on rough sets theory. Neighbourhood is detected by reducts calculations and rule-learning algorithms are applied to induce transition rules for CA. Experiments were performed to explore the possibility of CA identification for boundary detection, convex hull transformation and skeletonization of binary images. The experimental results show that the proposed approach allows finding CA rules that are useful for extraction of specific features in microscopic images of blood specimens.