ABSTRACT
This chapter presents a broad overview of rough sets theory and a review of the current literature on rough sets-based approaches to various problems in medical imaging, such as medical image segmentation and object extraction, feature extraction/reduction, and image classification. Rough
representation of the region of interest and rough image entropy are discussed. Hybridized rough set frameworks with other computational intelligence techniques, including neural networks, particle swarm optimization, genetic algorithms, support vector machines, and fuzzy sets, are presented. Challenges to be addressed and future directions of research are also presented.
