Mining interesting association rules in medical images

Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Mining association rules in medical images is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we extend the concept of association rule based on object and image in medical images, and propose two algorithms to discover frequent item-sets and mine interesting association rules from medical images. We describe how to incorporate the domain knowledge into the algorithms to enhance the interestingness. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.