A pattern recognition approach to enhancing structures in 3D CT data

In medical image processing, several attempts have been made to develop filters which enhance certain structures in 3D data based on analysis of the Hessian matrix. These filters also tend to respond to other structures, e.g. most vessel enhancement filters also enhance nodule-like objects. In this paper, we use pattern recognition techniques to design more optimal filters. The essential difference with previous approaches is that we provide a system with examples of what it should enhance and suppress. These examples are used to train a classifier that determines the probability that a voxel in an unseen image belongs to the desired structures. The advantages of such an approach are excellent performance and flexibility: it can be used for any structure by providing the appropriate examples. We evaluated our approach on enhancing pulmonary fissures, which appear as plate-like structures in 3D CT chest scans. We compared our approach to the results of a recently proposed fissure enhancement filter. The results show that both methods are able to enhance the fissures, but our approach shows better performance; the areas under the ROC curves are 0.9044 and 0.7650, respectively.