Voxel labelling in CT images with data-driven contextual features
Spatial contextual information is useful for voxel labelling and especially suitable for the images with relatively fixed scene structure such as CT images. For each voxel, the intensity values of nearby and far away positions are sampled as its contextual features and such contextual features have shown promising performance. However how to determine sampling position to construct good contextual features remains a critical problem since a good sampling could significantly improve the classification performance. In this paper we proposed a novel approach by discovering discriminative sampling pattern. We emphasize that the sampling pattern is not hand craft but data driven and can cater to a particular type of problem, such as kidneys labelling in contrast-enhanced CT images. After discriminative pattern is discovered it can be adapted for use in other datasets of the same problem. Experiments on kidney dataset showed considerable improvements over competing methods.