Supervoxel classification forests for estimating pairwise image correspondences

Abstract

This article presents a general method for estimating pairwise image correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxel-wise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling, which is then regularised using majority voting within the boundaries of the target's supervoxels. This yields semi-dense correspondences in a fully automatic, unsupervised, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as atlas/patch-based segmentation, registration, and atlas construction. We demonstrate the effectiveness of our approach in two different applications: a) initialisation of longitudinal registration on spine CT data of 96 patients, and b) atlas-based image segmentation using 150 abdominal CT images. Comparison to state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.