Multi-modal brain image registration based on subset definition and manifold-to-manifold distance

Abstract

Image registration is an important procedure in multi-modal brain image processing. The main challenge is the variations of intensity distributions in different image modalities. The efficient SSD based method cannot handle this kind of variations. And other approaches based on modality independent descriptors and metrics are usually time-consuming. In this article, we propose a novel similarity metric based on manifold-to-manifold distance imposed on the subset of original images. We define a subset for a compact representation of the original image. Manifold learning technique is employed to reveal the intrinsic structure of the sampled data. Instead of comparing the images in the original feature space, we use the manifold-to-manifold distance to measure the difference. By minimizing the distance between the manifolds, we iteratively obtain the optimal registration of the original image pair. Experiment results show that our approach is effective to deal with the multi-modal image registration on the BrainWeb dataset.