Non-rigid image registration using electric current flow
In this paper, a novel electric current flow (ECF) based model is proposed to perform feature based non-rigid brain image registration. The ECF features simultaneously capture both voxel intensity and inter-voxel distance information. In the proposed ECF framework, each voxel is regarded as exhibiting electric potential proportional to voxel intensity. Voxels are connected by conductive wires in a pairwise manner. Each conductive wire has resistance, in which the resistance value is proportional to the length of the wire. The electric potential difference among connected pixels induces electric current passing through their connected wire. The amount of the electric current is the ratio between the voxel potential difference and the wire resistance. The potential difference and resistance are respectively proportional to the voxel intensity difference and the inter-voxel distance. By analyzing the electric current induced by the connection between a reference voxel and its counterparts in a given range, the ECF algorithm searches for the most salient connection to construct the ECF features. The ECF features are incorporated in the Markov random field labeling framework for non-rigid image registration. The registration quality of the proposed method has been evaluated intensively on both BrainWeb and IBSR databases. It is compared with four related approaches. Experimental results illustrate that the proposed method consistently achieves the highest registration accuracy among all the compared methods on both databases.