Automatic characteristic-calibrated registration (ACC-REG): Hippocampal surface registration using eigen-graphs. (July 2020) - CHAN, Hei Long; YAM, Tsz Chun; LUI, Lok Ming
Highlights: Automatic intrinsic feature extraction on hippocampal mesh surface. Feature calibration by Eigen-graphs to improve consistency and accuracy of the feature correspondences on different subjects. Fast and accurate surface registration is available in just 5 s with minimal RMSE. Quantitative comparison with other existing methods demonstrates the effectiveness and efficiency of the proposed model on real data from the ADNI database. Abstract: In this paper, we propose an efficient algorithm, the ACC-REG, to automatically extract intrinsic key characteristics on hippocampal mesh surfaces and hence compute an accurate registration mapping between them. Given a pair of hippocampal surface mesh, the proposed algorithm constructs the eigen-graphs, an intrinsic feature on the surface, on each surface as its representative. The eigen-graphs are then calibrated along the longitudinal direction of the hippocampal surfaces. Accurately corresponded intrinsic characteristics on each hippocampus can thus be extracted. As a result, the two surfaces can be registered with improved accuracy and low computation cost. Experiments on ADNI data demonstrate the effectiveness of the proposed ACC-REG model over existing methods.