Automatic characteristic-calibrated registration (ACC-REG): Hippocampal surface registration using eigen-graphs. (July 2020)

• Record Type: Journal Article
• Authors: CHAN, Hei Long; YAM, Tsz Chun; LUI, Lok Ming
• Is Part Of: Pattern recognition. Volume 103(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Feature extraction -- Feature correspondence -- Surface registration -- Surface deformation -- Eigen-graph; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107142
• Languages: English
• ISSNs: 0031-3203

Abstract

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.