[A reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images].

In the field of medical image registration, corresponding landmarks can serve as an crucial reference for the quantitative evaluation of registration algorithm errors. However, the corresponding landmarks extracted by existing methods generally lack anatomical correspondence. While some approaches have begun to take bifurcation points of tubular structures as landmarks, the establishment of accurate one-to-one correspondences between these points is hindered by highly similar local features and missing correspondences, thereby impeding effective quantification of registration errors. To address these limitations, this paper proposes a reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images. First, the bifurcation points of tubular structures with anatomical correspondence are extracted as landmarks. Secondly, to address the issues of highly similar local features and missing correspondences, a reweighted graph matching model is constructed base on a local similarity strategy and a modality independent neighborhood descriptor, which iteratively identifies correct matches and rectifies mismatches to progressively refine the correspondence between landmarks. Finally, the