Robust cross-anatomy image registration leveraging keypoint priors and contour-based assessment

Accurate registration between lateral cephalometric radiographs and facial profile photographs is a prerequisite for integrated craniofacial analysis, yet it remains challenging due to inherent modality gaps and the absence of clinically validated evaluation criteria. This study presents a clinically-driven engineering framework that systematically integrates high-resolution keypoint localization with a weighted Procrustes-based two-dimensional similarity registration. The uniform scale factor accommodates global magnification differences between imaging modalities, while shearing and local non-rigid deformation are excluded. To ensure clinical interpretability, we incorporate expert-defined anatomical priors as weights into the transformation process and introduce an adaptive seed-driven contour tracking (ASDCT) algorithm for precise performance evaluation. Experiments on 198 paired clinical samples demonstrate that our framework achieves stable performance (Contour Distance of 3.52 pixels; Contour Dilation Overlap of 87%) and provides metrics that correlate strongly with expert clinical assessments. By unifying landmark-guided alignment and boundary-consistency evaluation, this framework offers a robust, reproducible, and anatomically reliable solution for cross-modal craniofacial integration, effectively bridging the gap between computational precision and clinical practice.