Accurate alignment of histopathological whole slide images (WSIs) across different staining modalities is essential for comprehensive multimodal tissue analysis, yet remains challenging due to complex deformations, sectioning artefacts, and staining variations. This paper presents an efficient registration framework that effectively addresses these challenges through a progressive multi-stage approach. Our approach integrates efficient tissue segmentation using Florence2-SAM2 with a progressive three-stage registration strategy. Initial coarse alignment utilises tissue mask centroids and geometric transformations to establish preliminary correspondence. Subsequently, the accelerated feature extraction module (XFeat) identifies and matches distinctive tissue landmarks from the roughly aligned images, significantly reducing the computational burden of the final stage. The registration concludes with fine alignment using diffusive regulariser based iterative optimisation. Comprehensive evaluation on the ANHIR, ACROBAT and HyReCo datasets demonstrates that our method achieves superior registration accuracy (med-TRE 6.01  \(\upmu \) m for restained and med-TRE 65.5  \(\upmu \) m for consecutive sections) and computational efficiency (average runtime of 17 s) compared to state-of-the-art approaches. The proposed framework enables precise spatial correlation of histological features in differently stained modalities, facilitating advanced analysis for both diagnostic and research applications.