Deformable medical image registration based on multi-level transformation progressive and image enhancement

Abstract Medical image registration is a critical problem in medical image analysis, enabling the spatial alignment of anatomical structures across different imaging modalities. However, existing algorithms often struggle with local registration of large deformations and exhibit limited feature extraction capabilities. To address these challenges, we propose a multi-level transformation progressive registration algorithm (MTPR). This method incorporates the concept of multi-level transformations, the model performs four progressive registration steps, predicting the deformation field from coarse to fine. Initially, the model applies an enhancement process, introducing a hybrid filtering enhancement module based on wavelet transform and improved guided filtering to enhance image edges. During the registration phase, we propose a pyramid-shared weight enhancement network, which precisely extracts multimodal image features and implements a progressive deformation field prediction strategy. In order to increase the feature extraction capability of the model, we propose spatial feature fusion module in skip connection of encoder and decoder, which combines multi-scale information into a spatial feature representation with rich context information. Additionally, we introduce a dual-similarity metric to enhance model capability for local organ registration by incorporating structural similarity, increasing the model’s attention to local organs. Experiments conducted on publicly available datasets (OASIS, LPBA40) and clinical CT/MR data, achieved an average dice similarity coefficient (DSC) of 0.822, average average symmetric surface distance (ASSD) of 0.741 mm, average standard deviation of jacobian determinant (Std.Jacobian) of 0.247 in the clinical CT/MR data. The Wilcoxon signed-rank test statistical analysis shows that the evaluation indicators of the MTPR algorithm are significantly better than other baseline methods (P < 0.05). The MTPR model’s multi-scale information aggregation capability effectively handles large deformations, demonstrating excellent registration accuracy and generalization performance.