An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Keywords Multimodal registration · Atlas · entropy · Discrepancy structures 1 Introduction weighted mutual information  bring spatial informa- tion to improve the registration performance. They intro- Multimodal medical imaging is an effective examination duce the spatial information to resolve the complex texture method in medical diagnosis and computer-aided surgery disturbance. Cross-cumulative residual entropy (CCRE) is . Multimodal images could provide physicians with another information entropy to measure similarity between complementary information of patients’ tissues. However, multimodal images [22, 23]. CCRE is more robust as the multimodal images
Medical & Biological Engineering & Computing – Springer Journals
Published: Jun 4, 2018
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