Hierarchical Multimodal Image Registration Based on Adaptive Local Mutual Information

摘要：

We propose a new, adaptive local measure based on gradient orientation similarity for the purposes of multimodal image registration. We embed this metric into a hierarchical registration framework, where we show that registration robustness and accuracy can be improved by adapting both the similarity metric and the pixel selection strategy to the Gaussian blurring scale and to the modalities being registered. A computationally efficient estimation of gradient orientations is proposed based on patch-wise rigidity.We have applied our method to both rigid and nonrigidmultimodal registration taskswith differentmodalities.Our approach outperforms mutual information (MI) and previously proposed local approximations of MI for multimodal (e.g. CT/MRI) brain image registration tasks. Furthermore, it shows significant improvements in terms of mTRE over standard methods in the highly challenging clinical context of registering pre-operative brain MRI to intra-operative US images.

关键词： multimodal image registration image-guided neurosurgery

作者: Dante De Nigris Laurence Mercier Rolando Del Maestro D.Louis Collins Tal Arbel

作者单位: McGill University, Centre for Intelligent Machines McGill University, Dept.of Biomedical Engineering Montreal Neurological Institute and Hospital, McGill University

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 643-651

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）