The registration of intraoperative ultrasound (US) images with pre- operative magnetic resonance (MR) images is a challenging problem due to the di erence of information contained in each image modality. To overcome this di culty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imag- ing, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hypere- chogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually effcient, (ii) produces no statistically di erent registration accuracy com- pared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use.