Surface Registration Using Textured Point Clouds and Mutual Information

Abstract

A new inter-modality surface registration algorithm that uses textured point clouds and mutual information is presented within the context of model-updated image guided procedures. The algorithm has been developed to capitalize on the information generated by a laser range scanner. The current iteration of the algorithm is optimized for cortical surface registration. Intra-modality validation for the algorithm is provided in both physical and imaging phantoms. The physical phantom is generated using a laser range scanner that reports texture coordinates. The imaging phantom is generated from gadolinium enhanced MR volumes of the brain. Simulated inter-modality registration experiments on a cortical surface are also presented. Results of the experiments show successful registration accuracies on the order of the resolution of the surfaces (i.e. submillimetric). The results demonstrate that the registration algorithm and laser range scanner have potential application in deformation tracking during surgery and model-updated image-guided procedures.