Mutual Information-Based Methods to Improve Local Region-of-Interest Image Registration

摘要

Current methods of multimodal image registration usually seek to maximize the similarity measure of mutual information (MI) between two images over their region of overlap. In applications such as planned radiation therapy, a diagnostician is more concerned with registration over specific regions of interest (ROI) than registration of the global image space. Registration of the ROI can be unreliable because the typically small regions have limited statistics and thus poor estimates of entropies. We examine methods to improve ROI-based registration by using information from the global image space.