Incorporating spatial information into entropy estimates to improve multimodal image registration

摘要

We propose an approach to incorporate spatial information into the estimate of the entropy of an image by partitioning a high-dimensional space into regions over which a one-dimensional histogram can be indexed. This approach obviates the need for the construction of high-dimensional histograms and makes no assumptions on the form of the high-dimensional probability mass function. We use this partitioned estimate of entropy to compute image similarity measures, useful in image registration, and we examine the convergence properties of the registration process.