Gradient Based Optimization of an EMST Image Registration Function
Mert Rory Sabuncu, Peter J. Ramadge · 2006
This paper examines the problem of registering images using an information theoretic metric (e.g., entropy) estimated using a Euclidean minimum spanning tree (EMST). The objective is to find an extremum of the metric with respect to a vector of free parameters. One of the major difficulties posed by such graph theoretic metrics is concurrently obtaining gradient information as the metric is computed. Obtaining the gradient is a first step in efficiently optimizing the metric. Our main contribution is to show how to obtain a gradient-based descent direction from the computation of the EMST metric. We also indicate how this can be used for optimizing image registration over a vector set of parameters and provide some preliminary experimental results.