Gradient based optimization of an EMST image registration function

• Published in: Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 Vol. 2; pp. ii/253 - ii/256 Vol. 2
• Main Authors: Sabuncu, M.R., Ramadge, P.J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Application software; Concurrent computing; Convergence; Entropy; Histograms; Image processing; Image registration; Military computing; Tree graphs; Zinc
• ISBN: 9780780388741; 0780388747
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2005.1415389

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.