Quad-tree Based Entropy Estimator for Fast and Robust Brain Image Registration

摘要

The performances of information-theoretic multi-modality image registration methods crucially depend on the model representing the joint density function of the co-occurring image intensities and on the implementation of the entropy estimator. We proposed an entropy estimator for image registration based on quad-tree (QT) that is essentially an entropic graph entropy estimator, but can be adapted to work as a plug-in entropy estimator. This duality was achieved by incorporating the Hilbert kernel density estimator. Results of 3-D rigid-body registration of multi-modal brain volumes indicate that the proposed methods achieve similar accuracies as the registration method based on minimal spanning tree (MST), but have a higher success rate and a higher capture range. Although the MST and QT have similar computational complexities, the QT-based methods had about 50% shorter registration times.