@inproceedings{3b8d8700ebd24c47b62264a82a594a79,
title = "Gradient intensity: A new mutual information-based registration method",
abstract = "Conventional mutual information (MI)-based registration using pixel intensities is time-consuming and ignores spatial information, which can lead to misalignment. We propose a method to overcome these limitation by acquiring initial estimates of transformation parameters. We introduce the concept of 'gradient intensity' as a measure of spatial strength of an image in a given direction. We determine the rotation parameter by maximizing the MI between gradient intensity histograms. Calculation of the gradient intensity MI function is extremely efficient. Our method is designed to be invariant to scale and translation between the images. We then obtain estimates of scale and translation parameters using methods based on the centroids of gradient images. The estimated parameters are used to initialize an optimization algorithm which is designed to converge more quickly than the standard Powell algorithm in close proximity of the minimum. Experiments show that our method significantly improves the performance of the registration task and reduces the overall computational complexity by an order of magnitude.",
author = "Ramtin Shams and Parastoo Sadeghi and Kennedy, {Rodney A.}",
year = "2007",
doi = "10.1109/CVPR.2007.383425",
language = "English",
isbn = "1424411807",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
booktitle = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07",
note = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 ; Conference date: 17-06-2007 Through 22-06-2007",
}