Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information

doi:10.11947/j.AGCS.2018.20170368

Abstract

Abstract: Multimodal image registration method based on feature matching can't satisfy the demands of pixel level registration precision.This paper proposes a multimodal image registration algorithm considering grayscale and gradient information.The nonparametric registration model based on Markov random field (MRF) makes full use of the image information of multimodal image to measure the similarity which considers the grayscale and the gradient statistical information are considered,and the value space is discretized to improve the convergence speed.The algorithm is validated both qualitatively and quantitatively demonstrating its potentials on three groups of multimodal image registration experiments.The result indicates that the proposed algorithm is superior to the polynomial model registration based on manual selection and the multimodal image registration only with gray information only.At the same time,this algorithm has some applicability for multimodal image registration of large deformation.In terms of spatial accuracy,the average registration error is less than 1 pixel and the maximum registration error is less than 2 pixels.

Key words: multimodal image, gradient information, Markov random field, discrete optimization, non-parametric registration

CLC Number:

P237

YAN Li, WANG Ziqi, YE Zhiyun. Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 71-81.

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