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Journal Articles Pattern Recognition Year : 2016

Rigid Image Registration by General Adaptive Neighborhood Matching

Abstract

This paper aims to propose a new feature and intensity-based image registration method. The proposed approach is based on the block matching algorithm : a displacement field is locally computed by matching spatially-invariant intensity sub-blocks of the images before performing an optimization algorithm from this vector field to estimate the transformation. Our approach proposes a new way to calculate the displacement field by matching spatially-variant sub-blocks of the images, called General Adaptive Neighborhoods (GANs). These neighborhoods are adaptive with respect to both the intensities and the spatial structures of the image. They represent the patterns within the grayscale images. This paper also presents a consistent shape metric used to match the GANs. The performed qualitative and quantitative experiments show that the proposed GAN matching method provides accurate displacement fields enabling to perform image rigid registration, even for data from different modalities, that outperforms the classical block matching algorithm with respect to robustness and accuracy criteria.
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Dates and versions

emse-01274011 , version 1 (26-09-2019)

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Johan Debayle, Benoît Presles. Rigid Image Registration by General Adaptive Neighborhood Matching. Pattern Recognition, 2016, 55, pp.45-57. ⟨10.1016/j.patcog.2016.01.024⟩. ⟨emse-01274011⟩
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