Abstract
In this paper, we propose a novel approach for estimating image similarity. This measure is of importance in assessing image correspondence or image alignment and plays an important role in image registration. Currently, this problem is approached rather one-dimensionally since most registration methods consider the problem as either mono- or multi-modal. This perspective leads to the selection of some form of either the correlation coefficient (CC) or mutual information (MI) as image similarity measure (ISM). We propose a more generic framework for ISM construction, based on absolute joint moments, which can be considered as a generalization of CC. Within this framework, we propose a specific ISM that provides a different trade-off between MI and CC in terms of performance and computational cost for general registration problems. To illustrate this, we compared CC and MI with the proposed ISM and performed extensive experiments with regard to accuracy, robustness and speed. The evaluation demonstrated that the proposed absolute joint moments is a good combination of properties of CC and MI, with respect to speed and performance.
Highlights
Image registration is an optimization process that utilizes a similarity measure to find the optimal alignment of two images
The experiments show that absolute joint moment (AJM) is robust to noise, fairly robust to contrast inhomogeneities, more robust than CC and less robust than mutual information (MI) for non-linear intensity distortion
The robustness test showed that MI256 in not very robust to noise
Summary
Image registration is an optimization process that utilizes a similarity measure to find the optimal alignment of two images. The selection of an image similarity measure, especially in the case of medical image registration, is usually reduced down to the question whether a multi- or mono-modal registration is required. This black-and-white perspective leads to well-known answers and results in the selection of correlation coefficient (CC) for mono-modal registration and mutual information (MI) for multi-modal registration. Since most mono-modal image registration problems assume this type of functional relationship between images, CC was considered a dedicated approach for this type of problems and is often the first choice
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