Efficient combined SSIM- and landmark-driven image registration in a variational framework

Abstract

Image registration methods are used to establish geometrical correspondences between different datasets. Various characteristics of image data can be exploited to drive image registration algorithms. Thus, the currently available schemes can be roughly divided into two classes: intensitybased and feature-based registration schemes. In this paper, we present a mathematical framework, based on the calculus of variations, for combining these two classes in order to benefit from the advantages of both strategies. The goal is to obtain a registration algorithm which achieves a good matching of datasets near landmark locations but also away from them (by matching the corresponding intensities). The proposed approach includes the novel formulation of a disparity term which simultaneously takes into account the structural similarity index (a similarity measure which considers spatial dependencies in the images) and the location of outstanding points. Since the iteration which results of the variational formulation is translated into the frequency domain, the implementation of the proposed algorithm offers a good speed-performance tradeoff when compared to other state-of-the-art image registration implementations. Experimental results show the advantages, in the medical setting, of the combined SSIMand landmark-based approach over well-established registration techniques which use either landmark or intensity information alone. In particular, the registration of triple-phase 3D computed tomographies of the liver under injection of a contrast agent has been chosen for such a comparison. The datasets are acquired at different times depending on the arrival time of the contrast agent in arteries, portal and hepatic veins, so they have to be registered in order to show the liver structures acquired at each phase in a common framework. These multi-phase studies provide tumor enhancement on the arterial and portal venous phases that support differential diagnosis of lesions in the liver.

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