  Intensity-based image registration approaches rely on similarity measures to
guide the search for geometric correspondences with high affinity between
images. The properties of the used measure are vital for the robustness and
accuracy of the registration. In this study a symmetric, intensity
interpolation-free, affine registration framework based on a combination of
intensity and spatial information is proposed. The excellent performance of the
framework is demonstrated on a combination of synthetic tests, recovering known
transformations in the presence of noise, and real applications in biomedical
and medical image registration, for both 2D and 3D images. The method exhibits
greater robustness and higher accuracy than similarity measures in common use,
when inserted into a standard gradient-based registration framework available
as part of the open source Insight Segmentation and Registration Toolkit (ITK).
The method is also empirically shown to have a low computational cost, making
it practical for real applications. Source code is available.
