Adaptive grid generation based non-rigid image registration using mutual information for breast MRI

In this paper a new approach for non-rigid image registration using mutual information is introduced. A fast parametric method for non-rigid registration is developed by adjusting divergence and curl of an intermediate vector field from which the deformation field is computed using finite-central difference method. Mutual information is newly employed as the similarity measure in the gradient-based cost minimization (or mutual information maximization) of the existing registration framework. The huge amount of data associated with MRI is handled by a fully automated multi-resolution scheme. The adaptive grid system naturally distributes more grids to deprived areas. The positive monitor function disallows grid folding and provides a mean to control the ratio of the areas between the original and transformed domain. The flexibility of the adaptive grid allocation could dramatically reduce processing time with quality preserved. Mutual information facilitates robust registration between different image modalities. Different types of joint histogram estimation are compared and integrated with the system. This scheme is applied on dynamic contrast-enhanced breast MRI, which requires the registration algorithm to be non-rigid, contrast-enhanced features preserving. Preliminary experiments show promising results and great potential for future extension.