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14 February 2012Multi-objective optimization for deformable image registration: proof of concept
In this work we develop and study a methodology for deformable image registration that overcomes a drawback
of optimization procedures in common deformable image registration approaches: the use of a single combination
of different objectives. Because selecting the best combination is well-known to be non-trivial, we use a multi-objective
optimization approach that computes and presents multiple outcomes (a so-called Pareto front) at once.
The approach is inherently more powerful because not all Pareto-optimal outcomes are necessarily obtainable
by running existing approaches multiple times, for different combinations. Furthermore, expert knowledge can
be easily incorporated in making the final best-possible decision by simply looking at (a diverse selection of)
the outcomes illustrating both the transformed image and the associated deformation vector field. At the basis
of the optimization methodology lies an advanced, model-based evolutionary algorithm that aims to exploit
features of a problem's structure in a principled manner via probabilistic modeling. Two objectives are defined:
1) maximization of intensity similarity (normalized mutual information) and 2) minimization of energy required
to accomplish the transformation (a model based on Hooke's law that incorporates elasticity characteristics
associated with different tissue types). A regular grid of points forms the basis of the transformation model.
Interpolation extends the correspondence as found for the grid to the rest of the volume. As a proof of concept we
performed tests on a 2D axial slice of a CT scan of a breast. Results indicate plausible behavior of the proposed
methodology that innovatively combines intensity-based and model-based registration criteria with state-of-the-art
adaptive computation techniques for multi-objective optimization in deformable image registration.
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Tanja Alderliesten, Jan-Jakob Sonke, Peter A. N. Bosman, "Multi-objective optimization for deformable image registration: proof of concept," Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831420 (14 February 2012); https://doi.org/10.1117/12.911268