Abstract
This study investigates a new non-rigid registration method based on Huber prior. The goal of image registration is to find a biologically plausible transformation which results in the spatial alignment of structurally or functionally corresponding regions in the two images. In order to improve the computing efficiency and registration precision, we used B-spline based free-form deformation (FFD) model. Our innovation is using Huber prior as penalty term of the energy function for better results. The FFD model with Huber prior which has high accuracy and strong robustness can settle the over-smoothing and edge information deficiency. We applied the proposed algorithm to both simulated data and real data registrations. Distinctly, the experiment results show that our method gets better results compared to conventional FFD registration algorithms.
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