A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis
Zheng Cui, Sasan Mahmoodi, Joy H. Conway, Matthew Guy, Emma B Lewis, Tom Havelock, Michael John Bennett · 2017
Non-rigid registration has been widely used in medical image processing for many years. In order to preserve the anatomical topology and perform the registration more realistically and reliably for image guided surgery, methods based on statistical deformation model have been receiving considerable interests. However, the shortcomings in previous work such as the empirically configured weighting parameter for the statistical term lead to a controversial and unrealistic alignment. Therefore, a non-parametric method based on statistical deformation model is proposed here to avoid the discussion of weighting parameter. Our novel method is developed through incorporating the statistical model into two indispensable terms: similarity metric and smoothing regularizer. The advantages of the proposed algorithm in terms of convergence rate and registration accuracy have been proved mathematically in methodology and evaluated numerically in experiments compared with the state of the art method. It has also laid a solid foundation for the development of multi-modality image fusion with prior knowledge in the future.