A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis

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.

[1]  Anant Madabhushi,et al.  A statistical deformation model (SDM) based regularizer for non-rigid image registration: application to registration of multimodal prostate MRI and histology , 2013, Medical Imaging.

[2]  Albert C. S. Chung,et al.  Multi-modal non-rigid image registration based on similarity and dissimilarity with the prior joint intensity distributions , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[3]  Dinggang Shen,et al.  Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping , 2006, Medical Image Anal..

[4]  Alejandro F Frangi,et al.  Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration , 2003, IEEE Transactions on Medical Imaging.

[5]  Russell H. Taylor,et al.  Integrating Statistical Models of Bone Density into Shape Based 2 D-3 D Registration Framework , 2009 .

[6]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[7]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[8]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[9]  Lawrence H. Staib,et al.  Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data , 2015, IEEE Transactions on Medical Imaging.

[10]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[11]  Daniel Cremers,et al.  Nonparametric Priors on the Space of Joint Intensity Distributions for Non-Rigid Multi-Modal Image Registration , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Thomas Vetter,et al.  A statistical deformation prior for non-rigid image and shape registration , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  David J. Hawkes,et al.  Using statistical deformation models for the registration of multimodal breast images , 2009, Medical Imaging.

[14]  Albert C. S. Chung,et al.  A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya Distances , 2017, Pattern Recognit..

[15]  Jianhua Yao,et al.  Deformable 2 D-3 D Medical Image Registration Using a Statistical Model : Accuracy Factor Assessment , 2012 .