A non-rigid image registration method based on multi-level B-spline and L2-regularization
To solve the problem that the cost function of the classic free-form deformation (FFD) cannot simulate transformation field of images with large elastic deformation or local distortion in image registration better, and to increase the registration accuracy and robustness, a new non-rigid image registration method based on the classic hierarchical FFD is proposed. Since the smooth term has a significant influence on registration accuracy, and its coefficient is not easy to be controlled in the classic hierarchical B-spline based FFD, a L2-regularization term with faster and more stable optimization is introduced in the cost function of the proposed model. By coordinating the coefficients of this regularization term and the smooth term, this novel L2-regularized FFD model is able to solve the problem of low registration accuracy caused by strong smooth constraint while maintaining the images topologies. The introduced L2-regularization term can impose a spatial constraint on the control lattices transformation field, and the over-registration problem can be suppressed to a certain extent, so it can register the images with local large distortion. A series of registration experiments of natural images and medical images show that the new method has an obvious advantage over the classic model in registration accuracy measured by mean square error.
KeywordsNon-rigid image registration B-spline Free-form deformation L2-norm
This work was supported by National Natural Science Foundation of China (Nos. 81371635 and 81671848), Key Research and Development Project of Shandong Province (No. 2016GGX101017), and Research Fund for the Doctoral Program of Higher Education of China (20120131110062).
- 1.Zitov, Barbara, Flusser, Jan: Image registration methods: a survey. Image Vis. Comput. 21(11), 9771000 (2003)Google Scholar
- 2.David, G.L.: Distinctive Image Features from Scale-Invariant Keypoints. Kluwer, Dordrecht (2004)Google Scholar
- 8.Jia, D.: The Research of Non-rigid Registration Algorithm Based on Image Characteristics and Optical Flow. Master Thesis, ShanDong UniversityGoogle Scholar
- 9.Pock, T., et al.: A duality based algorithm for TV- L, 1-optical-flow image registration. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, pp. 511–518. Springer, Berlin, Heidelberg (2007)Google Scholar
- 10.Sun, D., Qiu, Z.: A new non-rigid image matching algorithm using thin-plate spline. Acta Electron. Sin. 30(8), 1104–1107 (2002)Google Scholar
- 16.Schnabel, J.A., Rueckert, D.: A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 573–581 (2001)Google Scholar
- 19.Sun, D., Roth, S., Michael, J.B.: Secrets of optical flow estimation and their principles. In: Computer Vision and Pattern Recognition, pp. 2432–2439 (2010)Google Scholar
- 25.Vishnevskiy, V., Gass, T.: Isotropic total variation regularization of displacements in parametric image registration. IEEE Trans. Med. Imaging PP(99), 1-1 (2016)Google Scholar