|
保拓扑结构的非刚性医学图像配准方法
|
Abstract:
图像配准在图像处理领域发挥着举足轻重的作用,尤其是在医学图像处理领域,需要能保持原器官拓扑结构的非刚性图像配准方法。本文基于类Beltrami系数的保拓扑结构的非刚性医学图像配准方法,提出了新的惩罚函数,使得类Beltrami系数满足
,能更好地保证配准过程中的拓扑结构的保持。同时,我们提出了新的保拓扑结构的非刚性医学图像配准模型,并证明了关键数学性质。最后,我们在多个复杂的实际场景中进行了大量实验,验证了我们方法的优越性。
Image registration plays a crucial role in the field of image processing. Particularly in the area of medical image processing, there is a need for non-rigid image registration methods that can preserve the topological structure of original organs. This paper presents a novel penalty function based on the topological-structure-preserving non-rigid medical image registration method using the Beltrami-like coefficients. By using this penalty function, the Beltrami-like coefficients satisfy the condition
, which can better ensure the preservation of the topological structure during the registration process. Meanwhile, we propose a new topological-structure-preserving non-rigid medical image registration model and prove its key mathematical properties. Finally, we conduct numerous experiments in multiple complex real-world scenarios to verify the superiority of our method.
[1] | Malik, A.S., Boyko, O., Aktar, N. and Young, W.F. (2001) A Comparative Study of MR Imaging Profile of Titanium Pedicle Screws. Acta Radiologica, 42, 291-293. https://doi.org/10.1034/j.1600-0455.2001.042002291.x |
[2] | Modersitzki, J. (2009) FAIR: Flexible Algorithms for Image Registration. Society for Industrial and Applied Mathematics. |
[3] | Sotiras, A., Davatzikos, C. and Paragios, N. (2013) Deformable Medical Image Registration: A Survey. IEEE Transactions on Medical Imaging, 32, 1153-1190. https://doi.org/10.1109/tmi.2013.2265603 |
[4] | Chen, K., Lui, L.M. and Modersitzki, J. (2019) Image and Surface Registration. In: Handbook of Numerical Analysis, Vol. 20, Elsevier, 579-611. https://doi.org/10.1016/bs.hna.2019.07.001 |
[5] | Beg, M.F., Miller, M.I., Trouvé, A. and Younes, L. (2005) Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, 61, 139-157. https://doi.org/10.1023/b:visi.0000043755.93987.aa |
[6] | Droske, M. and Rumpf, M. (2004) A Variational Approach to Nonrigid Morphological Image Registration. SIAM Journal on Applied Mathematics, 64, 668-687. https://doi.org/10.1137/s0036139902419528 |
[7] | Han, H. and Wang, Z. (2020) A Diffeomorphic Image Registration Model with Fractional-Order Regularization and Cauchy-Riemann Constraint. SIAM Journal on Imaging Sciences, 13, 1240-1271. https://doi.org/10.1137/19m1260621 |
[8] | Haber, E. and Modersitzki, J. (2006) Intensity Gradient Based Registration and Fusion of Multi-Modal Images. In: Larsen, R., Nielsen, M. and Sporring, J., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2006. Lecture Notes in Computer Science, Springer, 726-733. https://doi.org/10.1007/11866763_89 |
[9] | Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. and Suetens, P. (1997) Multimodality Image Registration by Maximization of Mutual Information. IEEE Transactions on Medical Imaging, 16, 187-198. https://doi.org/10.1109/42.563664 |
[10] | Broit, C. (1981) Optimal Registration of Deformed Images. University of Pennsylvania. |
[11] | Christensen, G.E., Rabbitt, R.D. and Miller, M.I. (1996) Deformable Templates Using Large Deformation Kinematics. IEEE Transactions on Image Processing, 5, 1435-1447. https://doi.org/10.1109/83.536892 |
[12] | Fischer, B. and Modersitzki, J. (2002) Fast Diffusion Registration. Contemporary Mathematics, 313, 117-128. |
[13] | Frohn-Schauf, C., Henn, S. and Witsch, K. (2007) Multigrid Based Total Variation Image Registration. Computing and Visualization in Science, 11, 101-113. https://doi.org/10.1007/s00791-007-0060-2 |
[14] | Zhang, J. and Chen, K. (2015) Variational Image Registration by a Total Fractional-Order Variation Model. Journal of Computational Physics, 293, 442-461. https://doi.org/10.1016/j.jcp.2015.02.021 |
[15] | Zhang, D. and Chen, K. (2020) 3D Orientation-Preserving Variational Models for Accurate Image Registration. SIAM Journal on Imaging Sciences, 13, 1653-1691. https://doi.org/10.1137/20m1320006 |
[16] | Lam, K.C. and Lui, L.M. (2014) Landmark-and Intensity-Based Registration with Large Deformations via Quasi-Conformal Maps. SIAM Journal on Imaging Sciences, 7, 2364-2392. https://doi.org/10.1137/130943406 |
[17] | Lee, Y.T., Lam, K.C. and Lui, L.M. (2015) Landmark-Matching Transformation with Large Deformation via N-Dimensional Quasi-Conformal Maps. Journal of Scientific Computing, 67, 926-954. https://doi.org/10.1007/s10915-015-0113-5 |
[18] | Huang, C., Chen, K., Huang, M., Kong, D. and Yuan, J. (2024) Topology-Preserving Image Registration with Novel Multi-Dimensional Beltrami Regularization. Applied Mathematical Modelling, 125, 539-556. https://doi.org/10.1016/j.apm.2023.09.033 |