Open Access
Volume 57, Number 1, January-February 2023
Page(s) 299 - 328
Published online 21 February 2023
  1. M.A. Viergever, J.B.A. Maintz, S. Kleinc, K. Murphy, M. Staring and J.P.W. Pluim, A survey of medical image registration-under review. Med. Image Anal. 33 (2016) 140–144. [CrossRef] [Google Scholar]
  2. J. Salvi, C. Matabosch, D. Fofi and J. Forest, A review of recent range image registration methods with accuracy evaluation. Image Vision Comput. 25 (2007) 578–596. [CrossRef] [Google Scholar]
  3. T. Makela, P. Clarysse, O. Sipila, N. Pauna, Q. Pham, T. Katila and I. Magnin, A review of cardiac image registration methods. IEEE Trans. Med. Imag. 21 (2002) 1011–1021. [CrossRef] [PubMed] [Google Scholar]
  4. N. Chumchob, K. Chen and C. Loeza, A fourth-order variational image registration model and its fast multigrid algorithm. SIAM J. Multiscale Model. Simul. 9 (2011) 89–128. [CrossRef] [Google Scholar]
  5. D. Ferreira, E. Ribeiro and C. Barcelos, A variational approach to non-rigid image registration with Bregman divergences and multiple features. Pattern Recognition 77 (2018) 237–247. [CrossRef] [Google Scholar]
  6. Z. Nie and X. Yang, Deformable image registration using functions of bounded deformation. IEEE Trans. Med. Imag. 38 (2019) 1488–1500. [CrossRef] [PubMed] [Google Scholar]
  7. G. Hermosillo, C. Hotel and O. Faugeras, Variational methods for multimodal image matching. Int. J. Comput. Vis. 50 (2002) 329–343. [CrossRef] [Google Scholar]
  8. L.C. Evans, Partial Differential Equations. American Mathematical Society, Rhode Island (2010). [Google Scholar]
  9. H. Han, A variational model with fractional-order regularization term arising in registration of diffusion tensor image. Inverse Probl. Imaging 12 (2018) 1263–1291. [CrossRef] [MathSciNet] [Google Scholar]
  10. H. Han and Z. Wang, An alternating direction implicit scheme of a fractional-order diffusion tensor image registration model. Appl. Math. Comput. 256 (2019) 105–118. [Google Scholar]
  11. H. Han and H. Zhou, A variational problem arising in registration of diffusion tensor images. Acta Math. Sci. 37 (2017) 539–554. [CrossRef] [MathSciNet] [Google Scholar]
  12. P. Dupuis, U. Grenander and M.I. Miller, Variational problems on flows of diffeomorphisms for image matching. Quart. Appl. Math. 56 (1998) 587–600. [MathSciNet] [Google Scholar]
  13. A. Budhiraja, P. Dupuis and V. Maroulas, Large deviations for stochastic flows of diffeomorphisms. Bernoulli 16 (2010) 234–257. [CrossRef] [MathSciNet] [Google Scholar]
  14. M.F. Beg, M.I. Miller, A. Trouv and L. Younes, Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61 (2005) 139–157. [CrossRef] [Google Scholar]
  15. M. Bruveris, F. Gay-Balmaz, D.D. Holm and T.S. Ratiu, The momentum map representation of images. J. Nonlinear Sci. 21 (2011) 115–150. [CrossRef] [MathSciNet] [Google Scholar]
  16. J. Li, Y. Shi, G. Tran, I. Dinov, D. Wang and A. Toga, Fast local trust region for diffusion tensor registration using exact reorientation and regularization. IEEE Trans. Med. Imag. 33 (2014) 1–43. [CrossRef] [PubMed] [Google Scholar]
  17. H. Lombaert, L. Grady, X. Pennec, N. Ayache and F. Cheriet, Spectral log-demons: diffeomorphic image registration with very large deformations. Int. J. Comput. Vis. 107 (2014) 254–271. [CrossRef] [Google Scholar]
  18. J.P. Thirion, Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2 (1998) 243–260. [CrossRef] [Google Scholar]
  19. T. Vercauteren, X. Pennecb, A. Perchanta and N. Ayacheb, Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45 (2009) 61–72. [Google Scholar]
  20. N. Chumchob, Vectorial total variation-based regularization for variational image registration. IEEE Trans. Image Process. 22 (2013) 4551–4559. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  21. V. Vishnevskiy, T. Gass, G. Szekely, C. Tanner and O. Goksel, Isotropic total variation regularization of displacements in parametric image registration. IEEE Trans. Med. Imag. 36 (2017) 385–395. [CrossRef] [PubMed] [Google Scholar]
  22. D. Zhang and K. Chen, 3D orientation-preserving variational models for accurate image registration. SIAM J. Imaging Sci. 13 (2016) 1653–1691. [Google Scholar]
  23. W. Tian, H. Zhou and W. Deng, A class of second order difference approximation for solving space fractional diffusion equations. Math. Comput. 84 (2015) 1703–1727. [CrossRef] [Google Scholar]
  24. Y. Wang, L.M. Lui, X. Gu, K.M. Hayashi, T.F. Chan, A.W. Toga, P.M. Thompson and S.T. Yau, Brain surface conformal parameterization using riemann surface structure. IEEE Trans. Med. Imag. 26 (2007) 853–865. [CrossRef] [PubMed] [Google Scholar]
  25. L.M. Lui, T.W. Wong, W. Zeng, X. Gu, P.M. Thompson, T.F. Chan and S.T. Yau, Optimization of surface registrations using beltrami holomorphic flow. J. Sci. Comput. 50 (2012) 557–585. [CrossRef] [MathSciNet] [Google Scholar]
  26. Y. Wang, L.M. Lui, T.F. Chan and P.M. Thompson, Optimization of brain conformal mapping with landmarks. Med. Image Comput. Comput. Assisted Intervention (MICCAI) 2 (2005) 675–683. [Google Scholar]
  27. P.T. Choi, K.C. Lam and L.M. Lui, FLASH: fast landmark aligned spherical harmonic parameterization for genus-0 closed brain surfaces. SIAM J. Imaging Sci. 8 (2015) 67–94. [CrossRef] [MathSciNet] [Google Scholar]
  28. L.M. Lui and C. Wen, Geometric registration of High-Genus surfaces. SIAM J. Imaging Sci. 7 (2014) 337–365. [CrossRef] [MathSciNet] [Google Scholar]
  29. K.C. Lam and L.M. Lui, Optimized quasiconformal parameterization with user-defined area distortions. Commun. Math. Sci. 15 (2017) 2027–2054. [CrossRef] [MathSciNet] [Google Scholar]
  30. L.M. Lui, S. Thiruvenkadam, Y. Wang, P.M. Thompson and T.F. Chan, Optimized conformal surface registration with shape-based landmark matching. SIAM J. Imaging Sci. 3 (2010) 52–78. [CrossRef] [MathSciNet] [Google Scholar]
  31. H. Han and Z. Wang, A diffeomorphic image registration model with fractional-order regularization and Cauchy-Riemann constraint. SIAM J. Imaging Sci. 13 (2020) 1240–1271. [CrossRef] [MathSciNet] [Google Scholar]
  32. H. Han and A. Wang, A fast multi grid algorithm for 2D diffeomorphic image registration model. J Comput. Appl. Math. 394 (2021) 113576. [CrossRef] [Google Scholar]
  33. H. Han, Z. Wang and Y. Zhang, Multiscale approach for Two-Dimensional diffeomorphic image registration. SIAM J. Multiscale Model. Simul. 19 (2021) 1538–1572. [CrossRef] [Google Scholar]
  34. F.F. Wu and C.A. Desoer, Global inverse function theorem. IEEE Trans. Circuit Theory 19 (1972) 199–201. [CrossRef] [MathSciNet] [Google Scholar]
  35. J. Zhang, K. Chen and B. Yu, A novel high-order functional based image registration model with inequality constraint. Comput. Math. Appl. 72 (2016) 2887–2899. [CrossRef] [MathSciNet] [Google Scholar]
  36. Y.T. Lee, K.C. Lam and L.M. Lui, Landmark-matching transformation with large deformation via n-dimensional quasi-conformal maps. J. Sci. Comput. 67 (2016) 926–954. [CrossRef] [MathSciNet] [Google Scholar]
  37. G.P. Paillé and P. Poulin, As-conformal-as-possible discrete volumetric mapping. Comput. Graphics 36 (2012) 427–433. [CrossRef] [Google Scholar]
  38. J. Zhang and K. Chen, Variational image registration by a total fractional-order variation model. J. Comput. Phys. 293 (2015) 442–461. [CrossRef] [MathSciNet] [Google Scholar]
  39. F. Demengel, G. Demengel and R. Erne, Functional Spaces for the Theory of Elliptic Partial Differential Equations. Springer, London (2012). [CrossRef] [Google Scholar]
  40. J.B. Conway, A Course in Functional Analysis. Springer, New York (2007). [CrossRef] [Google Scholar]
  41. C. Kanzow and Y. Shehu, Strong convergence of a double projection-type method for monotone variational inequalities in Hilbert spaces. J. Fixed Point Theory Appl. 20 (2018) 51–72. [CrossRef] [Google Scholar]
  42. T.L. Friesz, Finite Dimensional Variational Inequalities and Nash Equilibria. Springer US, New York (2010). [Google Scholar]
  43. H. Han, A tensor voting based fractional-order image denoising model and its numerical algorithm. Appl. Numer. Math. 145 (2019) 133–144. [CrossRef] [MathSciNet] [Google Scholar]
  44. B. He, X. Yuan and W. Zhang, A customized proximal point algorithm for convex minimization with linear constraints. Comput. Optim. Appl. 56 (2013) 559–572. [CrossRef] [MathSciNet] [Google Scholar]
  45. G. Gu, B. He and X. Yuan, Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach. Comput. Optim. Appl. 59 (2014) 135–161. [CrossRef] [MathSciNet] [Google Scholar]
  46. B. He and X. Yuan, Convergence analysis of primal-dual algorithms for a saddle point problem: from contraction perspective. SIAM J. Imaging Sci. 5 (2012) 119–149. [CrossRef] [MathSciNet] [Google Scholar]
  47. A. Chambolle and T. Pock, A first-order primal-dual algorithms for convex problem with applications to imaging. J. Math. Imaging Vison 40 (2011) 120–145. [CrossRef] [Google Scholar]
  48. N. Gould and P.L. Toint, Preprocessing for quadratic programming. Math. Program. 100 (2004) 95–132. [MathSciNet] [Google Scholar]
  49. BrainWeb: Simulated Brain Database, (2013). [Google Scholar]
  50. 3D-IRCADb-02, (2019). [Google Scholar]
  51. Imlab-uiip/lung-segmentation-3d, (2019). [Google Scholar]
  52. H. Lombaert, Diffeomorphic Log Demons Image Registration (2012). [Google Scholar]
  53. Medical Image Registration Toolbox, (2016). [Google Scholar]
  54. H. Han, Z. Wang and Y. Zhang, Multiscale approach for Three-Dimensional conformal image registration. SIAM J. Imaging Sci. 15 (2022) 1431–1468. [CrossRef] [MathSciNet] [Google Scholar]
  55. Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (2004) 600–612. [CrossRef] [Google Scholar]
  56. E. Agostino, F. Maes, D. Vandermeulen and P. Suetens, Viscous fluid model for multimodal non-rigid image registration using mutual information. Med. Image Anal. 7 (2003) 565–575. [CrossRef] [Google Scholar]
  57. W. Bao, Y. Cai and X. Ruan, Groud states of Bose-Einstein condensates with higher order interaction. Phys. D 386387 (2019) 38–48. [CrossRef] [Google Scholar]
  58. M. Burger, J. Modersitzki and L. Ruthotto, A hyperelastic regularization energy for image registration. SIAM J. Sci. Comput. 35 (2013) 132–148. [Google Scholar]
  59. M. Droske and M. Rumpf, A variational approach to nonrigid morphological image registration. SIAM J. Appl. Math. 64 (2004) 668–687. [CrossRef] [Google Scholar]
  60. J. Zhang, K. Chen and B. Yu, An improved discontinuity-preserving image registration model and its fast algorithm. Appl. Math. Model. 40 (2016) 10740–10759. [CrossRef] [MathSciNet] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

Recommended for you